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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 successful supply chain must be able to operate at the lowest cost while providing the best customer service as well as environmental protection. As industrial players are under pressure but mostly unprepared to take back products after their usage, logistics network design becomes an even more important issue. To allow for a maximum of flexibility and efficiency, we consider an integrated design of the forward/reverse logistics network using full delivery graph. We apply a Memetic Algorithm with a novel population generation to find a near optimal solution for large size problems. The effect of different parameters on the behavior of the proposed Metaheuristic Algorithm is investigated. Using the experimental work to find the best parameters for this problem is the outlook of these researches.

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The distribution–allocation problem is known as one of the most comprehensive strategic decisions. In real-world cases, it is impossible to solve a distribution–allocation problem completely in acceptable time. This forces the researchers to develop efficient heuristic techniques for the large-term operation of the whole supply chain. These techniques provide near optimal solution and are comparably fast particularly for large-scale test problems. This paper presents an integrated supply chain model which is flexible in the delivery path. As solution methodology, we apply a memetic algorithm with a novelty in population presentation. To identify the optimum operating condition of the proposed memetic algorithm, Taguchi method is adopted. In this study, four factors, namely population size, crossover rate, local search iteration and number of iteration, are considered. Determining the best level of the considered parameters is the outlook of this research.

The distribution/allocation problem is known as one of the most comprehensive strategic decision. In real-world cases, it is impossible to solve a distribution/allocation problem in traditional ways with acceptable time. Hence researchers develop efficient non-traditional techniques for the large-term operation of the whole supply chain. These techniques provide near optimal solutions particularly for large-scale test problems. This paper presents an integrated supply chain model which is flexible in the delivery path. As the solution methodology, we apply a memetic algorithm with a neighborhood search mechanism and novelty in population presentation method called “extended random path direct encoding method.” To illustrate the performance of the proposed memetic algorithm, LINGO optimization software serves as comparison basis for small size problems. In large-size cases that we are dealing with in real world, a classical genetic algorithm as the second metaheuristic algorithm is considered to compare the results and show the efficiency of the memetic algorithm.

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