A decentralized coordination mechanism for integrated production–transportation–inventory problem in the supply chain using Lagrangian relaxation

This paper is concerned with integrated production, inventory and transportation planning problems directly related to the supply chain management. We establish a Lagrangian based coordination mechanism within the context of the decentralized planning approach. In addition, we demonstrate that dual prices obtained by the described approach might provide enough accuracy in case of sudden changes, such as in a customer’s demand, and thus they could assist agents to find new solutions. Computational results show that the proposed mechanism is able to help organizations to facilitate collaboration, improve business agility and meet business goals.

[1]  Ping Lou,et al.  Study on multi-agent-based agile supply chain management , 2004 .

[2]  Godwin C. Ovuworie,et al.  Mathematical Programming: Structures and Algorithms , 1979 .

[3]  Michael Florian,et al.  AN EFFICIENT IMPLEMENTATION OF THE NETWORK SIMPLEX METHOD. , 1997 .

[4]  Mikael Rönnqvist,et al.  A Lagrangean heuristic for the capacitated concave minimum cost network flow problem , 1994 .

[5]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

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

[7]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[8]  Awi Federgruen,et al.  Coordination Mechanisms for a Distribution System with One Supplier and Multiple Retailers , 2001, Manag. Sci..

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

[10]  Marshall L. Fisher,et al.  Supply Chain Inventory Management and the Value of Shared Information , 2000 .

[11]  Børge Obel,et al.  Design Models for Hierarchical Organizations , 1995 .

[12]  G. Barbarosoglu,et al.  Hierarchical design of an integrated production and 2-echelon distribution system , 1999, Eur. J. Oper. Res..

[13]  Ulrich Wilhelm Thonemann,et al.  Production, Manufacturing and Logistics Improving supply-chain performance by sharing advance demand information , 2002 .

[14]  David K. Smith.  Network Flows: Theory, Algorithms, and Applications , 1994 .

[15]  S. DAVID WU,et al.  Auction-theoretic coordination of production planning in the supply chain , 2000 .

[16]  Norman Sadeh,et al.  MASCOT: An agent-based architecture for dynamic supply chain creation and coordination in the internet economy , 2001 .

[17]  Clarence H. Martin,et al.  Integrated Production, Distribution, and Inventory Planning at Libbey-Owens-Ford , 1993 .

[18]  Monique Guignard-Spielberg,et al.  Lagrangean decomposition: A model yielding stronger lagrangean bounds , 1987, Math. Program..

[19]  J. Robinson.  AN ITERATIVE METHOD OF SOLVING A GAME , 1951, Classics in Game Theory.

[20]  Kadir Ertogral,et al.  Auction-theoretic coordination of production planning in the supply chain , 2000 .

[21]  Panos M. Pardalos,et al.  Heuristic approaches to production-inventory-distribution problems in supply chains , 2003 .

[22]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[23]  Børge Obel,et al.  Design models for hierarchical organizations : computation, information, and decentralization , 1995 .

[24]  H. Kuhn.  Classics in Game Theory , 1997 .

[25]  L. Zurich,et al.  Operations Research in Production Planning, Scheduling, and Inventory Control , 1974 .

[26]  K. Jörnsten,et al.  Designing a minimal spanning tree network subject to a budget constraint , 1988 .

[27]  A. Roadmapof.  A Roadmap of Agent Research and Development , 1995 .

[28]  K. Jörnsten,et al.  A new Lagrangian relaxation approach to the generalized assignment problem , 1986 .