TY - GEN
T1 - Multi-Objective Differential Evolution (MODE) for optimization of supply chain planning and management
AU - Babu, B. V.
AU - Gujarathi, Ashish M.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
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U2 - 10.1109/CEC.2007.4424816
DO - 10.1109/CEC.2007.4424816
M3 - Conference contribution
AN - SCOPUS:61449106643
SN - 1424413400
SN - 9781424413409
T3 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
SP - 2732
EP - 2739
BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
Y2 - 25 September 2007 through 28 September 2007
ER -