Designing Automated Allocation Mechanisms for Service Procurement

IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, VOL. 5, NO. 1, MARCH 2013

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Designing Automated Allocation Mechanisms for Service Procurement of Imperfectly Substitutable Services Sebastian Kruse, Alexandra Brintrup, Duncan McFarlane, Tomás Sánchez López, Kenneth Owens, and William E. Krechel Abstract—Self-serving assets (SSAs) are a new interpretation of the intelligent product technology, set to transform product lifecycle management through automation. SSAs are engineering assets that autonomously monitor their health and expiry dates, search for suppliers, and negotiate with them, while they are still in use by the customer. The concept enables more timely and transparent supplier decision making while eliminating central database transactions and tedious manual effort. Autonomous self-interested agents that act on behalf of their stakeholders naturally give rise to an allocation problem, under the assumption of private information held by trade parties and capacity constrained suppliers providing imperfectly substitutable goods (ISGs). In this paper, we develop and compare three automated competition mechanisms, constructed as iterative games, and test them in the context of the aerospace service supply chain. The competition mechanisms include a prioritized selection mechanism, extended Vickrey, and reverse Dutch auctions. Our context drives us to seek mechanisms that will not only perform well in terms of economic theory, but also in terms of computational performance. Key findings are that extended Vickrey auctions can handle multiple criteria and provide higher market efficiency at lower computational cost, especially in small to medium markets. As scalability is an issue in large markets, the use of auctions is recommended only for complex high value assets or under uncertain market scenarios. As business-to-business (B2B) environments are becoming the norm for many global companies, our study aims to be exemplary to those who would like to implement automated auction mechanisms in highly complex environments. Index Terms—Dutch auctions, imperfectly substitutable goods, intelligent products, multiagent systems, service supply chain, Vickrey auctions.

I. THE SELF-SERVING ASSET VISION AND ALLOCATION PROBLEM

THE

ASSET

T

ECHNOLOGICAL developments in the last decade led to a blueprint for making physical products intelligent in operational environments. This blueprint equipped products with

Manuscript received January 09, 2012; revised May 31, 2012; accepted July 11, 2012. Date of publication October 03, 2012; date of current version March 13, 2013. S. Kruse was with the Mechanics and Ocean Engineering Department, Hamburg University of Technology, Hamburg 21073, Germany. He is now with the Foundation Brake Department, Audi AG, Munich 85057, Germany (e-mail: [email protected]). A. Brintrup is with the Saϊd Business School, University of Oxford, Oxford OX1 1HP, U.K. and also with the Manufacturing Department, Cranfield University, Cranfield MK43 0AP, U.K. (e-mail: [email protected]). D. McFarlane is with the Distributed Information & Automation Laboratory, Department of Engineering, University of Cambridge, Cambridge CB30FS, U.K. T. Sánchez López is with Innovation Works, EADS U.K., Newport NP10 8FZ, U.K. K. Owens and W. E. Krechel are with The Boeing Company, Seattle, WA 98108 USA. Digital Object Identifier 10.1109/TCIAIG.2012.2222406

artificial intelligence and created the coupling of a product and its information-based representation that 1) possesses a unique identification; 2) is capable of communicating effectively with its environment; 3) can retain or store data about itself; 4) deploys a language to display its features and requirements; and 5) is capable of participating in or making decisions relevant to its own destiny [1]. In this research, the intelligent product is regarded as an intelligent agent, characterized by autonomy and social ability, enabling the agent to communicate, and to act on changes in its environment, as well as behave proactively and in a goal-directed manner [2]. As a result, many applications flourished in the areas of automated supply chain management, logistic systems, and product lifecycle management. We have seen the promise of products that produce themselves, monitor their health, and recycle themselves at the end of their life. Large multinational corporations such as Caterpillar, Fiat, DHL, and The Boeing Company have started to build prototypes (e.g., [3]–[5]). The concept, however, is very much in the developmental stage, and there is much research needed for companies to adopt such technology and extract value from them. Most products have not fulfilled the fifth requirement of the recipe: autonomy; and left decision making to external, human, parties. In a collaborative research project with The Boeing Company, we work toward developing products that form intelligent and autonomous communities onboard the aircraft. In our case, the intelligent product is a self-serving asset (SSA), which can monitor its expiry date or health, communicate with other products to codiagnose or to place batch orders, and ask for replacement parts and schedule service by finding and negotiating with suppliers autonomously. When negotiating with suppliers, the intelligent product considers the supplier capabilities in terms of delivery time, quality of service, or reliability. Hence, in this work, the intelligent product acts as an agent, and the same holds for suppliers in the market. Thus, traders in the market are intelligent products and suppliers are autonomous, self-interested agents equipped with artificial intelligence, such that they can optimize their own behavior depending on past events. This work focuses on designing and comparing trading mechanisms, especially auctions, with the goal of finding optimal solutions in terms of social and economic welfare, from a game-theoretic point of view. In game theory, social welfare is usually referred to as overall economic welfare of the agent society. In this paper, we report on options required for facilitating efficient but computationally scalable competition in a real-life market, through the use of agent

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Fig. 1. Service procedure in the SSA vision.

technology, computational intelligence, and market mechanism design. Based on these ideas, the result of the SSA vision is an open, traceable service chain, where manual database transactions and long hours spent on the phone to negotiate with suppliers are eliminated. The search space for suppliers is larger, potentially leading to a more optimal mode of decision making [5]. The final goal of an automated process triggered and executed by SSAs is shown in Fig. 1. The SSA architecture is the result of a refactoring exercise that aims at scalability and stability, where the burden of optimal decision-making responsibility is distributed among a holarchical multiagent society. In this architecture, component agents present their requirements, component manager agents in various aircraft societies find suppliers through yellow page agents, and interact with supplier agents to procure replacement parts. The systems architecture is presented in Fig. 2. While suppliers are in competition for serving the aircraft, clients might be in competition for receiving service if there is a scarcity of replacement parts. This scenario presents us with a repeated game that is a multiunit competition challenge that could potentially host thousands of agents. Therefore, we need to seek competition mechanisms that are able to handle 1) large numbers of clients and suppliers; 2) competition for multiple units; and 3) multiple supplier selection criteria, in an automated manner in an uncertain environment with ad hoc service requests. This environment leads to private information of agents. Clients only know about their own valuation of parts depending on a belief of monetary value and part quality. Suppliers are not aware of the valuation of their clients. In order to remain practicable with thousands of parts, it is important that the competi-

tion mechanism can allocate goods efficiently, distribute market power equally, and be computationally scalable. One way to resolve this allocation problem is the use of automated auctions. Auctions have been used throughout history if goods needed to be traded without tedious bilateral negotiation and prices had to be determined quickly and at low cost [6]. Recently, agent-based automated trading systems flourished as computational agents were found to be a useful abstraction for representing self-interested goal-directed parties. Wurman et al. [7] underlined the advantage of regularity in auctioning, as auction mechanisms offer simple concepts and rules of communication and allocation. Thus, the use of auctions is a natural fit for SSA agents. In this paper, we consider the case of automated, multicriteria, multiunit SSA auctions, where suppliers compete to serve a client, and clients compete with their opponents to get served. Most current auctioning mechanisms remain unsuitable for autonomous environments in real-life contexts, as they often are designed to handle a single criterion (price), single-sided competition, and are not easily scalable. In this paper, we develop and test three allocation mechanisms to address this gap and enable automated negotiation for intelligent products. We point out the SSA requirements and review automated auction literature (Section II), introduce an adaptive learning algorithm agent used to maximize utility (Section III), design three auction mechanisms (Section IV), and compare them in terms of economic welfare and computational scalability (Section V), before identifying future directions that stem from this research (Section VI). II. BACKGROUND In the past two decades, researchers have explored the synergy between agent-based systems and auctions in two main ways. The first is auction simulation using multiagent systems, where agents are used to test theoretical results from auction literature and game theory (e.g., [8]–[10]). Yamamoto et al. [11] mention the attractiveness of this synergy due to the suitability of agent-based systems to model auction environments as distributed systems with selfish players holding private information. In addition to mere simulation, automated systems have been used to deploy actual auction markets electronically. In the remainder of this section, we examine this area further since it is most relevant with its real-world applicability. In Section II-A, we introduce the requirements that the SSA environments demand on allocation mechanisms. In Section II-B, we review recent literature with respect to these requirements. Due to the large body of literature, this review only considers applied research in both-sided auctions and work on parallel multiunit auctions. A. Requirements in SSA Environment The SSA concept poses various nontrivial requirements to the allocation of goods and services. First, the allocation has to be made with respect to many criteria. Suppliers are not only selected for their price offering, but also for their reliability, quality, and even their location or firm size.

KRUSE et al.: DESIGNING AUTOMATED ALLOCATION MECHANISMS FOR SERVICE PROCUREMENT OF IMPERFECTLY SUBSTITUTABLE SERVICES

Second, in a supply chain, sellers and buyers often face twosided competition, that is, suppliers of goods and services compete with one another for serving the customer, and buyers compete for scarce service provision. An example is the service procurement for a landing gear component that has been in use for 20 years. The asset is so complex that there are only a few suppliers available. The long lifespan of products means that service provision may encompass parts that may even be obsolete. If multiple buyers put in requests for such components, competition among buyers may occur for prioritization of service. Third, different suppliers may offer different replacement products that fit requirements with varying degrees, thus their goods are imperfectly substitutable. For example, two lifevests might differ in terms of their expiry dates and cost. These two criteria need to be included in the designed mechanism—as our focus is distributed decision making; contrary to central decision making, which would be the case if we simply used any multicriteria decision-making methodology such as the analytic hierarchy process. Fourth, allocation could be multiunit, that is, if an order can only be partially satisfied at a supplier, another supplier who could fulfil the rest of the order will be sought. Finally, an automated allocation system with a large number of buyers and sellers means that a scalable allocation mechanism is needed in terms of computational effort. This also means that the allocation mechanism cannot have a central agent that manages the trading as this would mean a bottleneck and a single point of failure. To summarize, the designed allocation mechanism needs to fulfil the following requirements: • be suitable in a B2B-environment; • be decentralized and scalable; • allow parallel trading; • deal with imperfectly substitutable goods; • deal with multiunit trading; • be designed for multiple sellers and buyers; • handle repeated trading. B. Related Literature For handling of imperfectly substitutable goods (ISGs), David et al. [12] and Vulkan and Jennings [13] proposed utility functions that determine a single ranking system for single-sided competition. In terms of our two-sided competition requirement, there are two main auction types where such an extension is possible. The first type of mechanism is the group of Vickrey–Clark–Groves (VCG) auctions, as explored in [10], [14], and [15]. Also, supply chain applications have been explored with this mechanism, including [14] and [16]. These auctions employ a central agent collecting all asks and offers. In this context, an “ask” is a bid placed by a company offering a service in exchange for a certain amount of money. Therefore, the ask is the minimum amount of money requested by the supplier. The rules are structured in a way such that all rational agents bid truthfully. The drawback of these auctions is the inherent problem of the winner determination, which is NP-hard [17]. Furthermore, VCG auctions are usually only strategy proof, because the central agent makes extra payments

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to some agents [18]. Hence, these auctions are often not feasible in a real-world scenario [19]. The second type are double-auction mechanisms, named after their handling of double-sided competition. In these auctions, buyers can submit bids while sellers can submit asks, which leads to a symmetric market situation where market power is equally distributed between all players. These mechanisms were explored in [7], [10], and [16] and applied to a supply chain context in [20]. The main drawback of double auctions is the centralized allocation that collects all asks and bids and decides on the outcome, which limits their scalability. Table I summarizes existing auction formats and their properties, as well as how they fit into the requirements of the SSA case with the requirements listed above. It appears that there is no mechanism explored in literature that fulfils all requirements to be ideally suitable for the SSA scenario. Real-life applications are rare, and those which exist do not consider two-sided competition, imperfectly substitutable goods, or scalability (e.g., [21]–[23]). All properties are realized in some mechanism, but a combination suitable for the SSA scenario has not been explored yet. Moreover, only few works [7], [10], [13], [25] have compared mechanisms in terms of efficiency or scalability in such an environment. III. ADAPTIVE AGENT STRATEGY For most multiunit auctions, there is no analytical solution of the equilibrium strategy for players [27], meaning that there is no simple dominant strategy in a game-theoretic sense for agents. A dominant strategy means that an agent maximizes its utility by following this dominant strategy independently of the strategy of all other agents, which essentially leads to the fact that it does not make sense for any rational player to deviate from its strategy [17]. As such strategies do not exist in the multiunit SSA environment, learning strategies are the only way for traders to find optimal behavior in complex multiunit trading environments [28]. To make results more realistic and comparable, a simple strategy is employed by players. We use computational agents with the adaptive agent strategy proposed by Cliff and Bruten [29], named as the zero-intelligence-plus (ZIP) strategy, developed as an improvement upon the zero-intelligence (ZI) strategy developed by Gode and Sunder [8] earlier, in order to make agents’ behavior more realistic and to mimic “minimal-intelligence agents in market-based environments” [29]. An adapted ZIP agent tries to maximize its profit by using its last trading results to determine its action in the next trading round. During the auctions presented subsequently, agents use their offers or asks and the number of traded items during the last trading rounds to maximize their profit by increasing their profit margin if they trade successfully and decreasing it when they do not complete trades in the last round. For more information regarding the agent strategy in this work, we refer the reader to [29]. Fig. 3 shows a simple representation of the computational plugins that agents use in order to optimize their bargaining behavior. An agent collects the last round of trading results in the trading plugin. This information is then passed onto the learning plugin that updates the agents’ ZIP strategy and passes the new

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Fig. 2. Overview of the system architecture.

TABLE I OVERVIEW OF EXISTING LITERATURE RELATED TO THIS RESEARCH

strategy back to the trading plugin for the next round. This behavior is continuously repeated to find the optimal strategy for the utility maximizing agents. After some time, this agent behavior results in an equilibrium in a stable auction market, as presented in Section V. This kind of equilibrium does not mean a Nash equilibrium [6] (due to the fact that not all players have a dominant strategy during the auction process and also do not have assumptions about other

players’ strategies), but an equilibrium in the sense that the outcome of auctions stays constant after a number of rounds when the ZIP agents are adapted to the market. IV. AUCTION DESCRIPTION In this section, we design three allocation mechanisms to fulfil the SSA requirements. As allocation mechanisms we choose auctions, as they are suitable for allocation problems


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TABLE II OVERVIEW OF INVESTIGATED AUCTION MECHANISMS

Fig. 3. Learning mechanism during trading process.

where efficient outcomes are desired as a result of distributed decision making by self-interested agents. Furthermore, auctions are especially useful in our case as we aim to find procurement solutions in order to allocate goods efficiently from both economic and social welfare points of view. Economic welfare would include criteria such as cost minimization and quality-of-service maximization, whereas social welfare would include actions such as prioritizing SMEs, or minimizing carbon footprint. In line with varying goods offered by suppliers, and ad hoc service requests, during which multiple sellers and buyers can compete, auctions provide us with simple rules that force intelligent agents to follow certain game-theoretic strategies. The use of auctions in such distributed decision making has been discussed by Vickrey among many others [30]. The mechanisms we designed include a prioritized selection mechanism, an extended Vickrey auction, and a reverse Dutch auction. We will briefly explain why these groups of mechanisms were chosen. Among the several characteristics of auctions. two are fundamental. The first is whether the auction is open or closed, which refers to the accessibility of bid information by all agents taking part in the auction. If the bids of other agents are accessible to all agents, then the auction is referred to as an open auction. Alternatively, if bids are kept confidential, the auction is called a closed auction. The second characteristic is the pricing strategy. In a first price auction, the highest bid will win, while in a second-price auction, the second highest bid will win, which can be favorable from a game-theoretic point of view. Hence, the reviewed mechanisms are chosen to compare the performance effects of the general auction characteristics in the intelligent product application problem. However, there are no mechanisms that directly fit to the allocation problem of SSA, as we are faced with varying supplier offerings and buyer goals, as well as computational scalability concerns (Table I). In order to test the applicability of generic auction characteristics, we have thus customized the auction mechanisms reviewed. Table II shows how the choice of the mechanism fully captures these general characteristics. The effects of the mechanisms will be discussed in Section V. Before going into more detail, we will give a brief overview of the three designed mechanisms.

The first auction is the prioritized selection mechanism that is a reverse first-price auction in sealed bid format. That means that once a buyer asks for service, all sellers submit their quotes, the buyer evaluates all quotes based on his utility function, and decides for the best one. Hence, buyers will decide for the quotes with the highest value in terms of their utility functions, which makes it a first price auction. The sellers compete for service by their bids and learn from past outcomes about the margin they can gain. This mechanism is very straightforward and reduces agent communication to a minimum. The second mechanism is the extended Vickrey auction, based on the concept developed by Vickrey, connecting ideas from auction theory with game theory [30]. The extended Vickrey auction uses a closed- or sealed-bid format which keeps all bids secret, reducing agent communication overheads. The mechanism explores a second price auction, which we extended to a multiunit case. This auction shows how the second price scheme can influence the bidding behavior with its inherent dominant gaming strategies for players. Hence, this mechanism shows how outcomes of allocations can be optimized by using results from game theory in designing allocation mechanisms. Finally, we explore an open-auction format using a reverse Dutch auction. In this auction, the buyer publishes the service it wants and a small price it is willing to pay. This information is then sent out to all sellers willing to take part. The buyers repeatedly publish higher prices until a seller is willing to give service for that price. In this format, all bids are open, because sellers know the prices that other suppliers are willing to accept. This extra information can be used to update strategies on the seller side, but it necessitates more communication and thus reduces scalability. Table II summarizes the main characteristics of the three mechanisms. Next, we present the algorithmic design of the three mechanisms. A. Prioritized Selection Mechanism The rules for this mechanism are based on a reverse multiunit first-price auction. Buyers are intelligent product agents (component manager agents) with a hardware mechanism to monitor themselves and to process environmental data. They identify suitable suppliers with the help of the system architecture introduced in Fig. 2. When the suppliers are identified, the following process is carried out. 1) The component manager agent requests the available suppliers to send quotes. The response to a request for quote (RFQ) contains a service description sent by the supplier, which should include responses to several criteria asked by the client.

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2) The suppliers decide on a quote using data on their location and further information such as schedules, and whether they can fulfil the complete request or a portion of it. The quote fixes the price of the required service. Suppliers can determine their price depending on experience from past trading rounds in order to maximize their profit. When several RFQs arrive at a supplier at the same time interval, it will give quotes as long as it has inventory. 3) The quote is sent back to the component manager agent. This agent sorts the quotes using its utility function in order to decide for the best supplier. The best supplier is the one that offers items with the highest profit for the component manager, given as (1) where is the group of all received quotes, is the value for an item , and is its price. Note that is determined by a variety of criteria due to the selection of imperfectly substitutable goods. In the context of commercial aircraft service procurement, the following criteria could be used to determine the value of a product : • reliability of supplier; • service and product quality of supplier; • delivery date for service; • location/distance. Supplier performance with respect to some criteria, such as the ones mentioned above, is dynamically updated. Based on these characteristics, a value function for the goods of different suppliers looks like

Fig. 4. First steps during all allocation mechanisms.

(2) is the value of where is the value of a product, while the th product characteristic (e.g., delivery time) and is a factor to determine the importance of the th characteristic for the overall product value . The choice of all depends on the preferences of an agent’s stakeholders and the industry in which they operate. For example, a luxury aerospace company may prefer high-quality goods, where its market differentiator is customer service experience, whereas a low-cost company might try to reduce costs by picking the supplier that can deliver quickest in order to reduce turnaround and maintenance costs. Agents decide for these preferences by having different s. The utility then depends on value and price, as introduced above. This is how the selection of ISGs is included in the auction. 4) After the best supplier is identified, the component manager agent orders parts from this supplier in case . With an order, it signals that it is willing to pay the asking price for the service. If the best supplier cannot meet the complete demand, it orders as many parts as possible from this supplier and orders the remaining parts from the next best supplier. These first steps are executed during all mechanisms and are graphically summarized in Fig. 4. Once an order arrives at a supplier agent, the process continues as follows (Fig. 5).

Fig. 5. Trading process in the prioritized selection mechanism.

5) The supplier serves the component manager agent on a first-come–first-served basis. Hence, it sends an “acceptmessage” to ordering agents as long as it can fulfil the original request (5.a). If the service is sold to other agents while the order is being finalized in step 4), it sends back a message to refuse the order (5.b.1). If it can only fulfil part of the original order, it reserves inventory, and sends back a response to component manager agents, stating that it will fulfil part of the order. Then, the component manager can order more parts from the next best supplier (5.b.2). 6) If a component manager agent receives a positive response, it responds with a preliminary contract (6.1). This message is answered by suppliers with a service contract and a trade is fixed (6.2). The above mechanism has a dominant strategy for buyers, where each selfish buyer is best off choosing the best suitable supplier which promises the maximal utility. If a supplier sends a positive response, it answers with a preliminary contract in order to fix the trade. It will always be more promising to choose the best suitable supplier in order to maximize the utility. The sellers’ situation is more complex. There is no dominant strategy. Sellers will need to choose a price for their quotes that


is attractive enough to be chosen by buyers, but high enough to gain as much profit as possible. Hence, sellers can adapt their prices strategically over time in order to maximize their revenue. There is no direct competition among buyers. Buyers cannot influence their chance to deal with the best suitable supplier, because the price they have to pay is fixed in the offer. As a single buyer cannot influence trade prices, every buyer deals with the supplier that offers the most attractive price. The crucial variable here is timing. In order to get service, a buyer needs to send its order before an attractive supplier is out of stock. Sellers compete by specifying their prices as they will set the price sufficiently low to be attractive enough to sell its stock, but high enough to gain as much profit as possible. The main advantage of using this allocation mechanism is its simplicity. From a computational scalability point of view, this mechanism is promising, as trading parties do not exchange many messages during the process of bargaining. A drawback is the lack of direct competition among buyers. Buyers do not have the possibility to express how much they value a certain supplier by their bid. Hence, suppliers do not try to give service to buyers that value their products the most, but rather to the first incoming buyers. A mechanism which tries to overcome these drawbacks is presented below. B. Extended Vickrey Auction The extended Vickrey auction is a variation of the multiunit auction. The auction is called on the seller side and has a sealed-bid format, meaning that the bid is not published and competitors do not know about the monetary value of other bids. The main motivation is to set up an auction mechanism that is only called if sellers are facing a demand that is higher than their supply. The approach is intended to reduce the number of messages for a bargaining process to a minimum when there is high supply in the market (as no auction will then be called). Furthermore, the auction is called in low supply situations, in which it is designed such that bidders have a dominant bidding strategy. The initial rules are the same as for the prioritized selection mechanism following the same steps 1)–4), as also shown in Fig. 4. After this step, the following set of events occur. 5) Once a supplier receives its first order, it sets an internal timer. It does not react to the orders until the timer expires. 6) When the timer expires, the supplier agent collects all the orders that have arrived. It then checks if more parts are ordered than it can provide, or if it has enough inventory to fulfil all orders. 7) In the latter case, it sends messages to the component manager agents stating that it can fulfil their orders (7.1). As in the prioritized selection mechanism, if a component manager agent receives a positive response, it responds with a preliminary contract (7.2). This message is answered by suppliers with a service contract (7.3) and a trade is fixed. 8) In the case that a supplier agent does not have enough stock to fulfil all orders, it calls an extended Vickrey auction. 9) Component manager agents calculate a bid and send it back to the supplier agent. 10) The supplier agent collects all bids and determines the winner or the group of winners and the number of parts

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they get. The highest bids win the auction, but the winners only pay the price of the highest losing bid. 11) After having determined the winners and the prices, the supplier informs the component manager agents about the outcome. 12) The winning component manager agent answers with a preliminary contract (12.1), and the supplier sends back a service contract in order to fix the deal (12.2). 13) If a component manager agent wins only a fraction of the parts it needs or if it does not win at all, it sends out orders to the next best supplier. This process is summarized in Fig. 6. As in the prioritized selection scenario, buyers have a dominant strategy at the beginning of the process, as they choose the best suitable supplier depending on their expected trade utility. Once an auction is called, buyers have a dominant bidding strategy as well. Depending on the market, there are two dominant strategies. The first strategic situation is a market with a single seller, where it is the dominant strategy for a component manager agent to bid with its true valuation . If an agent reports its true valuation, its paying price will be its bid or less than its bid. Hence, the mechanism is individual rational, and a selfish agent has the incentive to take part in the auction because the utility for the trade of item is . If the price is determined by the first losing bid , an agent with value that bids truthfully and wins will gain a utility of (3) If a component manager agent submits a bid higher than its true valuation such that , it would still gain the same utility as specified in (3). But if the highest losing bid exceeds agent ’s value such that the agent would gain a negative utility (4) Hence, a component manager agent has no incentive to bid higher than its true valuation. The situation is very similar for an agent that sends a bid lower than its true valuation , such that . In that case, it does not gain any higher utility than the utility from (3). But if the bid that would be the highest losing bid in a truth telling scenario is higher than the bid , agent looses the auction, and the bidder with the bid wins the item. In this case, agent does not gain any utility, even though it would have done by bidding its true valuation . Hence, the mechanism is incentive compatible in a market situation where only one seller is available. The second strategic situation a buyer may face is more complex. It occurs when an agent takes part in one supplier’s auction, but can also get service from a different supplier. There is still a dominant strategy, but it is not truthful bidding any more. A buyer agent orders its parts at Seller1, who calls an auction because it faces high demand. Agent expects a utility of when choosing Seller1 with its specified price . Nevertheless, it could also have ordered at Seller2 with an expected utility of (5)

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Fig. 6. Trading process in extended Vickrey auction.

Fig. 7. Trading process in reverse dutch auction.

When agent bids in Seller1’s auction, it has to consider that it could still order at Seller2. Hence, a rational agent will not bid truthfully anymore, but will submit a bid that makes its expected utility from a trade with Seller1 at least as high as the utility in the alternative trade with Seller2, which means (6) and leads to a minimum utility

, that is (7)

in the case of being a winner in the auction of Seller1. If buyer agent loses the auction, it would prefer trading with Seller2. Hence, the dominant strategy is to submit a bid , which is determined by subtracting the utility from a trade with the next best supplier from the valuation of the best supplier’s product , as specified in (6). As in the prioritized selection mechanism, there is no dominant strategy on the seller side. Sellers need to submit quotes that are attractive enough to sell their parts or to be able to call an auction in order to make buyers compete for parts. Buyers compete with each other when attractive suppliers face more demand than they have inventory or capacity. This auction format encourages buyers to reveal their preferences in order to get the service they prefer. Sellers compete by setting the prices in their quotes. The most obvious advantage, compared to the prioritized selection mechanism, is competition on the buyer side. Market efficiency should increase by making buyers compete for the best suppliers. The mechanism leads to situations where buyers get service from an attractive supplier that values the supplier’s products the most, or that has the least attractive alternatives. This increases the market surplus and the overall efficiency of the auction mechanism. Moreover, in case of a

changing market situation where demand rises or supply falls, the enhanced competition should lead to an adoption of prices. Drawbacks are an increasing number of messages exchanged due to the auction process under competition. Nevertheless, the number of messages is still very low, because the auction employs a sealed-bid format, and auctions are only called if there is competition among buyers. A problem reducing market efficiency is that auctions might happen in parallel. This means that alternative suppliers might not be available anymore for the losers of auctions. A third mechanism that is called on the seller side of the market and employs an open format is the one introduced in the following section. C. Reverse Dutch Auction Reverse auctions are mechanisms regularly used as B2B e-procurement auctions [31]. In these auctions, buyers advertise the service they are looking for and sellers place bids to provide the service. A lower bid is more attractive in this format. We develop a modified version of the reverse Dutch auction. The auction is called on the buyer side and an open-auction format is used, where buyers increase prices until a seller is willing to give service at an announced price. In this open format, more information is revealed for auction participants than in closed formats. For example, sellers can determine when competing sellers are accepting bids. An open-auction format is also interesting in terms of gaming behavior. In a regular reverse auction, the bidder with the lowest asked price will serve the buyer who called the auction. For the SSA, this needs to be reconfigured as buyers’ valuations do not only depend on the price but also on multiple criteria. Hence, the price needs to be increased individually for each seller in order to equal out the expected utility for each possible trade, instead of price equivalence. The auction mechanism is adapted to these constraints as follows (Fig. 7).


As with the other two mechanisms, the reverse Dutch auction starts out with the first three steps, as introduced in Fig. 4. 4) Once the quotes are made, buyers collect them and accept a quote if a price is attractive or they need a part urgently as in steps 4), 5), and 6) of the prioritized selection mechanism, as introduced in Section IV-A. 5) Otherwise, they start a reverse Dutch auction. First, buyers evaluate each seller with respect only to its nonprice attributes, such as reliability and distance costs (5.1). Hence, a component manager agent calculates the value for the service of each seller . Then, it can choose a maximum utility that it will try to gain, which determines the start prices for the reverse auction. For each seller , the component manager agent calculates a different start price that it is willing to pay. This price is calculated as (8)

6)

7)

8)

Hence, if any supplier accepts the offered price , the utility for the component manager agent is the same at . After having determined all start prices, the component manager agent sends bids to all suppliers that sent a quote (5.2). Suppliers decide if they are willing to accept the price (6.a) or if it is not high enough (6.b). They respond to the component manager agent accordingly. If a supplier is willing to accept the price, the component manager agent will send a preliminary contract (7.1) and the supplier agent will answer with a service contract in order to fix the deal (7.2). If no supplier accepts the bid, the component manager agent calculates new prices. It raises each bid by a small amount . This leads to a new price (9)

The component manager sends bids with the updated prices to all suppliers. 9) If again all suppliers decline the new bids, the component manager agent raises the prices once more, going back to step 8). This is repeated until it gets service or no suppliers are available, because they sold their stock to other agents. The latter might happen when competing component manager agents send bids in parallel. 10) If a supplier cannot fulfil the request anymore, it will send a notice to the component manager agent (10.a). In case more bids are accepted than the component manager agent needs parts, it can decline an accepted bid (10.b). 11) The process ends when a component manager agent acquires all parts it requires (10.b or 11), when no suppliers with inventory are left, or when the component manager agent has to raise its bids to a level such that its expected utility becomes (11). This auction mechanism has a dominant strategy for buyers. They start raising their prices at a low level, where their expected utility is high. Then, they keep increasing the bids by reducing their expected utility. They do this until a supplier accepts the offer and will give service. By sending parallel offers

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with the same utility, buyers are indifferent to which supplier accepts an offer. Suppliers with a lower expected reliability or with higher distance costs will accordingly need to provide service at a lower price. Decisions a buyer needs to make are the price increment and the starting price. For the starting price, it is optimal to choose a price that is just below the expected accept price in order to keep the duration of the trading mechanism low. From a strategic point of view, it would be optimal to choose a small increment in order to make buyers accept prices as low as possible. But this would lead to a high number of messages exchanged and an increased computational effort. Sellers have no dominant strategy. They need to decide at which level they accept offers. A seller needs to choose a level that is sufficiently high to gain profit with each trade, but low enough such that not too many other sellers are more attractive. Agents make this choice by the use of the adaptive strategy introduced earlier. Sellers need to accept prices that are lower than their competitors, when they want to trade with component manager agents. Hence, the competition is very similar to the prioritized selection mechanism. On the buyer side, there is no direct competition in a single auction, because buyers call the auction. The only way buyers can compete is to raise their reservation prices. Thus, they signal to sellers that they are willing to trade at a higher price, at which sellers could gain more profit than by trading with other buyers. A practical advantage is that a buyer can choose if it wants sellers to compete in a reverse auction to give service or if it wants to accept one of the orders immediately. That can be important if a buyer has high urgency for a part and does not want to call an auction that might lead to lower prices, but also runs the risk that suppliers prefer trading with other buyers and are running out of stock. Furthermore, an advantage of the open-auction format is that buyers and sellers can collect more information about each other in order to optimize their strategies. Due to the open ascending mechanism, sellers know, after an auction, at what price a buyer stopped raising offers, because some other supplier gave service at that price. Then, a supplier can calculate the profit it would have made at that price and decide if it wants to underbid this price, in case the same buyer asks for service again. A possible drawback is that a single auction itself is not strategy proof. Designing a reverse auction that is strategy proof when goods are imperfectly substitutable is challenging. As Milgrom [32] points out, an efficient single-sided reverse auction that is strategy proof and truth telling for multiattribute items is a reverse Vickrey auction where auctioneers need to publish their utility function and their beliefs about each seller’s quality. This is necessary to achieve incentive compatibility and make sellers agree on fixed payments. However, this does not seem to be a realistic scenario in the competitive environment of the SSA scenario, especially applied to a highly competitive market such as aerospace, where buyers would not be willing to publish their utility functions and their internal beliefs about sellers’ quality. Furthermore, the advantage of the available information means additional processing power from a computational point of view. If the extra information of the open format is not adopted, this auction is very similar to the prioritized selection mechanism and is often outcome

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TABLE III SELF-SERVING ASSET AUCTIONS

equivalent. Finally, the open-auction format results in a high number of messages reducing the scalability of the system [33]. D. Design Comparison Table III shows the main characteristics of the developed auction mechanisms in terms of format, who triggers and calls an auction and where the competition is enhanced. All developed mechanisms are suitable for a large number of sellers and buyers and are used in a selection environment where buyers have multiple decision criteria and more than one item can be traded per auction round. Auctions can take place in parallel in a B2B-exchange environment. V. EVALUATION A. SSA Test Bed Each product in the SSA society is represented by a dedicated computational agent, which monitors the component and enables it to decide autonomously. The decision to order may be triggered by expiry dates, health, or flight hours. Components sense their environment through the use of wireless sensor nodes. Each component agent has a component manager agent which calls for batch orders, and negotiates with the suppliers it finds through a yellow pages agent. Suppliers are represented by dedicated computational agents, and use the Ontology Web Language to describe their services. The software is a prototype that provides a test bed to develop strategies and algorithms for the SSAs. Our experiments are conducted in this prototype environment. The system is coded in the open-source multiagent development software Cougaar, Inc. [34]. In [35], we detail the reasons behind our selection of Cougaar for the prototype. In this environment, messages between agents are exchanged using a Java-based Message Transport Service. Agents have plugins, which determine their functionality, and execute when messages are received or internal timers expire. Since agents can set timers for themselves and plan actions, they are proactive. B. Explored Scenarios In this work, we examine four market scenarios, chosen due to their occurrence for different types of assets in the aerospace service supply chain. 1) Small Stable Market: During this set of experiments, small and stable markets are explored. This occurs when goods new to a market are traded, and demand stays on a constant level. A small market can also occur when parts are complex, expensive, or have high distance costs. In this scenario, suppliers farther away from a maintenance site are not considered. There are six component manager agents with a choice of three

different suppliers. Stability of market is simulated by keeping the demand of single agents constant for each round. In order to minimize the random effects or the effects of starting values, a number of experiments are executed. Ten experiments are executed for each mechanism, such that standard deviations become sufficiently small to analyze significant differences. Demand in the market is higher than supply to make the occurrence of auctions more likely. Each experiment lasts 20 rounds to give agents the opportunity to adapt to the market situation. 2) Small Stochastic Market: For this series of experiments, component manager agents have a stochastically varying demand and part values are changed due to varying urgency of needs in different rounds. Markets are still small. This situation can occur in the aerospace service industry for new goods that are traded for the first time. For example, components fail mainly for health reasons and do not have to be maintained on a prescheduled basis. This leads to varying demand, especially when the number of traded items is small. The second factor contributing to more dynamic markets is the change of part values, for example, for critical parts that have a direct influence on turnaround time as the aircraft cannot take off until service is complete. This can lead to high urgency, when a part needs to be replaced before the next takeoff. The experiments are set up with six buyers choosing among three suppliers. Values, costs, and locations are drawn from a discrete uniform distribution. The dynamic environment is simulated by drawing the number of ordered items before each round from a random distribution and changing the component manager agents’ part values. Experiments last 20 trading rounds each and are repeated ten times. Afterwards, the average values of measures over the simulations are calculated and standard deviations are determined in order to detect the consistency of results. In these experiments, we do not use the reverse Dutch auction anymore. This is because the prioritized selection mechanism is based on a first-price sealed-bid auction that is strategically equivalent to a Dutch auction, as shown by [6]. Our experiments confirm this because they show that the reverse Dutch auction is outcome equivalent to the prioritized selection mechanism, especially because the ZIP agents do not employ the extra information of the open format of the reverse Dutch auction, as pointed out earlier. 3) Large Market: Experiments in larger markets explore the behavior of mechanisms when many suppliers are available and more buyers are involved in the bargaining process. Situations like these can occur when suppliers are searched on a global, rather than local, scale. This can be the case when items have low transportation costs, and thus, suppliers which are farther away from a maintenance location can be considered. A further scenario is the trading of parts that do not need to be replaced immediately. Hence, an aircraft manager can decide to replace


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TABLE IV MARKET SCENARIOS WITH SIMULATION CONDITIONS

items at different locations using the flight schedule of the aircraft, meaning that a larger number of potential suppliers can be taken into account. No differentiation between dynamic and stable markets is made, because outcomes show no significant difference for large markets. Large markets are simulated with ten suppliers and 12 component manager agents. 4) Monopolist Market: In certain situations, sellers might have a monopoly on certain products or services. This can be the case when products are rarely traded and there is a small market with high entry barriers for other suppliers. Other reasons can include intellectual property ownership, or limited economies of scale that no other suppliers can offer. Monopolist markets are simulated with ten component manager agents seeking service from a single supplier that faces a demand higher than its capacity. An overview of the experiments conducted for the different market scenarios with the number of experiments and closing conditions is given in Table IV. For statistical significance, it is important to mention that, for the comparison of each mechanism, we executed 200 rounds of trading (except for monopolist markets where we executed 100 rounds due to stability of outcome). Due to parallel auctions called on the side of buyers or sellers depending on the auction format, each round includes several auctions. This means that we end up with about a minimum of 1000 auctions executed for each mechanism in each market scenario, although the exact number depends on demand and supply that is generated stochastically in the multiagent environment. To make outcomes comparable, the costs and part values, the supplier locations, and their reliability values are kept constant. C. Performance Measures We use the following performance measures for auction evaluation. 1) Allocative Efficiency: Efficiency is often a major goal for the auction designer (see, e.g., [6] and [18]). In this work, efficiency signifies that the goods and services are provided in a way that airlines and suppliers can trade to obtain maximal profit. In a single-item case, allocative efficiency means that a seller sells goods to the buyer who values it the most [6]. For settings with numerous buyers and sellers, efficiency is more

complex and refers to the maximum sum of utilities obtained by all players [18]. Hence, an allocation outcome , that is one of all possible outcomes , is efficient when (10) where agents take part in an allocation mechanism and each agent gains a profit of . Allocative efficiency measures the maximal total welfare in the market, which is why researchers often refer to it as the efficient or optimal market solution [17]. 2) Market Power: In a truly competitive market, neither buyers nor sellers set the prices. The price will be set by demand and supply. Nevertheless, certain allocation rules can bias sales to a trading party. In our application, it is important that a trading mechanism neither solely benefits suppliers nor airlines. If one party would gain most of the profit, the other would not be willing to accept the market mechanism. Hence, the designed mechanisms should aim to divide market power equally (see, e.g., [36]). Market power measures how strongly a market mechanism tends to benefit buyers or sellers. Hence, market power is an important measure for ensuring fairness and symmetry in a market. In a classical market model, buyer market power measures the actual surplus made by buyers and compares that to the profit buyers would have made when trades happen at the equilibrium price [37]. The difficulty is that in the SSA scenario there is no single equilibrium price due to market constraints. When buyers value sellers differently, there is no such thing as an equilibrium price, as buyers are willing to pay different amounts of money to different sellers, due to their ISGs. The simplest way to adapt the measure is to compare the buyers’ surplus to the sellers’ surplus. In this case, the outcome depends on the distribution of values and costs, and supply and demand. For the same distribution, different market mechanisms can be compared in terms of surplus shares. If sellers’ valuations and buyers’ costs are distributed similarly, the measure is a good indicator. 3) Computational Performance: In automated systems, fault tolerance and scalability are the main measures for system evaluation [33]. For the SSA scenario, the computational performance of the auction mechanism is a key factor as the system might deal with hundreds or thousands of agents. This means

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Fig. 8 Average surplus shares in small stable markets over ten rounds of simulations including standard deviations. (a) Allocative efficiency. (b) Buyer surplus. (c) Seller surplus.

that only mechanisms that reduce the agent’s footprint to a minimum are feasible so as to prevent communication from being so lengthy that optimal solutions are found too late to be executed. The scalability of multiagent platforms is determined with respect to two different variables: the number of agents the system is able to handle and the number of exchanged messages [33]. In our case, the choice of a certain auction or mechanism format does not influence the number of agents. We thus examine number of messages and resolution time as measures of computational performance [38]. D. Results on Allocative Efficiency and Market Power Allocative efficiency and the distribution of market power are analyzed to determine economic performance. We chose the number of experiments and the number of trading rounds in the experiments such that statements regarding statistical significance can be made with respect to the student’s t-test [39]. Hence, we support our results with the t-values for the standard risk or alpha level of 0.05. Critical t-values are presented to interpret the significance of the simulation results. In this work, the t-values regarding the null hypothesis are used. This means that, depending on the distribution of the simulation results, the t-test determines if the difference in the mean values is significant at the chosen risk level of 0.05. This is done by calculating the critical t-values that would mean a significant difference taking the observed distributions and the risk level of 0.05 and comparing these values to the actual ones from our distributions. Hence, the t-values are able to state if differences in the simulation results are statistically significant. 1) Small Stable Market: Fig. 8(a) shows the average relative efficiency over the trading rounds in a small stable market. The extended Vickrey auction has a higher efficiency at the beginning of the simulation. The prioritized selection mechanism and the reverse Dutch auction start out in a similar way. The development of the market shares is analyzed. Fig. 8(b) and (c) shows buyer and seller surplus. Initially surplus shares are different. Sellers gain most of the surplus for the extended Vickrey auction, while buyers gain most of the surplus in the prioritized selection mechanism and the reverse Dutch auction. Over time, the surplus shares converge. Nevertheless, sellers still gain a bigger part of the surplus in the extended Vickrey auction than

they do in the prioritized selection mechanism and in the reverse Dutch auction after the results have stabilized. The simulations show that the extended Vickrey auction adapts faster to initial conditions under high demand. Under high uncertainty, the extended Vickrey auction allocates goods most efficiently. This uncertainty is inherent during the beginning of trading because agents have not collected enough data about the market yet. However, over time, the agents can adapt to past outcomes of the allocation mechanisms. This results in convergence of all mechanisms to similar relative efficiency. It becomes obvious that the reverse Dutch auction and the prioritized selection mechanism are outcome equivalent due to their strategical equivalence for agents. The main advantage the reverse Dutch auction offers is the revelation of more information due to the open-auction format. However, in order to use this information, agents would need to have more advanced strategies, which were not implemented during the course of this study. Fig. 8(b) and (c) shows that the surplus shares develop over time as well. This development is based on the adaptive agent strategies. Since there is high demand in this series of experiments, a bigger part of the surplus is gained by suppliers. The experiments show that the extended Vickrey auction finds suitable surplus shares faster. This is especially the case under high demand because buyers compete in auctions where they have a dominant strategy that does not change between rounds. Hence, adaptive component manager agents do not bid differently, once they compete in an auction. Furthermore, results show that the final level of seller surplus in an extended Vickrey auction is still higher than for the other mechanisms. Even in a stable situation, sellers have a higher market power in a Vickrey auction than they have with mechanisms called on buyer side. The dominant strategy for buyers leads to situations where sellers can extract as much profit from the market as possible. It should be mentioned that, in the case of high supply, all auction forms show very similar results. This is based on the fact that auctions are only called when single sellers are facing high demand during the extended Vickrey auction. Thus, the extended Vickrey auction and the other mechanisms are outcome equivalent, when each seller has an inventory high enough to serve all incoming orders. Hence, these results are not presented.


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TABLE V T-VALUES FROM STUDENT’S T-TEST FOR FIRST ROUND OF TRADING IN SMALL STABLE MARKETS WITH A CRITICAL T-VALUE FROM 1,76

TABLE VI T-VALUES FROM STUDENT’S T-TEST FOR 20. ROUND OF TRADING IN SMALL STABLE MARKETS WITH A CRITICAL T-VALUE FROM 1,76

Fig. 9. Average surplus shares in small stochastic markets over ten rounds of simulations including standard deviations. (a) Allocative efficiency. (b) Buyer surplus. (c) Seller surplus.

Tables V and VI support these statements. The t-values show that the prioritized selection mechanism and the reverse Dutch auction show no significant difference in the first and the last trading round, while the extended Vickrey auction is superior in the first round but shows no significant difference once agents have adapted to the market situation in the last round. In summary, the extended Vickrey auction can adapt faster to initial conditions, especially under high demand. This results in a higher relative efficiency at the beginning of the executed experiments. But, when the goods are traded regularly for a longer period, mechanisms converge to similar results in terms of efficiency and outcomes become equivalent. Hence, auctions only lead to a higher overall surplus at the beginning of a series of trades, when the trading market for goods is stable. 2) Small Stochastic Market: Fig. 9(a) shows the simulation results for relative efficiency in the small and dynamic market setup. The relative efficiency, using the extended Vickrey auction, is higher than the prioritized selection mechanism. Furthermore, auctions are not outcome equivalent over an increasing number of rounds. Experimentation shows that the mean relative efficiency of the extended Vickrey auction, with 78.4%, is nearly 10% higher than the average relative efficiency of the prioritized selection with 69.3%. The t-test supports these statements with the t-value of 4.86 showing a superior extended Vickrey auction at a risk level of 5%. Fig. 9(b) and (c) shows the shares of surplus. Surplus that is gained among all buyers is higher for the prioritized selec-

Fig. 10. Average allocative efficiency in large markets over ten rounds of simulations including standard deviations.

tion mechanism, while sellers gain a major part of the surplus using the extended Vickrey auction. For small and dynamic markets, the simple adaptive strategy is not sufficient to make the outcome of a prioritized selection scheme equivalent to the extended Vickrey auction, even though relative efficiency seems to increase over time. Hence, in situations with stronger dynamics, extended Vickrey can also be a superior mechanism in the long term and not only at the beginning of the trading period, in the case of agents using strategies utilized during this study.

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Fig. 11. Average surplus shares in monopolist markets over ten rounds of simulations including standard deviations. (a) Allocative efficiency. (b) Buyer surplus. (c) Seller surplus.

TABLE VII T-VALUES FROM STUDENT’S T-TEST IN MONOPOLIST MARKETS WITH A CRITICAL T-VALUE FROM 1,73

The development of the surplus shares shows that the extended Vickrey auction gives higher market power to the sellers and leads to higher efficiency if demand is dynamic. The reason is the inherent uncertainty about demand and supply in markets with more dynamic characteristics. Part of this uncertainty is resolved when competing agents can use dominant strategies. 3) Large Market: Fig. 10 shows the average relative efficiency of different mechanisms in the large market setup. The difference in relative efficiency between the prioritized selection mechanism and the extended Vickrey auction is small: the average allocative efficiency for the prioritized selection mechanism overall is 90.2%, while it is 89.9% for the extended Vickrey auction. The t-test gives a t-value of 0.47 for the slightly better prioritized selection mechanism, but the critical t-value is 1.64. Hence, the auction outcomes show no significant difference at the chosen confidence level. Compared to the market situations explored previously, the relative efficiency is higher on average. For big exchange markets, the effect of increasing efficiency by using auctions vanishes. The reason is that the difference in trade utility between suppliers ranked next to each other for a certain buyer becomes smaller. Hence, if a buyer cannot trade with the best suitable supplier, but only with the second best, the difference in profit is smaller than in small markets. Furthermore, it is more likely that auctions are happening in parallel. Hence, it is more often the case that losers in an auction cannot purchase goods at their second best supplier anymore, because the supplier already sold its inventory, which is a drawback for parallel extended Vickrey auctions. The combination of these two reasons leads to nearly equivalent auction outcomes. The high overall relative efficiency is caused by the same effect. The difference between an optimal allocation and one that is slightly suboptimal becomes smaller with a bigger market that has low variation in costs and values. Hence, it can be stated that

the outcomes of the developed allocation mechanisms become more equal in situations with bigger markets. The plots for the development of the surplus are not presented, because they show the same effects as obtained for small stochastic markets. The prioritized selection mechanism disadvantages the sellers, while the extended Vickrey auctions sets them at an advantage on average. This is again due to the fact that buyers compete in the seller’s auctions during extended Vickrey auctions, which forces them into a strong competition and favors the seller side. 4) Monopolist Market: Fig. 11(a) shows the average relative efficiency over each experimental round. It can be observed that the extended Vickrey auction allocates goods nearly fully efficient. Reverse Dutch auction and prioritized selection have lower efficiency values of around 85%. The t-values presented in Table VII show a clearly superior extended Vickrey auction, while the prioritized selection mechanism and the reverse Dutch auction show no significant difference at the chosen risk level. Fig. 11(b) and (c) shows the shares of buyers and sellers over the rounds of experiments. The seller and the buyer surplus with the extended Vickrey auction stays approximately constant, whereas the surplus share of the two other mechanisms moves toward the value of the extended Vickrey auction over an increasing number of auction rounds. These experiments highlight how powerful the effect of different auction mechanisms can be for single-seller scenarios. The extended Vickrey auction allocates goods in a nearly fully efficient manner, because it is originally designed for an efficient allocation in a single-seller scenario. All agents are bidding truthful because there is no other supplier available to purchase their goods. The truthful bidding leads to a trading price where supply equals demand and the allocation is efficient. Furthermore, there is no loss of efficiency due to parallel auctions. It should be mentioned that the extended Vickrey auction does


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Fig. 12. Number of exchanged messages with different numbers of trading agents; error bars indicate standard deviations. (a) Total number of messages. (b) Messages per agent.

not allocate goods always fully efficient because it is assumed that a supplier is not aware of its monopolist power. Hence, a supplier follows an adaptive strategy. This can lead to inefficiencies if a supplier raises its prices to a level such that it cannot call an auction anymore, because buyers lose interest. The reverse Dutch auction and the prioritized selection mechanism appear to be outcome equivalent again. However, they are lacking the strength of the extended Vickrey auction that provides truthful bidding as the dominant strategy for all agents. Hence, buyer agents need to adapt to past results during these mechanisms in order to maximize their profit, which in turn increases computational cost. The surplus shares gained by buyers and suppliers using the prioritized selection mechanism and the reverse Dutch auction converge to the surplus shares of the extended Vickrey auction. The reason is that the extended Vickrey auction equals supply and demand by finding the price where exactly as many buyers are interested as needed to sell the complete inventory. In market mechanisms where truth telling is not the dominant strategy and where demand and supply are stable, adaptive agents will converge to the same price. This occurs because a supplier raises its price as long as it sells all its inventory in each round. Summarizing, it can be stated that the extended Vickrey auction is powerful when only one supplier is available to sell a certain product or service, as market prices are found quickly and the relative efficiency is high. E. Results on Computational Scalability In addition to economic performance, in what follows, we explore the mechanisms from a computational point of view. 1) Number of Exchanged Messages: To detect the number of exchanged messages, each allocation mechanism is simulated ten times under different initial conditions but with a constant number of agents. We measure only competition related messages. Afterwards, the number of agents is increased and a new set of experiments is executed. Fig. 12(a) shows the total number of added messages during a specific mechanism, while Fig. 12(b) provides a measure for the average number of messages per agent. The reverse Dutch auction uses the highest number of messages, followed by the prioritized selection mechanism and the extended Vickrey auction. The extended

Fig. 13. Resolution time of different mechanisms over an increasing number of agents; error bars indicate standard deviations.

Vickrey auction and the prioritized selection are very close to each other, even though agents exchange slightly more messages during the extended Vickrey auction. The extended Vickrey auction performs slightly worse than the prioritized selection mechanism, as the auction causes extra communication when sellers face high demand. However, the degree of necessary communication in a sealed-bid auction format is very low compared to the open-auction mechanism. Hence, the revelation of market information in an open auction comes with a high burden in terms of scalability, as the number of exchanged messages increases rapidly when the number of agents in the system grows. 2) Resolution Time: Fig. 13 shows the time until all goods in the market are allocated using a particular mechanism. As expected, resolution time increases with a growing number of agents for all three decentralized mechanisms. The prioritized selection mechanism performs best due to its simple rules and low requirements in communication. The extended Vickrey auction performs only slightly worse. In general, the rise in resolution time is linear and not exponential for the sealed bid auction mechanisms, which makes the system scalable. Moreover, the resolution time can be reduced when a component manager agent only considers a group of suppliers and not all suppliers

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Fig. 14. Reduction of resolution time during reverse Dutch auction when not all available suppliers are contacted; error bars indicate standard deviations.

that are available. Fig. 14 shows the resolution time for a reverse Dutch auction where three buyers can choose between ten suppliers but do not necessarily contact all. In this case, resolution time is reduced by more than 20%. However, this can lead to inefficiencies in case the subgroup of contacted suppliers does not have enough inventory to serve the buyer. VI. CONCLUSION We designed and tested three auction mechanisms for automated service procurement in the aerospace service supply chain, characterized by unpredictable market scenarios, imperfectly substitutable goods, multiple decision criteria, multiple sellers, and potentially hundreds or thousands of buyers. These were evaluated in a range of scenarios likely to face the commercial aerospace service industry, which included small dynamic and stable markets, as well as large and monopolist markets. Auction mechanisms are evaluated in terms of allocative efficiency, power distribution in the market, and computational performance. These performance measures reflect the challenges that face the auction design that should support automated trading in B2B environments. Our results show that it is possible to design auction mechanisms that fulfil the requirements of the SSA society. Auctions function in a fully automated manner and the agents use their private information to decide which price they are willing to bid or supply. Dynamically changing criteria such as reliability, and static criteria such as location, may be added in the decision process, using a utility function that determines the value of a service with respect to its price. Although, basic requirements were fulfilled, it was shown that different auction types perform very differently in the allocation of goods. Furthermore, the efficiency of allocation, distribution of market power, and scalability highly depend on given market scenarios and the characteristics of the product being traded. No mechanism could be identified that is superior for all simulated market conditions when considering the tradeoff between efficiency and scalability.

• In small markets with stable demand, the extended Vickrey auction adapts faster than others under initial uncertainty, providing higher market efficiency and seller surplus. Prioritized selection results in a buyer-dominant market. However, when repeated enough times, auctions have equivalent outcomes. • In a small but stochastic market, the extended Vickrey auction has the highest market efficiency and auction results do not converge. Extended Vickrey auction is seller dominant, while prioritized selection is buyer dominant. • In a large market with stable demand, the difference between extended Vickrey auction and prioritized selection is small in terms of market efficiency. Efficiency is higher in the larger market and the use of auctions is less beneficial as allocation mechanisms become outcome equivalent, but auctioning leads to higher computational effort. • In a monopolist single-seller scenario, the extended Vickrey auction provides the highest efficiency and consistently gives high market power to the seller. Reverse Dutch auction and prioritized selection are less efficient, but provide a fairer market. • Reverse Dutch auction with its open format is the worst in terms of computational performance, while prioritized selection performs best. The extended Vickrey auction performs well, because it utilizes a closed-auction format. • We recommend the use of the extended Vickrey auction in small and medium markets. The use of extended Vickrey auctions gives promising results as it enhances relative efficiency for certain market situations and only slightly increases the number of sent messages and the resolution time. However, the benefits in efficiency come with a higher market power for suppliers. If markets are more stable, the prioritized selection should be used due to its outcome equivalence but higher scalability. An open-auction format should only be used if agents need the information provided during the auction to determine their values. An open format comes with high cost in terms of computational effort. • We do not recommend the use of auctions in large markets and note that auctions will result in lower computational performance in agent-based allocation, compared to simpler, one-off allocation mechanisms. Auctions will be useful for complex, high value assets, or when goods are traded the first time with high uncertainty in the market. Our work results in interesting future research avenues for exploration. For instance, we have not experimented with different agents following different adaptive strategies. It will be a reasonable next step to explore which strategies agents follow if they are equipped with machine learning algorithms such as genetic algorithms or reinforcement learning. However, one needs to keep in mind that more advanced strategies will lead to bigger agents’ footprints, which means a tradeoff between intelligence and scalability. Our results on scalability are statistically significant, however it will be necessary to execute these experiments with several hundreds or thousands of agents in order to get more accurate statements on the number of exchanged messages and auction duration. Furthermore, the industrial environment within which


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[24] C. Preist and M. van Tol, “Adaptive agents in a persistent shout double auction,” in Proc. 1st Int. Conf. Inf. Comput. Econ., 1998, pp. 11–18. [25] M. P. Wellmann, W. E. Walsh, P. R. Wurman, and J. K. MacKieMason, “Auction protocols for decentralized scheduling,” Games Econ. Behav., vol. 35, pp. 271–303, 2001. [26] L. Ta, Y. Chai, and Y. Liu, “A multi-agent approach for task allocation and execution for supply chain management,” in Proc. IEEE Netw. Sens. Control, 2005, pp. 65–70. [27] A. Hailu and S. Thoyer, “Multi-unit auction format design,” J. Econ. Interact. Coordinat., vol. 1, pp. 129–146, 2006. [28] S. Markose, “Computability and evolutionary complexity: Markets as complex adaptive systems,” Econ. J., vol. 115, no. 504, pp. 159–192, 2005. [29] D. Cliff and J. Bruten, “Minimal-intelligence agents for bargaining behaviors in market-based environments,” Hewlett-Packard, Bristol, U.K., Tech. Rep. HP-97-91, 1997. [30] W. Vickrey, “Counterspeculation, auctions, and competitive sealed tenders,” J. Finance, vol. 16, no. 1, pp. 8–37, 1961. [31] P. F. Johnson and R. D. Klassen, “E-Procurement,” in MIT Sloan Management Review. Cambridge, MA, USA: MIT Press, 2005. [32] P. Milgrom, “An economist’s vision of the B-to-B marketplace,” White Paper, 2000 [Online]. Available: Perfect.com [33] F. Bellifemine, A. Poggi, and G. Rimassa, “Intelligent agents 7: Agent theories architectures and languages,” in Developing Multi-Agent Systems With JADE. Berlin, Germany: Springer-Verlag, 2001, pp. 42–47. [34] Cougaar project [Online]. Available: http://cougaar.org/ [35] T. Sánchez López, A. Brintrup, D. McFarlane, and D. Dwyer, “Selecting a multi-agent system development tool for industrial applications: A case study of self-serving aircraft assets,” in Proc. IEEE Int. Conf. Digit. Ecosyst. Technol., Dubai, Saudi Arabia, April 12–15, 2010, pp. 400–405. [36] Y. Shoham and K. Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge, U.K.: Cambridge Univ. Press, 2009. [37] J. Nicolaisen, V. Petrov, and L. Tesfatsion, “Market power and efficiency in a computational electricity market with discriminatory double-auction pricing,” IEEE Trans. Evol. Comput., vol. 5, no. 5, pp. 504–523, Oct. 2001. [38] S. Kraus, J. Wilkenfeld, and G. Zlotkin, “Multiagent negotiation under time constraints,” Artif. Intell., vol. 75, no. 2, pp. 297–345, 1995. [39] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes: The Art of Scientific Computing. Cambridge, U.K.: Cambridge Univ. Press, 1992.

Sebastian Kruse received a diploma in mechatronics from the University of Technology Hamburg, Hamburg, Germany, in 2011. For his diploma thesis he conducted research about decision making in multiagent systems at the Distributed Information and Automation Laboratory, University of Cambridge, Cambridge, U.K. During his studies he was a Visiting Researcher at the Human Engineering laboratory, University of California Berkeley, Berkeley, CA, USA, where his researched focused on the development of an active knee prosthesis for amputees. He started a common research project with the University of Technology Hamburg and Audi AG working on the simulation of friction induced vibrations for brake systems. Currently, he coordinates the NVH-development in the Foundation Brake Department at Audi AG, Munich, Germany.

Alexandra Brintrup received the Ph.D. degree in engineering from Cranfield University, Cranfield, U.K., in 2007. She carried out her postdoctoral studies at the University of Cambridge, Cambridge, U.K. She is a Senior Research Fellow at the University of Oxford, Oxford, U.K., and a Lecturer in Manufacturing at Cranfield University. Her research lies at the intersection of complexity science and operations management, which aims to model, analyze, and control how local agent behaviors result in global

32


performance and resilience within organizational systems, and how the global environment impacts individuals in return. Dr. Brintrup is a member of the IEEE Computer Society.

Duncan McFarlane received the Ph.D. degree in the design of robust control systems from the University of Cambridge, Cambridge, U.K., in 1988. He is a Professor of Service and Support Engineering at the Engineering Department, University of Cambridge, and Head of the Distributed Information & Automation Laboratory within the Institute for Manufacturing. He is also the Director of the Cambridge Auto-ID Lab and Research Director of the Service and Support Engineering Programme and the Aero ID Programme. His research focuses on distributed industrial automation, reconfigurable systems, radio-frequency identification (RFID) integration, and valuing industrial information.

Tomás Sánchez López received the B.Sc. and M.Sc. degrees in computer science from the Polytechnic University of Valencia, Valencia, Spain, in 2004 and the Ph.D. degree in computer science from the Korean Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 2008. He joined the Distributed Information and Automation Laboratory, University of Cambridge, Cambridge, U.K., where his researched focused in wireless sensor networks, radio-frequency identification (RFID), multiagent systems, and new concepts

in distributed systems known as the “Internet of Things.” Currently, he works at Innovation Works, EADS U.K., Newport, U.K., as the Team Leader for the Information Fusion group. His role involves the supervision and execution of projects related to the distribution and fusion of intelligence.

Kenneth Owens, photo and biography not available at the time of publication.

William E. Krechel received the B.S. degree in aeronautics from St. Louis University, St. Louis, MO, USA, in 1992 and the M.S. degree in technology management from Washington University, Seattle, WA, USA, in 1997. He is a Senior Level Systems Engineer on the Boeing Research and Technology, Support and Services, Supply Chain Management team as the Technical Lead Engineer on the team, The Boeing Company, Seattle, WA, USA. He is a veteran of the U.S. Navy and holds an FAA Airframe and Powerplant license. His primary responsibility at The Boeing Company is to provide technical leadership to the BR&T Supply Chain Management team and to integrate academic research from Boeing strategic academic partners in order to leverage the best of Boeing, industry, and academia. He has 19 years of aerospace support and services experience and has led research and development initiatives to improve supply chain capability, efficiency, effectiveness, and velocity in both military and commercial applications. He presently has five patents pending approval by the U.S. Patent Office relating to revolutionary advancements of supply chain technology.

Designing Automated Allocation Mechanisms for Service Procurement

Designing Automated Allocation Mechanisms for Service Procurement

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