Modeling and random path-based direct encoding for a closed loop supply chain model with flexible delivery paths

J. Oper. Res. Soc. China

2022

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.

Section: Solution Approachmentioning

confidence: 99%

Taguchi Analysis for Improving Optimization of Integrated Forward/Reverse Logistics

J. Oper. Res. Soc. China

2022

“…But lack of enough intensification spurs researchers to another alternative with stronger search operators. According to [38,39], memetic algorithms (MAs) have the benefits of GAs and also an additional local search engine to improve intensification. Recently, memetic algorithms (MAs) obtained increasing attention from the evolutionary computation community (ECC) and has been shown to be promising and effective with considerable success for solving difficult optimization problems in numerous applications domains [40][41][42].…”

Section: Solution Approachmentioning

confidence: 99%

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

2021

SN Oper. Res. Forum

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.

“…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.…”

Section: Solution Approachmentioning

confidence: 99%

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

Mathematical Problems in Engineering

2018

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.