Multiobjective Optimization in Multiechelon Decentralized Supply Chains

Abstract

Supply chain is a collaborative strategy between raw material vendors, manufacturers, and finished product distributors. It aims for synchronized material, information, and financial flows within the internal components of the supply chain to leverage an effective business outcome. The performance of a supply chain is governed by inventory (resources) minimization and order fill-rate (output) maximization. Any performance improvement in real world supply chains could lead to substantial gain in customer service levels and profit margins, thereby adding to its competitive edge over rival supply chains. This may be achieved by revising the tactical decisions to leverage both internal and external entities of the network and utilizing bullwhip as a beneficial constraint. The present work attempts to improve supply chain performance in a multiobjective fashion using multiobjective optimization. A hyper-space diagonal counting method is employed to process the Pareto front and locate an implementable solution. The workability of this multiobjective performance enhancement approach and the Pareto analysis to identify the right decision are demonstrated using a case study that takes into consideration the different business strategies adopted by supply chains.

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