198
A Review of Supply Chain Management using Multi-Agent System
Vivek Kumar1 and Dr. S. Srinivasan2
1
Research Scholar, Department of computer Science & Engineering, Suresh Gyan Vihar, University
Jaipur, Rajasthan 302 004, India
2
Professor & Head, Computer Science Department
PDM Engineering College,
Bhadurgarh, Haryana 124 507, India
Abstract
Supply chain consist of various components/ identities like
supplier, manufacturer, factories, warehouses, distributions
agents etc. These identities are involved for supplying raw
materials, components which reassembles in factory to produce a
finished product. With the increasing importance of computer-based communication technologies, communication networks are
becoming crucial in supply chain management. Given the
objectives of the supply chain: to have the right products in the
right quantities, at the right place, at the right moment and at
minimal cost, supply chain management is situated at the
intersection of different professional sectors. This is particularly
the case in construction, since building needs for its fabrication
the incorporation of a number of industrial products. This paper
focuses on an ongoing development and research activities of
MAS (Multi Agent System) for supply chain management and
provides a review of the main approaches to supply chain
communications as used mainly in manufacturing industries.
KEYWORDS: Information exchanges, Multi Agent System, knowledge sharing and supply chain management
1. Introduction
Supply chain is a worldwide network of suppliers, factories,
warehouses, distribution centers, and retailers through which raw
materials are acquired, transformed, and delivered to customers.
In recent years, new software architecture for managing the
supply chain at the tactical and operational levels has emerged. It views the supply chain as composed of a set of intelligent
software agents, each responsible for one or more activities in
the supply chain and each interacting with other agents in the planning and execution of their responsibilities. Supply Chain
Management is the most effective approach to optimize working
capital levels, streamline accounts receivable processes, and
eliminate excess costs linked to payments.
2. Literature Survey
Analysts estimate that such efforts can improve working Capital
levels, streamline accounts receivable processes, and eliminate excess costs linked to payments. Analysts estimate that such
efforts can improve working capital levels by 25% [2]. Today, the best companies in a broad range of industries are implementing supply chain management solutions to improve business performance and free cash resources for growth and innovation. Supply Chain Management is about managing the physical flow of product and related flows of information from
purchasing through production, distribution and Delivery of the
finished product to the customer. This requires thinking beyond the established boundaries, strengthening the linkages between the supply chain functions and finding ways to pull them together. The result is an organization that provides a better service at a lower cost. MihaelaUlieru et al. give a approach based on the holonic enterprise model [10] with the Foundation for Intelligent Physical Agents (FIPA) Contract Net protocols applied within different levels of the supply chain. The negotiation on prices is made possible by the implementation of an XML rule-based system that is also flexible in terms of configuration. According to Pericles A., the system is viewed as an organization or collection of roles that relate to each other and form an interaction model. Roles in the system are descriptions of business entities, whose functions are modeled at an abstract level. Whole system is divided in Business Description, Product Description, and Order Management Holarchy, Manufacturing Holarchy.ole Modeling. Author has given the following System JADE JADEContentClasses Content CodecOntology Customer Agent Content : ContentManager Content_Language: Codec Setup(addBehaviour() ) Concept Request_BehavioPredicateConcept : Concept Predicates : Predicate AgentAction : AgentAction Action Action()
Fig. 1 Class Architecture for customer Agents
IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010 199
ISSN (Online): 1694-0814 www.IJCSI.org
Yevgeniya Kovalchuk presented a way to manage the supply chain activities & try to automate their business processes [18]. In practice, all the activities are highly connected and interdependent. The project is mainly focused on the demand part of the supply chain. In particular, different methods for predicting customer offer prices that could result in customer orders are explored and compared in the system. RuiCarvalho et al. presented multi-agent technology as a sound alternative to classical optimization techniques that can contribute to solve hard problems. To prove this point , the MAS with the following functionalities designed : simulation of an almost infinite number of agents, heuristics for decision making, possibility to choose among alternative decision strategies and tactics, different evaluation criteria and evaluation functions, different message sequences, and stochastic or deterministic behavior. Monolayer information sharing SupplieManufacturer Distributor Retailer Monolayer information sharing SupplieManufacturer Distributor Retailer Fig. 2 Compartmentalization of operational information coordination
José Alberto R. P. Sardinha1 et al. presented a flexible architecture based on a distributed multi-agent architecture [15] of a dynamic supply chain. Intelligent agents tackle sub problems of a dynamic SCM. Authors present an implementation of this architecture by using international test bed for SCM solutions. The mail purpose of this work is to present a multi-agent architecture for a generic supply chain that uses a direct sales model, which links customers directly to a manufacturer through the Internet. Robert de Souza et al. addressed two main issues [6]: Can we chart the complex logistical dynamics of disk drive manufacturing? What are the critical success factors that impact the economics of hard disk drive manufacturing? The backdrop for this study is the (global) supply chain much in evidence in disk drive manufacture. Fu-ren Lin et al. analyzed the impact of various levels of information sharing including order, inventory, and demand information, which is based on transaction costs [14]. This study further examines the effects on supply chain performance in electronic commerce. Specifically, the multi agent simulation system Swarm is employed to simulate and analyze the buyer–seller correlation in sharing information among business partners in supply chains. Information sharing between firms helps to lower the total cost and increase the order fulfillment rate. In other words, information sharing may reduce the demand uncertainty that firms normally encounter. Onn Shehory et al. discussed suitability of agent modeling techniques [4] to agent-based systems development. In evaluating existing modeling techniques, addressed criteria from software engineering as well as characteristics of agent-based systems. Evaluation shows that some aspects of modeling techniques for agent-based systems may benefit from further enhancements.
This technique tries to answer the following questions: (1) which agent-based system characteristics and software engineering principles are addressed within AOSE modeling techniques, and to what extent? (2) What should be the properties of the future agent-oriented modeling techniques? Rasoul Karimi et al. developed a new multi attributes procurement auction [11]. It is new because it has been defined for a special model of supply chain, in that customer has a new scoring rule, and producers have new strategies for biding. Multi Attribute Procurement Auction is a good solution for Supply Chain problem which fits its requirements. The implementation of the Swarm simulation system incorporates multiple agents with the decision model to, in turn; determine the relationship with their trading partners. Fig. 3 demonstrates a purchasing agent’s decision model to determine which supplier should issue the purchase order. OFP Observer Swarm Statistics Swarm OFP Model Swarm Production Agent Production Planning Agent Cost Management Inventory Agent Management Work Flow AgentOrder Management Fig. 3 Swarm implementation for modeling supply chains.
A trading partner contains several agents, including order management, inventory management, policy management, production, production planning, and purchasing. Among them, the purchasing agent proposes the decision model to determine from which supplier products should be purchased. The purchasing agent buys goods from suppliers that offer the lowest price. The price issued by a supplier is the outcome of weighing production cost and coordination cost. The final price is the one issued by a supplier. Yang Hang et al. proposed a CSET framework [13] for whole supply chain in collaborative manner by incorporating it with the Just-in-Time (JIT) principle, known as CSET. The core of the CSET model is based on intelligent agent technology. This paper defines such double-agent mechanisms in details, as well as demonstrating its merits via simulation study. The MAS proposed here was implemented using LISP and had as first source of inspiration the agent creation language named RUBA [Ventura97]. The system has as main blocks i) an environment agent, in charge of the meaningful functioning of the system and event execution simulation [Michael90], ii) client agents, with needs to be satisfied and iii) firm agents, that have also needs but are capable of product manufacturing. The system also includes a blackboard, where agents can post their messages, and a set of communication rules (a communication protocol inspired on the Contract Net protocol
IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010 200
ISSN (Online): 1694-0814 www.IJCSI.org
[Smith80; Smith81]), common to all agents and that makes possible message exchange. KQML [Finin94], a standard message format, and KIF [Genesereth94], a message content specification, served as the basis for the communication protocol of our MAS [Huns99].The main elements of the system, agents, blackboard and a communication protocol, are essential for functioning. These agents are intelligent, because they are able to present successful behavior [Albus91]. Figure 4 shows the system behaviour. Fu-ren Lin et al. used multiagent simulation system, swarm, for simulating trust mechanism and analyzing the supply chain performance in four different market environments [19].Supply chain performance is evaluated by comparing the order fulfillment process of a mold industry both with and without trust mechanisms. From the experimental result ,
Creates Problem Information Warehouse Blackboard ------ Client Agent Factory Agent ----- Fig 4. Environment Agent and Agent Behavior
they found that the trust mechanism reduced the average cycle time rate and raised the in-time order fulfillment rate as the premium paying for better quality and shorter cycle time. Charles M. Macal et al. gave a new approach [5] to modeling systems comprised of interacting autonomous agents. & described the foundations of ABMS, identifies ABMS toolkits and development methods illustrated through a supply chain example, and provides thoughts on the appropriate contexts for ABMS versus conventional modeling techniques. William E. Walsh et al. highlighted some issue that must be understood to make progress in modeling supply chain formation [3]. Described some difficulties that arise from resource contention. They suggested that market-based approaches can be effective in solving them. Mario Verdicchio et al. considered commitment as a concept [17] that underlies the whole multi-agent environment, that is, an inter-agent state, react a business relation between two companies that make themselves represented by software agents. Michael N. Huhns et al. found after this research that supply chain problems cost companies [8] between 9 to 20 percent of their value over a six month period. The problems range from part shortages to poorly utilized plant capacity. Qing Zhang et al. provide a review of coordination of operational information in supply chain [12] . Then the essentials for information coordination are indicated.Vivek Kumar et al. gave a solution for the construction, architecture, coordination and designing of agents. This paper integrates bilateral negotiation, Order monitoring system and Production Planning and Scheduling multiagent system. Ali Fuat- Guneri et al gave the concept of supply chain management process[16], in which the firm select best supplier , takes the competitive advantage to other companies. As supplier selection is an important issue and with the multiple criteria decision making approach, the supplier selection problem includes both tangible and intangible factors.
The aim of this paper is to present an integrated fuzzy and linear programming approach to the problem. Firstly, linguistic values expressed in trapezoidal fuzzy numbers are applied to assess weights and ratings of supplier selection criteria. Then a hierarchy multiple model based on fuzzy set theory is expressed and fuzzy positive and negative ideal solutions are used to find each supplier’s closeness coefficient. Finally, a linear programming model based on the coefficients of suppliers, buyer’s budgeting, suppliers’ quality and capacity constraints is developed and order quantities assigned to each supplier according to the linear programming model. Amor et al. presented Malaca [9], an agent architecture that combines the use of Component- based Software Engineering and Aspect-Oriented Software Development.
Message Return Handles Aspect < Malaca supports the separate (re)use of the domain-specific functionality of an agent from other communication concerns, providing explicit support for the design and configuration of agent architectures and allows the development of agent-based software so that it is easy to understand, maintain and reuse. Ka-Chi Lam et al. investigated a selection model based on Fuzzy Principal Component Analysis (PCA) [7] for solving the material supplier selection problem from the perspective of property developers. First, the Triangular Fuzzy Numbers is used to quantify the decision makers' subjective judgments. Second, PCA is employed to compress the data of the selection criteria IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010 201 ISSN (Online): 1694-0814 www.IJCSI.org and eliminating the multi-collinearity among them. Third, the linear combined score of PCA (SCOREPCA) is used to rank the Suppliers. Unceatin Values (in 5 point sale DMs’ importance wrights Supplies’ ratings Uncertain VB/VL P/L M G/VG/V HH Fuzzy refraction 0 0.25 Fig. 6. Membership functions of DMs' importance weights and suppliers'ratings (modified from) Four material purchases are used to validate the proposed selection model.The results show that the proposed model can be adopted in construction material supplier selection by the property developers. Table 1: Summary S.No. Title Name & Explanation & Conclusion Authors 1. “supply Chain This paper seeks to address two Models in Hard main issues: Can we chart the Disk Drive complex logistical dynamics of Manufacturing” disk drive manufacturing? What are the critical success factors that impact the economics of hard disk Robert de Souza drive manufacturing? and Heng Poh Khong The pressures in the disk drive industry are classic supply chain economics; value, timing, supply, demand and technology development that all play a part into price erosion patterns. To address such issues the authors' postulate that the five chains interact to give rise to complexities, static models cannot easily handle. 2. Modeling supply In this paper the authors highlight chain Formation some issues that must be in Multiagent understood to make progress in System molding supply chain formation. William E. Supply chain formation is an Walsh and important problem in the Michael P. commercial world and can be Wellman improved by greater automated support. The problem is salient to the MAS community and deserving of continued research. 3. Evaluation of Author discusses suitability of Modeling agent modeling techniques to Techniques for agent-based systems development. Agent-Based In evaluating existing modeling Systems techniques, and address criteria from software engineering as well as characteristics of agent-based Onn Shehory systems. and Arnon Sturm Based on these findings, we intend in future research, to address the needs of agent-based system developers. This should be done in order to find the required modeling techniques and components for building agent-based systems. 4. Effects of Findings indicate that the more Information detailed information shared Sharing on between firms, the lower the total Supply Chain cost, the higher and the order Performance in fulfillment rate. And the shorter Electronic the order cycle time. In other Commerce words, information sharing may reduce the demand uncertainty that firms normally encounter. Fu-ren Lin, Firms that share information Sheng-hsiu between trading partners tend to Huang, and transact with a reduced suppliers. Sheng-cheng Lin This work investigated the buyer–seller relationship in electronic commerce with an Extranet as the platform for sharing information. Using the Swarm simulation system, based on transaction costs, we have identified effects of sharing various levels of information between supply chain partners. IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010 202 ISSN (Online): 1694-0814 www.IJCSI.org 5. Commitments As there are several analogies for Agent-Based between a company in a business Supply Chain network and an agent, the Multi-Management Agent System paradigm can be a valid approach for modeling supply chain networks. We Mario consider commitment as a concept Verdicchio and that underlies the whole multi-Marco agent environment, that is, an Colombetti inter-agent state, reacting a 6. Building Approach is based on the holonic Holonic Supply enterprise model with the Chain Foundation for Intelligent Management Physical Agents (FIPA) Contract Systems: An e-Net protocols applied within Logistics different levels of the supply Application for chain holarchy. To accommodate the Telephone differentiation of interests and Manufacturing provide an allocation of resources Industry throughout the supply chain holarchy, we use nested protocols as interaction mechanisms among MihaelaUlieru agents. Agents are interacting and through a price system embedded MirceaCobzaru into specific protocols. The negotiation on prices is made possible by the implementation of an XML rule-based system that is also flexible in terms of configuration and can provide portable data across networks. As the effectiveness of centralized command and control in SCM starts to be questioned, there is a critical need to organize supply chain systems in a decentralized and outsourced manner. Agent-based models can easily be distributed across a network due to their modular nature. Therefore, the distribution of decision-making and execution capabilities to achieve system decentralization is possible through models of operation with communication among them. The ontology structure of the JADE framework is, in our opinion, one of the best designed to address the issues of accessing and sharing information pertinent to a specific application. 7. A Multiagent It was modelled and implemented Systems a MAS with the following Approach for functionalities: simulation of an Managing almost infinite number of agents, Supply-Chain heuristics for decision making, Problems: new possibility to choose among tools and results alternative decision strategies and tactics, different evaluation criteria and evaluation functions, Rui Carvalho, different message sequences, and Luís Custódio stochastic or deterministic behavior. When we applied our MAS to a problem of SC management at HP, we obtained results with stock outs for every product of the bill of materials. On the contrary, some authors using mathematical tools only simulated the stock out of only one product of the bill of materials. 8. A Multi-Agent This paper presents a flexible Architecture for architecture for dealing with the a Dynamic next generation of SCM problems, Supply Chain based on a distributed multi-agent Management architecture of a dynamic supply chain. We define intelligent agent roles that tackle sub problems of a José Alberto R. dynamic SCM. P. Sardinha1, We also present an Marco S. implementation of this Molinaro2, architecture used in the Patrick M. international test bed for SCM Paranhos2, solutions, the Trading Agent Pedro M. SCM competition, as well as some Cunha2, experimental results. Ruy L. Milidiú2, Carlos J. P. de Lucena2 A multi-agent design is used in the architecture, because we believe it facilitates the development of modular entities that are distributed and reusable. The design was also used to implement an agent entry for the Trading Agent Competition. This system competed against 32 entries, and was able to classify to the quarter-finals of the 2005 competition. 9. How to Model Agent-based modeling and With Agents simulation (ABMS) is a new Proceedings of approach to modeling systems the 2006 Winter comprised of interacting Simulation autonomous agents. ABMS Conference promises to have far-reaching effects on the way that businesses use computers to support Charles M. decision-making. Macal and Michael J. North Computational advances make possible a growing number of agent-based applications across IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010 203 ISSN (Online): 1694-0814 www.IJCSI.org many fields. Applications range from modeling agent behavior in the stock market and supply chains 10. New Multi In this article, this constraint has Attributes been relaxed and a new Procurement procurement auction is defined. In Auction for this auction, seller agents can take Agent- Based different strategies based on their Supply Chain risk attribute. These strategies is Formation analyzed and compared mathematically. Rasoul Karimi, Authors define a new MAPA Caro Lucas and which is usable under the new Behzad Moshiri model of supply chain. In this MAPA, the producer could have two different strategies based on its risk attribute. These two strategies are compared mathematically and also in a simulation. 11. Multi-Agent The research approach followed is Decision presented. The results achieved so Support System far along with the plans for future for Supply work are given next. Chain Management Various techniques for predicting bidding prices in the context of Yevgeniya dynamic competitive Kovalchuk environments are explored. Apart from the SCM, the solutions can be used in forecasting financial markets and participating in on-line auctions. 12. Double-agent The model is supported by Architecture for double-agent architecture with Collaborative each type of agents who makes Supply Chain provisional plans of order Formation distribution by Pareto optimality and JIT coordination respectively Yang Hang and Simon Fong As a result, pipelining manufacturing flow is achieved. This is significant to dynamic supply chain formation as it can help to optimize constraints and costs across production, distribution, inventory, and transportation. 13, Essentials for Provide a review of coordination Information of operational information in Coordination in supply chain which is classified Supply Chain into information types, their Systems impact on supply chain performance, and the policy of information sharing Qing Zhang and Wuhan Multi-agent computational environments are suitable for studying classes of coordination issues involving multiple autonomous or semi-autonomous optimizing agents where knowledge is distributed and agents communicate through messages. 14. Effects of Trust The multiagent simulation system Mechanisms on Swarm is employed to simulate Supply Chain and analyze the buyer–seller Performance correlation in sharing information Using Multi-among business partners in supply agent Simulation chains and Analysis The deeper the information Fu-ren Lin ,Yu-sharing level, the higher in-time wei Song and order fulfillment rate and the Yi-peng Lo shorter order cycle time, as information sharing may reduce the demand uncertainty that firms normally encounter. Finally, firms that share information between trading partners tend to transact with a reduced set of suppliers. 15. A Multiagent Paper present solution for the Conceptualizatioconstruction, architecture, n For Supply-coordination and designing of Chain agents. This paper integrates Management bilateral negotiation, Order monitoring system and Production Planning and Scheduling Vivek kumar , multiagent System. Amit Kumar Goel , Prof. S.Srinivisan The wide adoption of the Internet as an open environment and the increasing popularity of machine independent programming languages, such as Java, make the widespread adoption of multi-agent technology a feasible goal 16. An integrated A hierarchy multiple model based fuzzy-lp on fuzzy set theory is expressed approach for a and fuzzy positive and negative supplier ideal solutions are used to find selection each supplier’s closeness problem in coefficient. Finally, a linear supply chain programming model based on the management coefficients of suppliers, buyer’s budgeting, suppliers’ quality and capacity constraints is developed Ali Fuat Guneri, and order quantities assigned to IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010 ISSN (Online): 1694-0814 www.IJCSI.org 204 Atakan Yucel , each supplier according to the linear programming model. Gokhan References Ayyildiz [1] Ali Fuat Guneri, Atakan Yucel , Gokhan Ayyildiz “An integrated fuzzy-lp approach for a supplier selection problem in Fuzzy set theory approach helps supply chain management” Expert Systems with Applications 36 to convert decision-makers’ (2009) 9223–9228 experience to meaningful results by applying linguistic values to assess each criterion and [2] C. Iglesias, M. Garijo, J. Centeno-Gonzalez, and V. J. R., \"Analysis and Design of Multiagent Systems using MAS-alternative suppliers. An agent architecture that CommonKADS,\" presented at Agent Theories, Architectures, 17. Malaca: A component and combines the use of Component- aspect-oriented based Software Engineering and agent Aspect-Oriented Software architecture” Development Information and Software Provided explicit support for the Technology design and configuration of agent architectures and allows the Mercedes Amor development of agent-based *, Lidia Fuentes software 18. A material Tthe Triangular Fuzzy Numbers is supplier used to quantify the decision selection model makers' subjective judgments. for property Second, PCA is employed to developers using compress the data of the selection Fuzzy Principal criteria and eliminating the Component multicollinearity among them. Analysis” Automation in Construction The model can efficiently eliminate the multicollinearity among the supplier's attributes Ka-Chi Lam , and help to reduce the trade-offs Ran Tao, Mike and repeatability errors in the Chun-Kit Lam selection process.and the proposed selection model can also reduce the subjective errors on the sense that the weight assigned for each ζ is generated automatically. 3. Conclusion Multi-agent system is a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver. The general goal of MAS is to create systems that interconnect separately developed agentsThus enabling the ensemble to function beyond the capabilities of any singular agent in the set-up in agent model. This research can demonstrate that agent technology is suitable to solve communication concerns for a distributed environment. Multi-agent systems try to solve the entire problem by collaboration with each other and result in preferable answer for complex problems. For further works, it is recommended for developing this model to have multi retailer and even multi distributor and apply the auction mechanism between them. and Languages, 1998. [3] Charles M. Macal and Michael J. North Tutorial on Agent-Based Modeling And Simulation Part 2: How to Model With Agents Proceedings of the 2006 Winter Simulation Conference [4] Fu-ren Lin, Sheng-hsiu Huang, and Sheng-cheng Lin , “Effects of Information Sharing on Supply Chain Performance in Electronic Commerce“ ,IEEE Transactions On Engineering Management, Vol. 49, No. 3, August 2002. [5] Fu-ren Lin ,Yu-wei Song and Yi-peng Lo ,”Effects of Trust Mechanisms on Supply Chain Performance Using Multi-agent Simulation and Analysis” , Proccding of the First Workshop on Knowledge Economy and Electronic Commerce. [6] José Alberto R. P. Sardinha1, Marco S. Molinaro2, Patrick M. Paranhos2, Pedro M. Cunha2,Ruy L. Milidiú2, Carlos J. P. de Lucena2 , “A Multi-Agent Architecture for a Dynamic Supply Chain Management” , American Association for Artificial Intelligence ,2006. [7] Ka-Chi Lam , Ran Tao, Mike Chun-Kit Lam “A material supplier selection model for property developers using Fuzzy Principal Component Analysis” Automation in Construction 19 (2010) 608–618 [8] Mario Verdicchio and Marco Colombetti ,” Commitments for Agent-Based Supply Chain Management” , ACM SIGecom Exchanges, Vol. 3, No. 1, 2002. [9] Mercedes Amor *, Lidia Fuentes “Malaca: A component and aspect-oriented agent architecture” Information and Software Technology 51 (2009) 1052–1065 [10] MihaelaUlieru, Senior Member, IEEE, and MirceaCobzaru , “BuildingHolonic Supply Chain Management Systems: An e-Logistics Application for the Telephone Manufacturing Industry” IEEE transactions on industrial informatics, vol. 1, no. 1, February 2005. [11] Onn Shehory and Arnon Sturm , “Evaluation of Modeling Techniques for Agent-Based Systems” , AGENTS’01, February 11-13, 2001, Montréal, Quebec, Canada. [12] Qing Zhang and Wuhan, “Essentials for Information Coordination in Supply Chain Systems”, Asian Social Science Vol. 4, No. 10 ,Oct 2008 IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010 205 ISSN (Online): 1694-0814 www.IJCSI.org [13] Rasoul Karimi, Caro Lucas and Behzad Moshiri ,” New Multi Attributes Procurement Auction for Agent- Based Supply Chain Formation” ,IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.4, April 2007. [14] Robert de Souza and Heng Poh Khong , “supply Chain Models in Hard Disk Drive Manufacturing” , lEEE ON Magnetics. VOL 35. No 1. March 1999 [15] Rui Carvalho, Luís Custódio , “A Multiagent Systems Approach for Managing Supply-Chain Problems: new tools and results “ , Inteligencia Artificial V. 9, No 25, 2005. [16] Vivek kumar , Amit Kumar Goel , Prof. S.Srinivisan, “A Multiagent Conceptulization For Supply-Chain Management”, Ubiquitous Computing and Communication Journal, Vol 4, No. 5 , 2009 [17] William E. Walsh and Michael P. Wellman,” Modling supply chain Formation in Multiagent System” , Artificial Intelligence, vol 1788: Agent Mediated Electronic Commerce II,Springer-Verlag, 2000 [18] Yevgeniya Kovalchuk , “Multi-Agent Decision Support System for Supply Chain Management” 10th Int. Conf. on Electronic Commerce (ICEC) ’08 Innsbruck, Austria. [19] Yang Hang and Simon Fong , “Double-agent Architecture for Collaborative Supply Chain Formation” , Proceedings of iiWAS2008. Mr. Vivek Kumar has completed his M.Phil (Computer Science) in 2009. Apart from this, he did M.Tech. (Computer Science, 2005) & MIT in 2001. He has 10 years of teaching experience in various engineering Colleges. Presently he is working as faculty in Gurgaon Institute of Technology and Management, Gurgaon, Haryana, India. Under the guidance of Dr. Srinivasan, he is pursuing Ph.D. from Department of Computer Science and Engineering, S. Gyan Vihar University, Jaipur, India He has published one international & two national (Conference Proceeding) papers on Supply Chain Management through Multi-Agent System. Dr S Srinivasan obtained his M.Sc (1971), M.Phil(1973) and Ph.D. (1979) from Madurai University . He served as Lecturer for 7 years in National Institute of Tehnology in the Computer Applications Department . Later he joined Industry as IT Head for 18 years . Again he started his teaching career serving as Professor and Head of the Department of Computer Science, PDM College of Engineering , Haryana, India. He has published several papers in Multi-Agent Technology Systems and its applications . He is member of Computer Society of India. Attended various national and international seminars and conferences and presented papers on Artificial Intelligence and Multi-Agent Technology. 因篇幅问题不能全部显示，请点此查看更多更全内容