Lagrangian heuristic algorithm for green multi-product production routing problem with reverse logistics and remanufacturing. (January 2021)

• Record Type: Journal Article
• Authors: Parchami Afra, A.; Behnamian, J.
• Is Part Of: Journal of manufacturing systems. Volume 58(2021)Part A
• Page Start: 33
• Page End: 43
• Publication Date: 2021-01
• Subjects: Multi-product routing -- Startup cost -- Reverse logistics -- Lagrangian relaxation -- Carbon emissions; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.11.013
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Multi-product production routing problem is studied. The startup, reverse logistics and remanufacturing decisions are considered. In this study, a mixed-integer mathematical model was proposed. The Lagrange relaxation algorithm is applied. The feasiblizer algorithm is provided for feasibilization of the solution. Abstract: Today, due to increased market competition, the integration of production and distribution decisions into the supply chain leads to efficiency improvements. Therefore, production routing models have been developed to optimize production and distribution, simultaneously. In recent years, since, the product life cycle has become shorter than in the past, product return policies with fast response times, emphasis on return management, deformation and restorage of finished goods have become very significant. In this paper, the multi-product production routing problem with startup costs and environmental considerations has been studied. Furthermore, reverse logistics and remanufacturing decisions have been integrated. After modeling the problem as mixed-integer linear programming, due to its NP-hardness and the successful application of the Lagrangian Relaxation algorithm (LR) in solving complex supply chain problems, this algorithm has been chosen as the solution method. After applying the standard LR algorithm and the improved LR algorithm that its subgradient optimization method was modified, as a heuristic algorithm, the feasiblizer algorithm is also proposed for feasibilization of the solution obtained from the LR algorithms. To validate the model and solution method, firstly, test problems are solved by GAMS, and then the proposed algorithm is applied to test problems. The numerical results show the good performance of the LR algorithm in medium-size test problems. Finally, based on the computational experiments, managerial insights on the problem have been provided.