A framework for internet-based production-distribution planning problem to competition and collaboration in supply chain management: A multi-agent approach

Global competition and rapidly changing technologies are forcing major changes in the production styles and new manufacturing systems. Traditional centralized environments are not able to meet these requirements. In recent years, the internet has become the worldwide information platform for data and information sharing. Information processing is an important challenge in an internet-based environment. One of the new forms of manufacturing technologies based information techniques is supply chain management (SCM). The production-distribution planning problem (PDPP) is a suitable approach to support global optimization in SCM and should be solved within the integrated structure. This approach involves the determination of the best configuration regarding location, size, technology content and product range to achieve the firm's long-term goals. On the other hand, teams of autonomous agents Asynchronous teams (ATeams), co-operating by sharing solutions through a common memory, have been proposed as a means of solving combinatorial optimization problems. In this paper, a multi-agent framework is presented to solve production-distribution planning problem (PDPP) according to the client/server architecture in an internet-based environment, where three genetic algorithms (GAs) are assumed to be the agents of the model. This framework can help the system to select the most appropriate strategy and solution for competition and collaboration in an internet-based manufacturing system.   Key words: Internet-based environment, multi-agent system, client/server, production-distribution planning, supply chain, competition and collaboration.

[1]  G. N. Evans,et al.  Application of a simulation methodology to the redesign of a logistical control system , 1998 .

[2]  Hau L. Lee,et al.  Strategic Analysis of Integrated Production-Distribution Systems: Models and Methods , 1988, Oper. Res..

[3]  Felix T.S. Chan,et al.  A hybrid genetic algorithm for production and distribution , 2005 .

[4]  Mitsuo Gen,et al.  Hybrid genetic algorithm for multi-time period production/distribution planning , 2005, Comput. Ind. Eng..

[5]  Young Hae Lee,et al.  Optimal production-distribution planning in supply chain management using a hybrid simulation-analytic approach , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[6]  Michael Milgate,et al.  Supply chain complexity and delivery performance: an international exploratory study , 2001 .

[7]  P. Tsiakis,et al.  OPTIMAL PRODUCTION ALLOCATION AND DISTRIBUTION SUPPLY CHAIN NETWORKS , 2008 .

[8]  Bülent Çatay,et al.  Strategic level three-stage production distribution planning with capacity expansion , 2006, Comput. Ind. Eng..

[9]  Chu Chai Henry Chan,et al.  Formulating Ordering Policies in a Supply Chain by Genetic Algorithm , 2006 .

[10]  Miguel A. Lejeune,et al.  Production , Manufacturing and Logistics A variable neighborhood decomposition search method for supply chain management planning problems , 2006 .

[11]  Hye Kyung Park,et al.  Virtual enterprise — Information system and networking solution , 1999 .

[12]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[13]  Zubair M. Mohamed An integrated production-distribution model for a multi-national company operating under varying exchange rates , 1999 .

[14]  Gui Yun Tian,et al.  Internet-based manufacturing: A review and a new infrastructure for distributed intelligent manufacturing , 2002, J. Intell. Manuf..

[15]  Ali Amiri,et al.  Production , Manufacturing and Logistics Designing a distribution network in a supply chain system : Formulation and efficient solution procedure , 2005 .

[16]  Halit Üster,et al.  Meta-heuristic approaches with memory and evolution for a multi-product production/distribution system design problem , 2007, Eur. J. Oper. Res..

[17]  Sai Ho Chung,et al.  Multicriterion genetic optimization for due date assigned distribution network problems , 2005, Decis. Support Syst..

[18]  Marc Goetschalckx,et al.  Strategic production-distribution models: A critical review with emphasis on global supply chain models , 1997 .

[19]  Asoo J. Vakharia,et al.  Integrated production/distribution planning in supply chains: An invited review , 1999, Eur. J. Oper. Res..

[20]  Efraim Turban,et al.  Information Technology for Management , 1995 .

[21]  Hong Yan,et al.  A strategic model for supply chain design with logical constraints: formulation and solution , 2003, Comput. Oper. Res..

[22]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[23]  Douglas J. Thomas,et al.  Coordinated supply chain management , 1996 .

[24]  Young Hae Lee,et al.  Production-distribution planning in supply chain considering capacity constraints , 2002 .

[25]  Vedat Verter,et al.  A continuous model for production-distribution system design , 2001, Eur. J. Oper. Res..

[26]  Shane Xie,et al.  Agent technology for collaborative process planning: a review , 2007 .

[27]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[28]  Christian Prins,et al.  A reactive GRASP and path relinking for a combined production-distribution problem , 2007, Comput. Oper. Res..

[29]  Theodore P. Stank,et al.  A framework for transportation decision making in an integrated supply chain , 2000 .

[30]  Yehuda Bassok,et al.  Optimizing delivery lead time/inventory placement in a two-stage production/distribution system , 2006, Eur. J. Oper. Res..

[31]  Abolfazl Kazemi,et al.  An Agent-Based Framework for Building Decision Support System in Supply Chain Management , 2008 .

[32]  B. Beamon Supply chain design and analysis:: Models and methods , 1998 .

[33]  Rakesh Nagi,et al.  Design and implementation of a virtual information system for agile manufacturing , 1997 .

[34]  William J. Baumol,et al.  An Inventory Theoretic Model of Freight Transport Demand , 1970 .

[35]  Kenth Lumsden,et al.  Improving the efficiency of the Hub and Spoke system for the SKF European distribution network , 1999 .

[36]  Said Salhi,et al.  Facility Location: A Survey of Applications and Methods , 1996 .

[37]  Nafee Rizk,et al.  Multi-item dynamic production-distribution planning in process industries with divergent finishing stages , 2006, Comput. Oper. Res..

[38]  Douglas H. Norrie,et al.  Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey , 1999, Knowledge and Information Systems.

[39]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .