A memetic algorithm with extended random path encoding for a closed-loop supply chain model with flexible delivery

Abstract

Logistics network design is a major strategic issue in supply chain management of both forward and reverse flow, which industrial players are forced but not equipped to handle. To avoid sub-optimal solution derived by separated design, we consider an integrated forward reverse logistics network design, which is enriched by using a complete delivery graph. We formulate the cyclic seven-stage logistics network problem as a NP hard mixed integer linear programming model. To find the near optimal solution, we apply a memetic algorithm with a neighborhood search mechanism and a novel chromosome representation including two segments. The power of extended random path-based direct encoding method is shown by a comparison to commercial package in terms of both quality of solution and computational time. We show that the proposed algorithm is able to efficiently find a good solution for the flexible integrated logistics network. Keywords Memetic algorithm Á Closed-loop supply design Á Random path Á Flexible delivery This article is part of a focus collection on ''Dynamics in Logistics: Digital Technologies and Related Management Methods''.

A Comparison between Memetic Algorithm and Genetic Algorithm for an Integrated Logistics Network with Flexible Delivery Path

“…Two decision variables y i ∈ {0, 1} and x ij ∈ ℕ 0 represent whether a stage i ∈ V is used and which quantity is shipped between node i and j. Some conditions are considered and presented in [16] to adapt problem 1. We would like to note that the set of nodes (1)…”

“…With in this study, we consider a memetic algorithm with a novelty in chromosome representation called "extended random path direct encoding method" and local search engine to enhance its search ability for the proposed flexible model and optimize the design of the supply chain network [16]. The reasons that we apply the extended random path direct encoding method can be summarized as follow:…”

The Effect of Various Parameters of Solution Methodology on a Flexible Integrated Supply Chain Model

“…Since our network design problem represents an NP-hard problem [16,[30][31][32], mixed integer linear programming cannot derive a suitable solution for large scale problems in acceptable time. Memetic Algorithms, however, belong to class of metaheuristic algorithms, which have been applied successfully for the proposed model and similar cases [25,[33][34][35][36][37]. A complete explanation has been presented in [25] regarding the procedure of initialization by extended random path-base direct encoding, two-point crossover, and local search as well as the Memetic Algorithm applied in this study.…”

“…To validate our approach, we consider LINGO [25] as a benchmark to assess the behavior and performance of the proposed MA in terms of accuracy of the obtained solution under different values of the related effective parameters. Within this study, one particular condition is selected and changing any other parameter is considered till the effect of each parameter is recorded and analyzed.…”

“…Algorithms have shown that there exist two major impact sources affecting the performance of Memetic Algorithms [17], that is, the chromosome representation and the memetic operators. Here, we utilize the chromosome representation from [25] and analyze the effect of various parameters on the memetic operators regarding both performance and computing time. We particularly focus on (i) number of iterations, (ii) population size, (iii) number of local search iterations, (iv) crossover rate.…”