Dynamic Business Process Formation by ... - Semantic Scholar

Proceedings of the 37th Hawaii International Conference on System Sciences - 2004

Dynamic Business Process Formation by Integrating Simulated and Physical Agent Systems Fu-ren Lin, Shyh-ming Lin Department of Information Management National Sun Yat-sen University Kaohsiung, Taiwan 804, R.O.C [email protected]

Po-win Hsueh Metal Industries Research and Development Centre Kaohsiung, Taiwan 811, R.O.C [email protected]

Abstract

One trend of information technology development for companies is to facilitate dynamical workflows formation to conduct business processes to gain competitive advantages. In order to keep pace with the trend, we anticipate that information systems of partner companies in a supply chain possess a certain degree of autonomy to plan proactively or react dynamically when coordinating with each other so as to capture business opportunities. Although certain degree of human involvement may be needed, as the business processes get complicated, the lead time of decision making gets compressed, and the variation of participating partner companies gets large, the capability of automated process formation then becomes essential. The embedded capability of automatic business process formation and management for inter-organizational information systems is the key to reaching swift coordination and collaboration for supply chain management. Agents are autonomous, proactive, and adaptive software that conatin some level of intelligence to perform complicated tasks autonomously [19, 25, 11, 12]. In a multi-agent environment, agents can also cooperate with other agents to carry out more complex tasks than they themselves alone can handle. The characteristics described here show that agent technologies have great potential to support dynamic process formation. An agent is rational if it acts to maximize its utility; however, its behavior in achieving given goals is imposed by given conditions and constraints, which is coined as the bounded rationality [22]. Framing the inter-organizational coordination problem with the concept of bounded rationality, the coherence of processes executed by different business entities may be constrained by incomplete information, information processing costs, and some non-neoclassical objective

This paper proposes a new framework that integrates simulated and physical agents to provide an efficient way for companies to form supply chains dynamically. In this integrated framework, physical agents coordinate with inter-organizational physical agents to conduct business processes whereas simulated agents model and analyze business processes to support physical agents in making rational decisions under uncertain situation and with incomplete information. This paper surveys different techniques used for dynamic process coordination and explains how the proposed integrated framework can be used by companies to reach a commonly accepted goal in dynamic supply chains. This paper also elaborates the efficient supply chain formation using a business process example of the mold industry, and finally discusses the development issues of this framework and future research directions.

1. Introduction The Internet has dramatically changed the way many companies conduct business, and that change results in new business models. The utilization of new business models, which guide companies’ policies and practices, enables companies to adapt themselves quickly in accordance with the dynamically changing business environments. Moreover, companies run on new business models are able to focus on their own core competence and cooperate with other complementary partners, and consequently they can operate efficiently to deliver values to customers. Thus, in the Internet era, an emerging need for companies is that they should coordinate to react to the market changes effectively and efficiently by virtue of information technologies.

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functions of economic models. The development of multi-agent systems for dynamic process formation should increase information sharing to keep track of the changes of surroundings in order to reduce the uncertainties of decision making. However, the information processing cost may prohibit obtaining necessary information to update the perception of the environment. Due to the constraint of obtaining complete information to reach optimal decision outcomes, shifting from optimal to satisfiable solution becomes the non-neoclassical objective of economic models. The mechanism of maintaining true belief about the outside world with viable decision choices enables agents to respond to dynamic environmental changes. This mechanism, embedded in multi-agent systems, calls for the capabilities of simulation, analysis, and execution. In this paper, first, we review the agents technologies applied to business process modeling, simulation, analysis, and facilitation in Section 2. In Section 3, we describe in detail the integrated framework of simulated and physical agents, and elaborate scenarios of utilizing the integration model using an example. In Section 4, we illustrate the process of dynamic business process formation. In Section 5, we develop an example of mold industry supply chain management to explain how the integrated systems facilitate dynamic process formation. In Section 6, we discuss the development issues of the integrated agent system and the future trend of the integrated agent system in Web services. Finally, Section 7 concludes this paper and specifies future research directions.

2. Agents for Business Process Modeling, Simulation, Analysis, and Execution 2.1. Agents for business process management Business process management consists of several tasks, such as process modeling, analysis, and execution. Process modeling approaches are grouped into four categories: activity-, object-, role-, and speech-act oriented approaches [10]. Commonly used process modeling methods include IDEF0, IDEF1, IDEF1X, IDEF3, RAD, REAL, Dynamic Modeling, Object-Oriented Modeling (OO), AI, and Multi-agent information system (MAIS) [20, 1, 8, 5, 23, 24, 27, 16]. The essential concepts to describe business processes extracted from these modeling methods include activity, behavior, resource, relation, agent, information, entity, event, verification and validation, and modeling procedure [14]. Business process

verification and validation can be achieved by various approaches, such as Petri nets and simulation [2]. The execution of business processes relies heavily on integrated information systems within or between organizations, such as well known enterprise resource planning (ERP) and supply chain management (SCM) systems. As business processes shift to the out-sourcing paradigm, the modeling, analysis and execution of business processes need to represent the characteristics of distributed autonomous entities across organizations. Bearing with autonomy, message passing, and selforganization characters, agents are suitable to represent these distributed autonomous business entities. A multi-agent system in general possesses the following properties: (1) each agent has incomplete capabilities to solve a problem, (2) there is no global system control, (3) data is decentralized, and (4) computation is asynchronous [9]. Many research efforts have been spending on developing multi-agent systems for business process management. For example, Lin and Shaw [15] demonstrate a multi-agent framework to simulate and analyze three typical supply chain structures. Fox, Barbuceanu, and Teigen [6] propose agent-oriented supply chain management to demonstrate the coordination of agents for semiconductor industry. Yung and his colleagues [28] adapt constraint networks to coordinating supply chain activities. Among these applications of agent technologies, there are basically two types of agent designs: simulated and physical agents. Simulated agents capture the structure of business entities and their corresponding business rules, and then simulate and analyze their interactions. Physical agents on behalf of business entities can communicate, coordinate, and collaborate with corresponding physical agents representing other business entities. The development of the simulated agent framework emphasizes the utilization of multi-level of abstraction and separation of concerns to simulate and analyze business processes [14]. For example, Swarm is a multi-agent simulation platform initiated by Santa Fe Institute [21], which became the Swarm Development Group, a non-profit organization (see www.swarm.org). Swarm is composed of a set of object libraries, which allow users to construct objects to represent agents and their actions. Swarm is a general-purpose package for simulating concurrent, distributed artificial worlds. The core of Swarm is an object-oriented framework for defining an agent’s behavior and the interaction between agents during simulation.

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Swarm uses the individual-based modeling approach, which allows each agent to have its own set of internal state variables. Individual agents have their local views of the world, and the combination of individual behavior determines the collective behavior of the whole group. In the Swarm system, an agent can itself be a swarm of agents. This hierarchical inheritance, which can be a depth of several layers, is called the nested inherent hierarchy property as shown in Figure 1. A swarm can be represented by a set of agents coordinated at the level of organization. Moreover, Swarm is not just a collection of objects but also a schedule. A nest structure of swarms is a nested hierarchy of schedules. The recursive management of time supports an entire spectrum of time and synchrony management ranging from strict, top-down, lock-step synchrony managed by a single sequential executor to loose asynchrony with effective parallelism. The Swarm simulation platform has been applied to many domains, such as, economics, finance, business, etc., collected in Luna and Stefansson [18]. Schedule

The Model

Probes

Swarm

The Interface

Agent Sub-Swarm

Sub-sub-Swarm

Figure 1. The Swarm’s nested inherent hierarchical structure The efforts of developing physical agents as software systems are firstly summarized as distributed artificial intelligence in Bond and Gasser’s book [3], and the inter-operability of multi-agent system across the Internet has been initiated by the Foundation for Intelligent Physical Agents (FIPA, http://www.fipa.org) in 1996. FIPA was formed to produce software standards for heterogeneous and interacting agents and agent-based systems. Many publicly available implementations of agent platforms conform to the FIPA specifications, such as FIPA-OS, JADE, et cetera. Figure 2 illustrates the building layers of

physical agents complying with FIPA standards (http://www.fipa.org).

Applications Abstract architecture Agent communication

Interaction protocols

Agent management

Communication acts

Agent message transport

Content languages

ACL representation

Envelope representation

Transport protocols

Figure 2. FIPA Specification

2.2. Techniques for dynamic process coordination The deployment of inter-organizational information systems upgrades the information sharing level, such as sharing inventory, point-of-sales information, and increases the flexibility of business processes to meet constantly changing market environments. The coordination among partner companies enhances the dynamics of business processes. Bearing the goal of enhancing business process dynamics, techniques have been proposed by various research works. The feasibility of a dynamic business process should satisfy constraints imposed by individual participating business divisions and companies, which can be formulated as a distributed constraint satisfaction problem (DCSP). Yokoo [26] reviewed a set of techniques to handle the distributed constraint satisfaction problem, such as asynchronous backtracking, asynchronous weak-commitment search, distributed breakout, and distributed consistency algorithms. Hannebauer [7] proposed the autonomous dynamic reconfiguration mechanism as a meta-process interleaved with a collaborative problem solving process for agents to determine their own structure of their organization on the individual level. It uses agent melting and splitting operations to redistribute problem solving knowledge, competence, goals and skills to reach effective workflows for business processes. The formation of dynamic business processes can be also viewed as a general partial global plan (GPGP) to coordinate distributed problem solving [13]. The agent coordination problem, represented as an extended AND/OR goal tree, is regarded as an optimization problem. To solve this problem is to maximize the utility of groups of agents, in the meantime attaining a number of higher-level goals. The commonly agreed

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workflow of a business process can be viewed as a decentralized and loosely coupled collection of problem solvers, and task distribution is a form of contract negotiation. Davis and Smith [4] proposed a framework of negotiation called contract net to specify communication and control in distributed problem solving environments. It initiated the efforts on tackling three issues: (1) the global coordinated behavior based on local incomplete knowledge, (2) the message protocols dealing with problem solving, and (3) the utility of negotiation as a fundamental mechanism for interaction. These potential mechanisms developed in collaborative problem solving and distributed constraint satisfaction can be applied to dynamic process formation in multi-agent systems. These fundamental techniques serve as the basis for the stateof-the-art multi-agent framework to integrate with emerging agent standards, such as FIPA, to facilitate the business process management.

3. The Integration of Simulated and Physical Agents 3.1 The framework of integrated agent system physical agents

The simulated agents, the epitome of the business entities and their functionalities, model the business processes of the real business environment. Agents located in the biggest circle placed in the center represent functions performed by a company, and agents in the other smaller circles represent corresponding functions of its business partners. Agents may represent a company’s functional entities, such as scheduling, order management, procurement, and product delivery. The data needed for these simulated agents to simulate and analyze business processes may come from the company’s information systems and the information exchange with its partner companies. These simulated agents can make use of these information sources to model, simulate, and analyze business processes between these participating companies. The alternatives suggested by simulation results contribute to corresponding physical agents in making decisions. Although still constrained by bounded rationality due to organizational structure and information sharing policies, data accessed by simulated agents proportionally reflect the dynamics of the business process. Moreover, based on theories, such as transaction cost economics, simulated agents can be used for analyzing alternative policies in managing business processes. In this manner, the collection of simulated agents acts as a decision support system to facilitate the decision making. Org. A

Org. D Org. C Org. B

Org. E Simulated agent set D

simulated agents

Physical agent set A

Org.C

Org.A

Simulated agent set A

Physical agent set E Physical agent set C

Physical agent set B

Physical agent set D

Self Org.B

Org.D

Simulated agent set B

AP

Simulated agent set E Simulated agent set C

DB

Figure 3. The integrated agent system with information systems The integrated agent system utilizing the corresponding capabilities of simulated and physical agents connects with information systems to execute business processes as shown in Figure 3. The integrated agent system embedded in an organizational information system has the combined abilities of analysis and decision-making. The analysis ability comes from simulated agents while the decisionmaking ability comes from physical agents.

Figure 4. The framework of the integrated agent system for dynamic business process formation Physical agents, represented as star-shaped entities in Figure 3, interact with physical agents embedded in partner companies based on the decision alternatives suggested by simulated agents. A company’s physical agents gifted with the negotiation ability can interact with partner physical agents to reach mutual agreed workflows to execute business processes based on initial knowledge provided by simulated agents. The responding information from other physical agents

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during the negotiation process can feed to simulated agents to conduct a new cycle of simulation, analysis and alternative suggestions, which will be used by physical agents to resume the negotiation process. Figure 4 illustrates the mapping from organizations with certain inter-organizational structures, e.g., supply chain network, to its corresponding physical agent sets, and the interaction between physical agents and simulated agents facilitates the formation of business processes. Simulated agents in each agent system simulate the what-if scenarios of physical environment, and physical agents will act on the suggestions offered by simulated agents.

3.2. The example of simulated and physical agent platforms: Swarm and FIPA (JADE) quotation agents inventory agents

Scheduling agents negotiation agents

trust agents

Make-or-buy agents workflow agents

Simulated agents container Org.C

Org.A

Self Org.B

Org.D

Swarm Kernel

Figure 5. Simulated agents on the Swarm platform There are many agent platforms available for researchers and practitioners to rapidly develop their own multi-agent systems. Every platform, such as Swarm, AScape, JADE, Zeus, has its own features and can be applied to specific domains. In this paper, Swarm as shown in Figure 5, serves as the example of a simulated agent platform, and JADE in Figure 6 as that of a physical agent platform. First, we elaborate characteristics of specific simulated agents designed by the Swarm simulation platform as follows: (1) A workflow agent is designated to simulate the processes and its activities of how to fulfill an incoming order. Besides, it simulates the management

of not only inter-organizational workflows but also intra-organizational workflows. The workflow agent is used to simulate the policies of how to handle the exceptions occurring in the process of fulfilling an order. (2) A trust agent is designated as a mechanism which makes recommendations of potential suppliers for outsourcing activities in the initial stage of forming a supply chain. The recommendations are made on the basis of the company’s past experiences in doing business with others. Lin, Song, and Lo [17] evaluated the performance of trust agents based on three trust antecedents: ability, integrity, and benevolence. (3) A make-or-buy agent is designated to support decision makings on manufacturing in-house or outsourcing. The make-or-buy agent simulates makeor-buy scenarios and determines which one is more cost effective. Several factors, such as strategies, business context, capacity, capability, statuses of other companies, and assessment of value creation, can be taken into consideration in the make-or-buy decision model. (4) A scheduling agent is designated to simulate intra-organizational activities, such as manufacturing, production planning, and shop floor control. The scheduling agent plays an important role in many situations, such as running simulations of ATPs (available to promise, quantity and delivery date commitment to a customer order), inserting a new order, and rearranging the ongoing schedules while negotiating with extra-organizations. (5) A quotation agent is designated to simulate the price and ATP of a specified activity that are queried by extra-organizations. (6) An inventory agent is designated to periodically simulate, calculate and adjust the safety stock, ordering quantity, and re-order point of parts in the dynamically changing business environment and the statuses of simulated partner suppliers. Inventory agents run simulations based on a pre-built inventory model. When the order or re-order points are reached, the inventory agent will inform its physical order-placing agent to issue purchase orders. (7) A negotiation agent is designated to simulate the negotiation of all inter-organizational activities and tries to find the workable coalition before a supply chain is formed. JADE (Java Agent DEvelopment Framework) is a physical multi-agent development framework which complies with FIPA specifications and aims at simplifying the development and implementation of multi-agent systems. The advantages of utilizing JADE as a physical agent platform include its capabilities to

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distribute agents across different operating platforms and make these agents to easily communicate with each other through the Internet. Besides, it can simply integrate with existing systems or databases, and provide graphical interfaces for users to manage their agents. Various physical agents (as portrayed in Figure 6) gifted by different functions are elaborated as follows: (1) An order-receiving agent is designated to listen to extra-organizational order-placing agents and to receive incoming orders. (2) A querying agent is designated to query extraorganizational agents about the prices and availabilities of specified outsourcing activities in the order fulfillment process of a supply chain.

querying agents

negotiation agents

quoting agents

4. Dynamic Business Process Formation Using the Integrated Agent System The process of forming a business process begins as soon as a new customer’s order arrives, and can be outlined in general into four major steps: (1) identify the customer’s requirements; (2) lay out activities to meet the customer’s requirements; (3) design the workflow to execute these activities; (4) coordinate with partner suppliers corresponding to activities to execute the business process. The integrated agent system here can fully support such process to form dynamic business processes. The scenario of how the integrated agent model performs is shown in Figure 7. The sequence of triggering operations is labeled in numbers. How these operations contribute to the four steps respectively and which agents will execute them are described below. :Extra-organization (customer)

:Intra-organization Physical agents

:Intra-organization Simulated agents

:Extra-organizations :Intra-organization AP or DB or Decision maker (suppliers)

1:PlaceOutsourcingOrder (order) Acknowledgement (order)

2:AddNewOrder(order) 3:RunSimulations(order)

order-placing agents

order-receiving agents

workflow management agents

4:FeedbackSimulationResults (processes) 5:Query TheATPandPrice (suborders) 6:GiveQuotation(suborders)

Recursive execution while no workable processes exist 7:DetermineWorkableProcesses [No workable processes exist] 8:RunSimulations(order, quotations)

[No workable processes exist] 10:Negotiate(suborders,potential_suppliers)

Agent Management System

Directory Facilitator

9:FeedbackSimulationResults(suborders, potential_suppliers, negotiation_strategies)

physical agents container

[Workable processes exist] 11:PlaceOutsourcingOrder (suborders, partner_suppliers) Acknowledgement (suborder) 12: FormProcess (suborders, partner_suppliers, process)

Figure 7. The sequence diagram of utilizing integrated agent model

Message Transport System

Figure 6. Physical agents on the JADE platform (3) A quoting agent is designated to quote for queries from extra-organizational querying agents. After receiving the simulated quotation data, the quoting agent returns the quote to the extraorganizational querying agent. (4) A negotiation agent is used to negotiate with extra-organizational negotiation agents (including customers and suppliers) to reach a commonly acceptable goal. (5) An order-placing agent is designated to place outsourcing orders to specific extra-organizational order-receiving agents. (6) A workflow management agent is designated for managing not only inter-organizational workflows but also intra-organizational workflows.

In Step 1, the company receiving the order identifies the customer’s requirement, such as specification of order, time and place to deliver, and price. Operations that contribute to Step 1 are described as follows: (1) PlaceOutsourcingOrder(order): The company’s customer place an order via its physical order-placing agent to the company. The company’s physical orderreceiving agent receives this order, and acknowledges it. (2) AddNewOrder(order): This operation acknowledges the receiving of a customer’s order after the order-receiving agent receives the order, extracts the customer’s requirement, and stores the order to databases. The actions of Step 2 are taken by the order receiving company to lay out activities needed to meet the customer’s requirement. Operations that compose Step 2 are described as follows: (3) RunSimulations(order): a physical orderreceiving agent informs the simulated workflow agent

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to execute this operation. The simulated workflow agent cooperates with the agents’ owner to analyze the order and simulate the processes of fulfilling the order in the virtually created business system. (4) FeedbackSimulationResults(processes): a simulated workflow agent invokes this operation to deliver simulation results to its physical querying agent. (5) QueryTheATPandPrice(suborders): a physical querying agent executes this operation to query the ATP and price information for the suborders to the physical quoting agent of a potential supplier. The suborders are the outsourcing activities, which are divided by a physical querying agent after the make-orbuy decision is made. (6) GiveQuotation(quote): the physical quoting agent of an supplier executes the GiveQuotation operation to return its quote after the profitable quoting policies are formulated by its simulated quotation agent. Step 3 aims to design workflows to execute activities of business processes, and decides activities to be completed in-house or by outsourcing. Operations that compose Step 3 are described as follows: (7) DetermineWorkableProcesses: It is a selfdelegated operation driven by a physical querying agent to determine workable processes of how to execute activities either in-house or outsourcing. (8) RunSimulations(processes, quotes): If no workable process exists, a physical querying agent will request the simulated negotiation agent to conduct the RunSimulations operation to further analyze the processes of fulfilling the order and the quotes that come from potential suppliers. (9) FeedbackSimulationResults(suborders, negotiation_strategies): a simulated negotiation agent executes this operation to generate negotiation strategies corresponding to specific suborders and potential suppliers to its physical negotiation agent. (10)Negotiate(suborders, potential_suppliers, negotiation_strategies): This operation is performed by a physical negotiation agent if no workable process exists. The negotiation strategies are generated by a simulated negotiation agent corresponding to specific suborders and potential suppliers. The goal of this operation is to form a feasible workflow to execute business processes for a supply chain. In Step 4, operations will be executed based on workflows generated in Step 3 to form business processes. Operations that contribute to Step 4 are described as follows: (11)PlaceOutsourcingOrder(suborders, partner_ suppliers): The company’s physical order-placing agent

issues orders to partner suppliers via this operation if workable processes are determined. Suborders are delivered to physical order-receiving agents of those corresponding partner suppliers. (12)FormProcess(suborders, partner_suppliers, process): After a workable process is determined and all the outsourcing suborders are delivered to partner suppliers, a new business process to fulfill the order is thus formed.

5. An Example of the Supply Chain Management in Mold Industry In this section, we introduce an example in the mold industry to demonstrate how the proposed integrated agent framework dynamically forms a supply chain to execute the order fulfillment process. Figure 8 shows the simplified order fulfillment process (OFP) of a fictitious company EMM (Excellent Mold Manufacturing Corporation). TPC

EMM

Suppliers

Order placing

Analysis & Design

Raw material / parts supply & Design

Various Design Machining & tooling &&Design

Mold assembly

Mold Testing

Mold receiving

Shipment

Figure 8. The order fulfillment process (OFP) of EMM

5.1 The order fulfillment process in the supply chain of the mold industry When EMM receives an order from its customer company, it designs the mold based on the order specification, and then decides its outsourcing scheme. Once the outsourcing scheme is determined, EMM frequently communicates with the potential suppliers listed in the scheme, and negotiates with them to reach a compromised price and schedule to finish the outsourcing activities. If all the compromises are reached, EMM sends corresponding contracts to them and monitors contracted activities. After all outsourcing activities are carried out and finished parts are shipped back, EMM assembles and tests the mold, and then delivers it to the customer.

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If we take a closer look into the order fulfillment process, we will find that the framework proposed in this paper can help to improve efficiency. Let’s take EMM as a leading role and provide an example about EMM’s operation to explain how the framework can be applied to mold industry supply chain. The example starts at receiving a customer’s order and is briefed as follows. A personal computer (PC) manufacturer named TPC (fictitious name) has designed a new model of multi-processor compact PC and plans to launch it into market within 4 weeks. This product is equipped with a special designed motherboard, where different cooling fans with temperature sensors are mounted on CPUs and accessory chips. All cables of fans and sensors are attached to a newly designed connector. TPC delivers the solid-model CAD (computer aided design) files and specification of this connector to his long-term contracted partner EMM, and EMM accepts the order and is required to deliver the workable press mold in 2 weeks (equal to 10 working days) . According to EMM’s estimate, all outsourcing parts must be finished and returned in 6 days and the total cost should be controlled to maintain its profit. After receiving these outsourcing parts, EMM can spend the rest of the time to assemble and test the mold, and then delivers the mold to TPC as the designated date.

5.2 The demonstration of the integrated agent system using Swarm and JADE platforms Now, we follow the four steps listed in Section 4 to illustrate how the supply chain can be formed to perform the order fulfillment process in the mold industry. (1) Identify the customer’s requirements. TPC’s physical order-placing agent invokes the PlaceOutsourcingOrder operation to deliver its order to EMM. After EMM’s order-receiving agent receives the order, it parses the order, invokes the AddNewOrder operation to store the order data into databases, and then forwards the customer’s requirements to the interior design department. (2) Lay out activities to meet the customer’s requirements. EMM’s order-receiving agent informs its simulated workflow agent to invoke the RunSimulations operation. An experienced team of EMM takes two days to complete the connector analysis and mold design, and lays out the manufacturing process. The workflow agent then simulates the manufacturing process in the virtually created business system, and

invokes the FeedbackSimulationResults operation to deliver simulation results to its physical querying agent. Figure 9 shows the manufacturing process of the mold’s key parts. In Figure 9, every activity is driven by its previous activity. EMM then tries to find potential partner companies, of which EMM is going to inquire, and see if their quotes meet EMM’s requirements. The physical querying agent next invokes the QueryTheATPandPrice operation. mold base standard parts

Purchasing & Outsourcing

Purchasing Outsourcing EDM electrode design & making Purchasing tool steel

Wire cutting

EDM Heat treatment

Surface treatment

Assembly & Testing

Milling

Figure 9. The workflow of the manufacturing process (3) Design the complete process workflow of the activities. After the quotation information returns, the physical querying agent invokes the DetermineWorkableProcesses operation. If the returned quotation information fails to compose workable processes, the negotiation is needed. Such being the case, a physical querying agent will request a simulated negotiation agent to conduct the RunSimulaion operation to generate feasible negotiation strategies. EMM’s physical negotiation agent thereafter begins to negotiate with potential partner suppliers until the workable processes are generated. (4) Coordinating with partner suppliers corresponding to activities to perform the order fulfilling process. After a workable process is generated, the physical order-placing agent invokes the PlaceOutsourcingOrder operation to deliver suborders to physical orderreceiving agents of partner suppliers, and thus successfully form a supply chain to fulfill the order.

6. Discussions The proposed integrated agent framework outperforms the traditional approach to forming a dynamic supply chain in terms of efficiency, effectiveness, and flexibility. The traditional approaches to forming supply chain are timeconsuming processes, but the proposed integrated agent system can make viable decisions based on the simulation results from simulated agents with up-todate intra- and inter-organizational information. In this

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study, Swarm, serving as the nested hierarchical simulated agent platform, simulates supply chain partner companies and their functionalities, and analyzes the order fulfillment process to generate viable workflows and negotiation strategies. JADE, serving as our physical agent platform, enables interorganizational coordination to execute business processes. In the viewpoint of efficiency, these two agent platforms integrate to streamline the simulation, analysis, and execution of business processes. Agents with autonomy, message passing, and selforganization characters are suitable to represent these distributed autonomous business entities across organizations. Since the simulated agents model the real business environment by updating intra- and interorganizational state information, and run simulations to lead physical agents to take effective actions to perform business processes, the integrated system increases the contextual awareness, reduces the uncertainties, and thus helps improve the effectiveness of real-time decision makings. The simulated agents can support the handling of exceptional situations that happen during the execution of business processes by efficiently simulating various scenarios to generate different alternatives in responding to unexpected events. The mechanism of maintaining true belief about the outside world with viable decision choices enables agents to respond to dynamic environmental changes. Thus, in terms of flexibility, this integrated agent system can deal with both predefined and ad hoc processes. There are still other issues to be considered, including inter-organizational business processes and communication language format for physical agents, and the future trend of the integrated agent system in Web Services. Because the physical agents in the integrated system have to communicate with interorganizational physical agents, we have to standardize the agent communication language (ACL) in order to communicate and negotiate to form supply chains. The shared inter-organizational business processes followed by physical agents and the communication language used in the processes can be developed by complying with RosettaNet (http://www.rosettanet.org/) and ebXML (http://www.ebxml.org/) standards. The JADE platform supports XML-based data exchanges and thus provides extendibility and flexibility for system architects to formulate their ACL messages. The JADE platform requires both XMLCodec package (developed by JADE-Board) and SAX parser package (developed by SunMicro) to support XML data exchanges. The integrated agent system can be extended to work with the Web service platform since both systems

operate on the Internet, use XML-based messages to exchange information, and interoperate directly with other applications. The embedded autonomy of agent systems in a Web service inherits the merit of the integrated agent system in efficiency, effectiveness, and flexibility to deliver Web services, such as automated partner selection facilitated by simulated trust and negotiation agents and physical negotiation agents via UDDI and WSDL protocols. An additional Web service standard, BPEL4WS (Business Process Execution Language for Web Services, http://www-106.ibm.com/developerworks/ webservices/library/ws-bpel/). BPEL4WS extends the Web Services interaction model and enables the ad hoc workflow execution.

7. Conclusion and Future Research The supply chain formation is a complex problem in the dynamically changing business environment since companies involved in such an environment have to coordinate with others to make decisions under uncertainty and incomplete information. This paper has presented the integrated agent framework to leverage the respective competence of simulated and physical agents to dynamically form supply chains. A physical agent, in our framework, coordinates with inter-organizational physical agents to execute business processes, whereas simulated agents model and analyze business processes to support physical agents to make decisions. Several research efforts may be spent on realizing the proposed integrated agent system on dynamic business process formation. For example, we can implement a prototype system based on the proposed framework, and then evaluate its performance with other frameworks such as simply simulated agents system. Besides, the prototype can be designed to interoperate with the Web service architecture to make the prototype itself a service oriented system that can be more easily composed into dynamic workflows and thus form dynamic supply chains. We may also develop a methodology, with which this integrated system can dynamically adjust its own strategies, such as quoting or negotiation policies.

Acknowledgement The authors are grateful to the following sponsors in this research: MOE Program for Promoting Academic Excellent of Universities under the grant number A-91H-FA08-1-4, and the Metal Industry Research and Development Centre.

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References [1] Appleton, D.S., Business reengineering with business rules, In V. Grover, & W. J. kettinger (Eds.), Business Process Change: Reengineering concepts, Methods and Technologies, , Idea Group Publishing, pp. 291-329, 1995. [2] Basu, A., and Kumar, A., “Research commentary: workflow management issues in e-business”, Information System Research, 13(1): 1-14, 2002. [3] Bond, A. H., Gasser, L. (Eds.), Readings in Distributed Artificial Intelligence, Morgan Kaufman Publishers, San Mateo, CA., 1988. [4] Davis, R, and Smith, R.G., Negotiation as a metaphor for distributed problem solving, in A.H. Bond, and L. Gasser (Eds.), Readings in Distributed Artificial Intelligence. Morgan Kaufman Publishers, San Mateo, CA., pp. 333-356, 1988. [5] Denna, E. L., Jon Jasperson, Kenny Fong, Reengineering and REAL business Process Modeling, In V. Grover, and W. J. Kettinger (Eds.), Business Process Change: Reegineering concepts, Methods and Technologies, Idea Group Publishing, pp. 350-375, 1995. [6] Fox, M.S., Barbuceanu, M., and Teigen, R., “Agentoriented supply-chain management”, International Journal of Flexible Manufacturing Systems, 12(2,3): 165-188, 2000. [7] Hannebauer, M., Autonomous Dynamic Reconfiguration in Multi-agent Systems: Improving the Quality and Efficiency of Collaborative Problem Solving, SpringerVerlag Berlin Heidelberg, Germany, 2002. [8] Huckvale, T., & M. Ould, “Process modeling - who, what and how: Role Activity diagramming”, In V. Grover, and W. J. Kettinger (Eds.), Business Process Change: Reegineering concepts, Methods and Technologies, Idea Group Publishing, pp. 291-329, 1995. [9] Jennings, N.R., Sycara, K. and Wooldridge, M., “A roadmap of agent research and development’, Autonomous Agents and Multi-Agent Systems Journal, 1(1): 7-38, 1998. [10] Kueng, P., Kawalek, P., and Bichler, P., “How to compose an object-oriented business process model?” In Brinkkemper, S. et al. (Eds), Method Engineering, Proceedings of the IFIP WG8.1/WG8.2 Working Conference, Atlanta, 1996. [11] Foner, L.N., “What’s an agent, anyway? A Sociological Case Study”, Agents Group, MIT Media Lab, May, 1993. http://foner.www.media.mit.edu/people/foner/Reports/Julia/ Agents--Julia.pdf. [12] Krause S., Morais de Assis Silva F., Magedanz T., PopescuZeletin R., Falsarella O. M., and Mendez C. R. A., “MAGNA – A DPE-Based Platform for Mobile Agents in Electronic Service Markets”, Proceedings of Third International Symposium on Autonomous Decentralized Systems, pp.93-102, 1997. [13] Lesser, V. et al., “Evolution of the GPGP/TAEMS Domain-Independent Coordination Framework.”, University of Massachusetts/Amherst Computer Science Technical Report, Number 02-03, http://mas.cs.umass.edu/research/ taems/, January, 2002.

[14] Lin, F.-r., Yang, M.-c., and Pi, Y.-h., “A Generic Structure for Business Process Modeling”, Business Process Management Journal, 8(1):19-41, 2002. [15] Lin, F.-r., and Shaw, M.J., “Reengineering the order fulfillment process in supply chain networks”, International Journal of Flexible Manufacturing Systems, 10(3): 197-229, 1998. [16] Lin, F.-r., Tan, G. W., and Shaw, M.J., “Multi-agent enterprise modeling”, Journal of Organizational Computing and Electronic Commerce, 9(1)” 7-32, 1999. [17] Lin, F.-r., Song, Y.-w., and Lo, Y.-p., “Effects of trust mechanisms on supply chain performance using multi-agent simulation and analysis”, The First Workshop on e-Business, Barcelona Spain, December 15, 2002. [18] Luna, F., and Stefansson, B., (Eds.) Economic Simulation in Swarm: Agent-based Modeling and Object Oriented Programming, Kluwer Academic Publishers, Boston, 2000. [19] Maes, P., “Artificial life meets entertainment: life like autonomous agents”, Communications of the ACM, 38, 11, pp. 108-114, 1995. [20] Mayer, R.J., Benjamin, P.C., Caraway, B.E., and Painter, M., A framework and a Suite of method for business process reengineering, In V. Grover, & W. J. kettinger (Eds.), Business Process Change: Reengineering concepts, Methods and Technologies, Idea Group Publishing, pp. 245290, 1995. [21] Minar, N., Burkhart, R., Langton, C., and Askenazi, M., “The Swarm simulation system: a toolkit for building multiagent simulations”, http://www.swarm.org, 1996. [22] Simon, H.A., Models of bounded rationality, Vol. 1. Cambridge, MA: The MIT Press, 1982. [23] van Meel, J. W., P. W. G. Bots, and H.G. Sol, Lessons learned from business engineering within Amsterdam Municipal Police Force: the applicability of dynamic modeling, In V. Grover and W. J. Kettinger (Eds.), Business Process Change: Reengineering Concepts, Methods and Technologies, Idea Group Publishing, pp. 402-423, 1995. [24] Wang, S., “OO modeling of business process”, Information system Management, pp.36-43, spring 1994. [25] Wooldridge, M. and Jennings N.R., Agent theories, architectures, and languages: a survey, in M. Wooldridge and N.R. Jennings (Eds.), Intelligent Agents, Berlin: SpringerVerlag, pp. 1-22, 1995. [26] Yokoo, M., Distributed Constraint Satisfaction: Foundations of Cooperation in Multi-agent Systems, Springer-Verlag, Berlin Heidelberg Germany, 2001. [27] Yu, E. and Mylopoulos, J., “AI modeling for business process reengineering”, IEEE Expert, pp. 16-23, August, 1996. [28] Yung, S.K., Yang, C.C., Lau, A.S.M, and Yen, J., “Applying multi-agent technology to supply chain management”, Journal of Electronic Commerce Research, http://jecr.org, 1(4), 2000.

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