Preprint Article Version 1 This version is not peer-reviewed

Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty

Version 1 : Received: 19 October 2017 / Approved: 20 October 2017 / Online: 20 October 2017 (03:07:28 CEST)

A peer-reviewed article of this Preprint also exists.

Vahdat, V.; Vahdatzad, M.A. Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty. Logistics 2017, 1, 11. Vahdat, V.; Vahdatzad, M.A. Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty. Logistics 2017, 1, 11.

Journal reference: Logistics 2017, 1, 11
DOI: 10.3390/logistics1020011

Abstract

In this paper, a two-stage stochastic programming modelling is proposed to design a multi-period, multistage, and single-commodity integrated forward/reverse logistics network design problem under uncertainty. The problem involves both strategic and tactical decision levels. The first stage deals with strategic decisions, which are the number, capacity, and location of forward and reverse facilities. At the second stage tactical decisions such as base stock level as an inventory policy is determined. The generic introduced model consists of suppliers, manufactures, and distribution centers in forward logistic and collection centers, remanufactures, redistribution, and disposal centers in reverse logistic. The strength of proposed model is its applicability to various industries. The problem is formulated as a mixed-integer linear programming model and is solved by using Benders’ Decomposition (BD) approach. In order to accelerate the Benders’ decomposition, a number of valid inequalities are added to the master problem. The proposed accelerated BD is evaluated through small-, medium-, and large-sized test problems. Numerical results reveal that proposed solution algorithm increases convergence of lower bound and upper bound of BD and is able to reach an acceptable optimality gap in a convenient CPU time.

Subject Areas

integrated forward/reverse logistics network; accelerated benders’ decomposition; two-stage stochastic programming

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