Multi-objective differential evolution (MODE) for optimization of supply chain planning and management

Many problems in the engineering domain involve more than one objective to be optimized simultaneously. The optimal solution to a multi-objective function results in a set of equally good solutions (Pareto optimal set), rather than a unique solution. Several entities are present in a typical supply chain problem. Each of these entities has its individual objectives. When all the objectives of supply chain are combined they work towards a common goal of increasing the profitability of an organization. The supply chain model is thus multi-objective in nature which involves several conflicting objectives. A three-stage supply chain problem (involving a network of supplier, plant and customer zones) is solved using Multi-Objective Differential Evolution (MODE) algorithm in this work. Three cases of objective functions are considered in this study. Pareto optimal solutions are obtained for each case. The results are compared with those reported using NSGA-II in the literature.

[1]  R. John Milne,et al.  Matching Assets with Demand in Supply-Chain Management at IBM Microelectronics , 2001, Interfaces.

[2]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[3]  Robert J. Vokurka,et al.  The relationship of logistics to supply chain management: developing a common industry definition , 2001, Ind. Manag. Data Syst..

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

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  B. Babu,et al.  Differential evolution for multi-objective optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[7]  Andi Cakravastia,et al.  A two-stage model for the design of supply chain networks , 2002 .

[8]  Pierpaolo Pontrandolfo,et al.  Inventory management in supply chains: a reinforcement learning approach , 2002 .

[9]  B. V. Babu,et al.  Multiobjective differential evolution (MODE) for optimization of adiabatic styrene reactor , 2005 .

[10]  Hongwei Ding,et al.  "ONE" a new tool for supply chain network optimization and simulation , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[11]  B. Babu,et al.  Strategies of Multi-Objective Differential Evolution (MODE) for Optimization of Adiabatic Styrene Reactor , 2007 .

[12]  Nejat Karabakal,et al.  Supply-Chain Analysis at Volkswagen of America , 2000, Interfaces.

[13]  Godfrey C. Onwubolu,et al.  New optimization techniques in engineering , 2004, Studies in Fuzziness and Soft Computing.

[14]  Hokey Min,et al.  The dynamic relocation and phase-out of a hybrid, two-echelon plant/warehousing facility: A multiple objective approach , 2000, Eur. J. Oper. Res..

[15]  Marcus Brandenburg,et al.  Integrating collaborative planning and supply chain optimization for the chemical process industry (I) - methodology , 2004, Comput. Chem. Eng..

[16]  Russell E. King,et al.  An Apparel-supply System for QR Retailing , 1992 .

[17]  B. V. Babu,et al.  Elitist - Multi-objective Differential Evolution (E-MODE) Algorithm for Multi-objective Optimization , 2007, IICAI.

[18]  Jalal Ashayeri,et al.  Central Distribution in Europe: A Multi‐Criteria Approach to Location Selection , 1997 .

[19]  Gerald G. Brown,et al.  Global Supply Chain Management at Digital Equipment Corporation , 1995 .

[20]  Rainer Storn,et al.  Differential Evolution-A simple evolution strategy for fast optimization , 1997 .

[21]  Jeffrey A. Joines,et al.  Supply chain multi-objective simulation optimization , 2002, Proceedings of the Winter Simulation Conference.

[22]  F. Azadivar,et al.  Simulation based optimization for supply chain configuration design , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..