Robotics and Computer-Integrated Manufacturing 36 (2015) 36–44
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Robotics and Computer-Integrated Manufacturing journal homepage: www.elsevier.com/locate/rcim
A multi-agent architecture for outsourcing SMEs manufacturing supply chain Sushma Kumari a,n, Akshit Singh a, Nishikant Mishra a,n, Jose Arturo Garza-Reyes b a b
School of Management and Business Aberystwyth University, UK Centre for Supply Chain Improvement, The University of Derby, Derby DE22 1GB, UK
art ic l e i nf o
a b s t r a c t
Article history: Received 1 February 2014 Received in revised form 25 September 2014 Accepted 29 December 2014 Available online 31 January 2015
In the present, global and competitive market customer's demands are very volatile. It is very difficult for small and medium size enterprises (SMEs) to satisfy their customer with quality product in reasonable price. Large firms are spending huge amount of their budget in generating technology development. To cope with this competitive market, usually, SMEs concentrate on their core expertise and outsource some non-core items or activities. The outsourcing decision (when, what, whom) is very crucial for small-scale industries. Realizing this scenario, in this article, an automated self-adaptive multi-agent system has been proposed. The proposed system will help SMEs to take an appropriate decision to mitigate the uncertainty in supply chain. The developed system exploits the “Outsourcing of operations” feature by its agents to conclude the manufacturing processes faster and reduce the idle time of certain machines with less human intervention. The communication protocol for agents has been described to give an insight of their communication. It was explained how various agents like outsourcing, material planning and supplier selection will come into action at different stages and take appropriate outsourcing decisions. Therefore, this multi-agent architecture will facilitate small scale manufacturing industries to execute their manufacturing processes and complex logistics issues efficiently. The execution of proposed architecture has been described in the simulated case study. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Automated self-adaptive multi-agent system Manufacturing supply chain Outsourcing SMEs
1. Introduction SMEs are seen as a symbol of innovation and competition in various industrial sectors. The revenue generated by them accounts for 50% of GDP and their workforce accounts for 60% of employment in the developed countries [1]. However, they are considered as ‘underachiever’ when it comes to doing business abroad. The Foreign Direct Investment (FDI) associated with SMEs is marginal and their contribution to exports is only 30%. The main reasons for their limited success are lack of resources (financial [2], technical [3] and relevant market information [3]), insufficient government support [4] and inability to deal with global affairs (international prices, unavailability of foreign business stakeholders etc.) [5]. Moreover, their revenue has suffered a lot because of recession in past decade. Also, the modern customer is looking for quality products at lower prices. Then, there is a time constraint i.e. they have to deliver the products in a short notice of time. All these above factors, collectively have affected the sustainability of SMEs. Therefore, they are working on various n
Corresponding authors. E-mail address:
[email protected] (N. Mishra).
http://dx.doi.org/10.1016/j.rcim.2014.12.009 0736-5845/& 2015 Elsevier Ltd. All rights reserved.
measures to address it like cost cutting in terms of inputs, redundancy of staff etc. In the past, SMEs usually, buy only raw materials from outside and perform rest of the operations in-house. However, to compete in present market scenario, sometimes, they even outsource some of their operations. Outsourcing is contracting out some of the business activities such as manufacturing, maintenance, accounting or logistics to the third party. It usually helps company to focus on their core business issues while minimizing different costs associated with non-core activities. This outsourcing of certain operations to external industrial partners has significantly brought down the expenses incurred and the time taken in manufacturing a product. The main advantages of outsourcing activities are [6– 14]: (a) low manufacturing costs, (b) minimize uncertainty related to the risk-pooling effect, (c) less capital investment, (d) more emphasis on core competency, (e) better capacity adjustment, (f) enhanced flexibility, (g) better response to the customer's requirement, (h) Quality improvement by better use of supplier's technical knowledge, (i) better use of new technology of the contracting partner. However, these outsourcing activities make the entire process complex and will only be helpful when SMEs take appropriate decision regarding what to outsource, when to outsource and whom to outsource. In all these processes, the
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manufacturing plant has to interact with more than one industry and negotiate the price and terms and conditions of the deal. Keeping the same in mind, in this article, multi-agent architecture has been designed. The proposed multi-agent architecture will help industry to take appropriate decision with the help of different artificial intelligent techniques. Multi-agent system (MAS) originates from distributed artificial intelligence. MAS are defined as an assembly of intelligent, self-interested software entities known as agents who work collectively to solve complex problems with more precision and less time and human intervention. These agents are autonomous in nature as they are active entities, which are capable of self-decision-making. They are designed to make a decision to execute or not a requested task by considering its goals, priorities and the situation [15]. Agents for communication among themselves and with the human users deploy Agent Communication Language (ACL) [16]. MAS are also capable of intra firm and inter firm interactions [17]. In the literature, plethora of research has been conducted to address different issues related to outsourcing activity in manufacturing supply chain [18–28]. During the study, Zheng et al. [29] found that information sharing is a critical issue for different segments of distributed manufacturing. In order to address this issue, they suggested web based multi-function scheduling system. Knowledge and communication within different segment is an integral part of manufacturing supply chain [30]. Suggested a multi-agent architecture to establish the effective communication and coordination among the distributed global manufacturing networks [31–34] proposed a decentralized information system to establish the link between the global manufacturing networks. The goal was to address the issue related to due date of customer in distributed manufacturing supply chain [35]. Proposed genetic algorithm (GA) based approach to generate plans for single and distributed manufacturing systems [36]. Utilized the beauty of fuzzy rule and developed multi-agent architecture to address the issue related to the imprecise information and fault recovery in a distributed network [37]. Suggested a distributed multi-agent architecture for fault diagnosis system in artillery command system. To minimize the production cost and lateness of the customer order [38], proposed a multi-agent scheduling systems for a distributed manufacturing environment. Multi-agent mediator architecture for distributed manufacturing was proposed by [39]. In distributed architecture for knowledge sharing [40], proposed a multi-agent system (MAS) in an e-business domain [41]. Reviewed the development and use of a multi-agent modeling techniques and simulations in the context of manufacturing and supply chain management [42]. Conducted a study to find out the challenges and research opportunity related to application of multi-agent architecture in holonic manufacturing systems [43]. Highlighted the application of MAS in improving the coordination of manufacturing supply chains associated with major uncertainties [44]. Utilized multi-agent system in conjunction with auction based negotiation approach for distributed multi project scheduling [45]. Applied multi-agent system to production planning and scheduling framework for mobile robots [46]. Discusses the architecture of a framework, which is using RFID enabled multi-agent system for flexible manufacturing shop [47]. Presents an overview of using multi-agent system with bio inspired methods to solve complex problems of reconfigurable manufacturing systems [48]. Proposes a real time solution for an automotive supply chain to execute multi-level decision-making process in order to enhance their service levels and profits [49]. Implemented a web based tool to a precision engineering SME to improve their production planning and their consequent performance. In the literature, there are plenty of attempts being made to address various outsourcing issues of manufacturing supply chain. However, there is not enough research being done in outsourcing operations of manufacturing
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SMEs. Outsourcing a particular operation is a complicated decision and requires lots of human interference. In the past, researchers have applied various meta-heuristic techniques, mathematical modeling, multi criteria decision making etc. There was not a single method, which was single handedly able to address problems of different types in different scenarios. It makes this phenomenon more complex. In this paper, algorithm portfolio is utilized by multi-agent system, which is capable to select appropriate method or algorithm depending upon the situation. Therefore, this multi-agent system is automated in its decision-making. This article proposes self-adaptive multi-agent architecture to address this issue. The beauty of this automated system lies in the ability of precise multi-tasking of its agents. Outsourcing, material planning agent and supplier selection agent are responsible for outsourcing some of the internal operations of the manufacturing industry to the external industrial partners in order to bring down the manufacturing time and manufacturing price of the final product. Maintenance agent looks after the planning and execution of the maintenance policies for the machines in the manufacturing plant. Additionally, it can also make provision for outsourcing of the maintenance of certain machines to some external industrial partners according to the circumstances. Purchase order collection agent takes the order from the client and interacts with the planning agent to finalize the due date of delivery for the products. If there is a case where the client wants the delivery of the products earlier and is adamant in its approach leaving no room for negotiation then purchase order collection agent interacts with planning agent and outsourcing and material planning agent to outsource certain number of the internal operations of the manufacturing plant to external industries in order to meet the deadlines proposed by the client. These agents interact with each other using an agent communication language called Multiagent logic language for encoding Teamwork (MALLET).
2. Agent framework The proposed multi-agent architecture for outsourcing supply chain is presented in Fig. 1. It consists of purchase order collection agent, outsourcing and material planning agent, supplier selection agent, planning agent, knowledge base agent, maintenance agent, reconfiguration agent and forecasting agent. All these agents work in collaboration with each other. The properties of each agent are described below. 2.1. Purchase order collection agent This agent receives the order from the clients for the manufacturing unit. It confirms the due date for the delivery of the ordered products by interacting with planning Agent. If the client mentions a deadline and the planning agent (after looking at its manufacturing plan) says that it cannot meet the deadlines, looking at its capacity (number of machines available) and the current workload. Then, the purchase order collection agent tries to negotiate with the client. If the client is strict about the deadline then some of the operations are outsourced to an outsourcing partner by taking help of outsourcing and material planning agent and gets the product manufactured within the given time limit. Then, it decides the mode of payment whether it is cash, cheque, online banking, wires transfer, bank draft or paying in installments depending on its communication with administrative agent and hence confirms with the client. He further decides the way of delivery of the ordered products with the help of transport agent. It could be either through road transport using lorries, via railways, or using airfreight by reaching a consensus with the client. This agent is in continuous touch with the knowledge base agent
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Knowledge Base Forecasting agent
Purchase order collection agent
Supplier Selection agent and Transport agent
Knowledge Base Agent
Maintenance agent
Outsourcing and material collection agent
Planning agent
Administrative agent
Fig. 1. Proposed multi-agent architecture.
Purchase order collection _ agent (negotiate with client); If (client sticks to old due date) { Pass _ copy ( ) { Purchase order collection _ agent ( ) - 4 Outsourcing _agent ( ); } } } Pass _ copy ( ) { Administrative _ agent ( ) - 4 Purchase order collection _agent ( ); } Purchase order collection _ agent (decide mode of payment);
throughout its activities. This agent can update itself both online and offline. Below is the protocol used by purchase order collection agent to communicate with other agents. 2.1.1. Purchase order collection agent protocol This agent receives the order from the client like the quantity of products and due date for the products to be delivered. Then, it sends this information to Planning _ agent ( ). Then, Planning _ agent ( ) analyzes its manufacturing plan, it sends this information to Purchase order collection _ agent ( ). Then, Purchase order collection _ agent ( ) tries to negotiate with the client. If client sticks to old due date then it sends this information to Outsourcing _agent ( ). Then, it receives information from administrative _ agent ( ) regarding mode of payment. Finally, it decides the mode of payment. Purchase order collection _agent ( ) { Register _ order (Details of order, due date); Pass _ copy ( ) { Purchase order collection _ agent ( ) -4 Planning _ agent ( ); } Planning _ agent (Analyze manufacturing plan); If (due date cannot be met) { Pass _ copy ( ) { Planning _ agent ( ) -4 Purchase order collection _ agent ( ); }
}
2.2. Outsourcing and material planning agent This agent is in continuous communication with planning agent and forecasting agent. It has all the records of the available raw material within the manufacturing unit. The moment forecasting agent sends information about the future demand, it makes a decision about the different kinds of material and their respective quantities needed and sends this information to the supplier selection agent to look for the appropriate supplier to get it from. It also takes decision if a particular operation/part has to be done within the plant or outsourced and this decision will be based on the due date, expectations of the customer and available resources in plant. If he finds that particular operation/part has to
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be outsourced it looks for the outsourcing partner or if there is any accessory, which needs to be imported, in order to complete the product being manufactured. Below is the protocol used by outsourcing and material planning agent to communicate with other agents: 2.2.1. Outsourcing and material planning agent protocol It receives information from Forecasting _ agent ( ) regarding future demand. Then, it decides the amount of raw material and other inputs needed. Then, it sends this information to Supplier selection _ agent ( ). It receives information from Purchase order collection _ agent ( ) regarding outsourcing of certain operations. Finally, it makes a decision on what operations will be outsourced to which company. Outsourcing _agent ( ) { Pass _ copy ( ) { Forecasting _ agent ( ) - 4 Outsourcing _ agent ( ); } Outsourcing _ agent (makes decision on materials needed & their respective quantities); Pass _ copy ( ) { Outsourcing _ agent ( ) - 4 Supplier selection _ agent ( ); } Pass _ copy ( ) { Purchase order collection _ agent ( ) -4 Outsourcing _ agent ( ); } Outsourcing _ agent (makes decision about outsourcing of certain operation); }
2.3. Supplier selection agent The moment this agent gets information from outsourcing and material planning agent, it starts looking for the available suppliers of the raw material. It could use the database of knowledge base agent and look for the company records of past deals with various suppliers and check their feedback. It can have a look in catalogs (online and printed) of various reputed suppliers, supplier and commodity directories, trade journals and trade shows and various trade advertisements. It can use its professional contacts and networking or might look for the other capable suppliers on the internet. Supplier selection is done by evaluating different factors and following independent procedures. Firstly, it looks for the few major factors, which are listed below in order of their priority: a. b. c. d. e.
Availability Quality of the material being supplied Cost Reliability Distance of the supplier from manufacturing unit
Then, it also takes into account the technical expertise of the supplier by considering factors like design engineering capability, sustaining engineering capability, facilities and equipment used, output capacity, long term potential, flexibility to ramp up and
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down quickly and responsiveness, logistics expertise and track record of cost reductions. Finally, it looks at the financial and management aspects of the supplier. It reviews the supplier by their financial history, balance sheet, income statement and credit worthiness. It further looks at the efficiency of their executive and management team, management controls, information systems. Then, it checks their policy framing techniques like policies organization structure and decision making, alignment with the mission, corporate culture, values and goals of their corporate structure and their ability to integrate procurement systems. Supplier selection agent will use following protocol to communicate with other agents: 2.3.1. Supplier selection agent protocol It receives information regarding the quantity of raw material and other inputs needed from Outsourcing _ agent ( ). Then, it uses information form Knowledge base _ agent ( ) to review all the available suppliers. Finally, it decides the appropriate supplier. Supplier selection _agent ( ) { Pass _ copy ( ) { Outsourcing _ agent ( ) -4 Supplier selection _ agent ( ); Knowledge base _ agent ( ) - 4 Supplier selection _ agent ( ); } Supplier selection _ agent (decides the appropriate supplier); }
2.4. Planning agent This agent deals with the planning and organization of various operations in manufacturing unit. It generates the manufacturing plan, which includes when the products will be manufactured, their quality and the methods used. This agent depicts which machine will be used and when it will come into operation in order to manufacture a particular product. This agent is continuously in touch with the maintenance agent. It is done in a way to minimize the idle time for the machines. These days, the manufacturing lines are flexible in nature. It also decides the storage for the raw material and other materials needed for the manufacturing. Furthermore, it chooses the optimum place to store the raw materials considering the minimum logistics and storage expenses. It is also responsible for the easy availability of raw materials to the machines in the manufacturing unit and make sure that exact amount of raw material is available in the manufacturing unit as excess or deficiency of the material can affect the efficiency of the manufacturing unit. As soon as the new order arrives, the planning agent takes appropriate manufacturing decision by employing artificial intelligence technique such as genetic algorithm (GA), simulated annealing (SA), tabu search (TS), ant colony optimization (ACO) and bee colony optimization (BCO). However, in literature, it was observed that no random search algorithm exists that can be regarded as best suited strategy for a particular type of problem. Selection of the algorithm depends on nature of the problem. Keeping the same in mind, in this article, planning agent uses algorithm portfolio concept [50]. Algorithm portfolio is a collection of different algorithms and/or different copies of the same algorithm running on different processor. The portfolio algorithm is
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exploited through combining the algorithms and analyzing their performances on multiple processors as well as on a single processor. The main task of the portfolio is to choose the algorithms that lead to a high level of performance on a given problem instance, that is, to bring out near-optimal solutions in stipulated time frame. This leads to minimization of the computational cost, increase in the diverse problem solving capability, and maintenance of the quality from the best. The algorithm portfolio system will start working as soon as the new problem instance arrives and will be characterized by allocating a time limit to provide the solution. Thus, initially, the allocator will assign an experimentation time to various algorithm agents and will decide the priority order of the algorithm agents. The data collected during the experimentation from each algorithm agent will be fetched to the selector agent on the basis of whom it decides the algorithms to be bunched in a portfolio to solve the stage problem at hand. These algorithms are run for over a number of iterations. At the end of iteration, the control agent will assign improved knowledge to each algorithm agent. Thus, rather than making a single decision about the final selection of an algorithm agent for the remaining time, the selector will revisit its choice over and over. Planning agent will use following protocol to communicate with other agents: 2.4.1. Planning agent protocol This agent receives information from Maintenance _ agent ( ) regarding maintenance schedule. Then, it receives information from Purchase order collection _ agent ( ) regarding the new order. Then, it generates the manufacturing plan. Planning _agent ( ) { Pass _ copy ( ) { Maintenance _ agent ( ) -4 Planning _ agent ( ); Purchase order collection _ agent ( ) -4 Planning _ agent ( ); }
Teamwork knowledge in MALLET
Planning _ agent (generates the manufacturing plan); }
2.5. Knowledge base agent This agent is connected with all the other agents such as outsourcing and material planning agent, supplier selection agent, planning agent, forecasting agent and maintenance agent. This agent stores all the information related to the manufacturing processes. It is done by collecting information from all the other agents and monitoring their activities. It holds the information about the current status of components being processed in order to manufacture a product, the order in which they are processed and their processing times. Furthermore, it stores the information regarding the successful and unsuccessful decisions made by other agents. It helps so that the mistakes committed in the past are not repeated. This agent can update itself both online and offline. 2.6. Forecasting agent This agent decides the amount of raw material to be ordered and pass this information to purchase order collection agent, outsourcing and material planning agent and supplier selection agent. This decision is taken in accordance with the market fluctuations prevailing at that moment of time. Forecasting agent consists of high performance tools for forecasting and estimating the quantitative and qualitative demands of the current trends of the market. The main objective of this agent is to minimize the error in the estimated demand pattern by considering the market fluctuations. This agent utilizes a vast set of judgmental and mathematical tools depending upon the nature of forecasting data available. It is a very complicated job to choose the best method for forecasting the demand pattern. In the literature, there are plenty of methods available for judgmental and mathematical forecasting methods such as Unaided judgment, Prediction markets, Delphi, Structured analogies, Game theory, Judgmental Decomposition, Judgmental bootstrapping,
Domain knowledge
MALLET Parser
Individual Mental model
Share team Knowledge
Process Tracking
Cast Reasoning Engine
Goal Management
Fig. 2. CAST architecture [38].
Proactive behaviours
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Table 1 Processing time and available machines. Part no Operation number Processing/outsourcing unit 1
1 2 3 4
2
5 6 7 8
3
9
10 11 12
4
13 14 15 16
5
17 18 19 20
Processing time
M1 M2 M2 M3 M2 M4 M5
5 3 7 6 3 3 4
M1 M2 M3 M3 M4 M2 M5
7 4 6 7 7 4 10
M1 M2 M3 M4 M4 M5 M1 M5
4 5 8 5 6 5 4 4
M2 M3 M3 M3 M4 M2 M4 M5
2 6 8 3 8 6 7 4
M1 M3 M3 M4 M5 M1 M5
3 5 7 9 6 6 3
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and Segmentation. Choosing the best forecasting method for any particular situations is a very tough task and sometimes more than one method may be appropriate. In order to choose the best forecasting method, forecasting agent adopts a decision-making methodology. For example, in a particular situation if the agent decides to go ahead with the quantitative methods, the selection of the appropriate method will be based on the intercession tree. Forecasting agent will use following protocol to communicate with other agents: 2.6.1. Forecasting agent protocol This agent decides the amount of raw materials to be ordered. Then, it sends this information to Purchase order collection _ agent ( ), Outsourcing _ agent ( ) and Supplier selection _ agent ( ) using Pass _ copy ( ) function. Forecasting _agent ( ) { Forecasting _agent (decides the amount of raw material to be ordered); Pass _ copy ( ) { Forecasting _ agent ( ) - 4 Purchase order collection _ agent ( ); Forecasting _ agent ( ) - 4 Outsourcing _ agent ( ); Forecasting _ agent ( ) - 4 Supplier selection _ agent ( ); } }
2.7. Maintenance agent
Expert systems, Simulated interaction, Intentions and expectations surveys, Conjoint analysis, Extrapolation, Quantitative analogies, Rule-based forecasting, Neural nets, Data mining, Casual model
It utilizes the sensor in the machines to monitor their maintenance requirements, breakdown status, idle or in process stages etc. There are various maintenance policies being followed in the manufacturing sector having their respective advantages and shortcomings. This agent will prioritize the sequence of machines, which needs maintenance or repair. It will also take decision about whether a machine can be repaired or it needs to be replaced. It will prepare a maintenance schedule in a way so that there will be
Table 2 Precedent relationship between operations.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
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Fig. 3. Final manufacturing plan suggested by planning agent.
minimum alterations in the working of other agents. It will further make a decision about the maintenance of machine whether it will be done in the premises or it needs to be outsourced. It maintains all the records such as information regarding repair date, warranty/ guarantee of the machines. Furthermore, it has the information regarding which machines are dependent on other machines. It will communicate with the knowledge base agent for selecting the new machines. It keeps the record of the spare parts, necessary tools, lubricating oils etc. that needs to be ordered for the respective maintenance of the machines. The Maintenance Agent is responsible for monitoring the maintenance requirements of conveyances and therefore has the ability to both access appropriate data sources and to monitor the operational state of conveyances and high value loading facilities through the interpretation of sensor data. Maintenance agent will use following protocol to communicate with other agents: 2.7.1. Maintenance agent protocol This agent prepares Maintenance schedule of all the machines and passes this information to Planning agent. Maintenance _agent ( ) { Maintenance _agent (prepares maintenance schedule); Pass _ copy ( ) { Maintenance _ agent ( ) - 4 Planning _ agent ( ); } }
{ Administrative _agent (monitors operations of other agents); Administrative _agent (maintains all accounts & overheads); Pass _ copy ( ) { Administrative _ agent ( ) -4 Purchase order collection _ agent ( ); } }
2.9. Communication ontology In the proposed agent architecture, an agent can co-operate with other agent by sending help signal. Help signal can only be responded by agents who come in perceptory region of the agent who has sent signal. Mathematically, ‘help’ signal Hit send by ith agent for collaborative help in task ‘t’ is defined as
Hit = (ai , tt )
∀ APR i
where APRi represents the set of agents within the perception range (PR) of agent ‘i’ and is defined as
{
APR i = ai ∈ a/aiPR i
}
∃ APR i ∈ a, APR i ∧ ¬ (ai )
a jPR < dij < PRi
2.8.1. Administrative agent protocol This agent monitor the operations of all other agents and monitors all the accounts & overheads. Then, it passes the information regarding mode of payment to Purchase order collection agent ( ). Administrative _agent ( )
(2)
(3)
and a jPRi represents the ‘j’ agent lying in the perceptory range of agent ‘i’ and is defined as
2.8. Administrative agent This agent will make sure that all the operations performed by various agents are within the legal boundaries. It will take care of agreements, warranties, guaranties, transport security and other health and safety measures. It will maintain all the accounts like salaries of employees, payments of suppliers, customer's payments and other overheads. Below is the protocols used by administrative agent to communicate with other agents:
(1)
(4)
dii represents the distance between the agents i and j. In this architecture, Manhattan distance has been taken into account because of its effectiveness, compared to Euclidean distance in parallel computing scenario [51,52]. Upon receiving a ‘help’ signal form agent i, agent j send a ‘reply’ signal r ij defined as
r ij = (a j , ai , Hit )
(5)
2.10. Communication channel The performance of multi-agent architecture depends on precise communication between the agents. In distributed manufacturing multi-agent system, role of communication channel gets intensified. The agents send signals to communicate among themselves. As soon as agents receive signals, they take
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appropriate action. In literature, for effective communication between agents, plethora of communication channel has been developed like Knowledge Query Manipulation Language (KQML), Agent Communication Language etc. One of the most advanced and widely used languages is Multi-Agent Logic Language for Encoding Teamwork (MALLET). MALLET uses team oriented programming for communication. By using sequential and iterative processes, MALLET helps in smooth information flow in the system and accurate encoding of information. This type of iterative processes could be procedural or declarative. In the proposed architecture agents will use MALLET communication channel to facilitate effective and precise communication among the agents. Collaborative Agents for Simulating Teamwork (CAST) act as an interpreter of MALLET. The schematic diagram of CAST is shown in Fig. 2. The salient features of Communication channel are briefly described below: 1. Precise – The communication language used in the framework must be clear, accurate and expressive. 2. Interpretability – The communication language used in the framework must be easily decoded or interpreted by the agents. 3. Multiple usage – The communication language used in the framework must have the multiple usage characteristic. It will facilitate the retrieval of stored information infinite times thereby saving cost to company and effort. Agents predefine their tasks, abilities and work plans prior to initiating their communication. Then, they integrate their tasks with other agents according to their condition. The latter agent then sends its prerequisite like required information and knowledge to the former agent. The communication among the agents can be Parallel (PAR), Iterative (WHILE, FOR), Sequential (SEQ), Conditional (IF) or Choice specific (CHOICE). The agent starts the execution of its task only when its pre requisites are fulfilled. If, somehow, a particular agent fails to fulfill its pre requisites, other agents provide assistance to it and they help each other. The next section consists of a case study of a SME who is employing the proposed architecture to improve their productivity.
3. Case study A medium size manufacturing industry is being considered to demonstrate the execution process of proposed architecture. As soon as new customer has arrived, purchase order collection agent collects all information related to the order like design of the product, its specification, due date etc. Now he communicates with all other agents such as supplier selection agent, planning agent, outsourcing and material ordering agent, maintenance agent to know the current status of the plan. On the basis of the collected information such as raw material stock, machine availability, current orders in process, purchase order collection agent estimates the due date for the new order. If the industry is not able to meet the due date of his new customer, purchase order collection agent try to negotiate with the customer on due date. As soon as order is finalized, purchase order collection agent pass all the information related to order to all appropriate agents. Thereafter, outsourcing agent decides the production policies like deciding if all the operations would be done within the plant or few of them needs to be outsourced. Similarly, material ordering agent, looking at existing stock decides how, when, what has to be ordered keeping in touch with forecasting agent. When all these are decided, supplier selection agent selects appropriate partner or suppliers of the raw material. In all these processes, agents consult
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knowledge base agent to collect information related to the similar deals in the past. The history of any deal with the selected supplier is being checked in its database and if the feedback is positive then the deal is being finalized. Now, planning agent plans the entire process of manufacturing. In this process, planning agent uses algorithm portfolio for manufacturing planning, scheduling and execution. After manufacturing, the order is delivered to the customer. All these processes and the decision made is being tracked and stored in the knowledge base agent for future reference. In this case study, company got two orders from different customers. Each order consisted of manufacturing of 5 parts. In order to manufacture the products, one of the operations needs to be outsourced which cannot be performed in the manufacturing unit. So, the outsourcing agent decided to outsource one of the operations. Detailed information related to the order such as due date, precedent relationship and type of operation is given in Tables 1 and 2. The final operation sequence generated by planning agent using algorithm portfolio is mentioned in Fig. 3. The final outcome shows the efficacy and the execution process of the proposed architecture.
4. Conclusion In present scenario, small and medium size manufacturing industries are seriously affected from global recession. To survive in this competitive market, they need to produce high quality of products in minimum cost. Concentrating on their core expertise will help them to achieve the above objective. Usually, SMEs outsource some of their operations or services to third party. Outsourcing decision is very crucial. Keeping the same in mind, in this article a multi-agent architecture framework has been proposed. The proposed framework is flexible in nature and can be modified as per the requirements of manufacturing industries. The proposed architecture is self-adaptive in nature and will help SMEs to negotiate with customer on due date. It will also automatically decide on outsourcing policy and supplier selection. The multi-agent architecture existing in the literature is usually not capable of acting alone and requires user's interference. The requirement of user interference is the biggest hurdle for SMEs to adopt the multi-agent architecture for their business. In the proposed architecture, the agents independently communicate among themselves without any user interference and automatically develop new skills to accommodate new changes using different artificial intelligent techniques. For particular scenario, selection of an appropriate artificial technique is a decisive decision. In the proposed agent architecture, the beauty of algorithm portfolio has been utilized for selection of appropriate planning algorithm. In the recommended system, maintenance agent is also capable of taking independent decision on outsourcing the maintenance operations of the machines if they cannot be done cheaply and conveniently within the manufacturing plant without any external user interference. The selection of the correct forecasting method usually depends on problem environment. In literature, agent architecture used only one method for forecasting. However, in this agent architecture, more than one forecasting method has been used and the agent automatically selects a particular method for a specific problem. In short, with the help of proposed architecture, SMEs will be capable of taking their decision with less user interference. In future, some more decision-making parameters will be taken into account while choosing an outsourcing partner apart from price, deadline and capacity of plant. Additional meta-heuristic algorithms will be included in the algorithm portfolio and the performance of the Multi-agent system will be compared by implementing it to a real world problem.
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