A Multi-objective Evolutionary Optimization Approach for an Integrated Location-Inventory Distribution Network Problem under Vendor-Managed Inventory Systems

doi:10.1007/s10479-010-0801-3

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Title:	A Multi-objective Evolutionary Optimization Approach for an Integrated Location-Inventory Distribution Network Problem under Vendor-Managed Inventory Systems
Authors:	Liao, Shu-hsien
Contributors:	淡江大學管理科學學系
Keywords:	Supply chain integration;Vendor-managed inventory;Multi-objective supply chain model;Multi-objective evolutionary algorithm
Date:	2010-11-04
Issue Date:	2013-08-12 13:12:36 (UTC+8)
Publisher:	New York: Springer New York LLC
Abstract:	In this paper, we propose an integrated model to incorporate inventory control decisions—such as economic order quantity, safety stock and inventory replenishment decisions—into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk poling phenomenon. Multi-objective decision analysis is adopted to allow use of a performance measurement system that includes cost, customer service levels (fill rates), and flexibility (responsive level). This measurement system provides more comprehensive measurement of supply chain system performance than do traditional, single measure approaches. A multi-objective location-inventory model which permits a comprehensive trade-off evaluation for multi-objective optimization is initially presented. More specifically, a multiobjective evolutionary algorithm is developed to determine the optimal facility location portfolio and inventory control parameters in order to reach best compromise of these conflicting criteria. An experimental study using practical data was then illustrated for the possibility of the proposed approach. Computational results have presented promising solutions in solving a practical-size problem with 50 buyers and 15 potential DCs and proved to be an innovative and efficient approach for so called difficult-to-solve problems.
Relation:	Annals of Operations Research 186(1),　p.231-229
DOI:	10.1007/s10479-010-0801-3
Appears in Collections:	[Department of Management Sciences] Journal Article

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