Production planning for stochastic manufacturing/remanufacturing system with demand substitution using a hybrid ant colony system algorithm. (10th March 2019)

• Record Type: Journal Article
• Authors: Liu, Wenjie; Ma, Wenyan; Hu, Yi; Jin, Mingzhou; Li, Kai; Chang, Xiangyun; Yu, Xianyu
• Is Part Of: Journal of cleaner production. Volume 213(2019)
• Page Start: 999
• Page End: 1010
• Publication Date: 2019-03-10
• Subjects: Production planning -- Hybrid manufacturing/remanufacturing system -- Demand substitution -- Ant colony system algorithm with random sampling method; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2018.12.205
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: A hybrid manufacturing/remanufacturing system (HMRS) is an effective tool to address the global challenge of resource depletion and environmental deterioration. This paper aims to make an optimal production plan for a stochastic HMRS with demand substitution. To achieve the above objective, a multi-period mixed integer programming model was first constructed. An ant colony system algorithm with random sampling method (ACS-RSM) was proposed to minimize the total expected cost of the stochastic HMRS. Finally, the proposed model and ACS-RSM algorithm were applied to an auto alternator case. The effects of the used product recovery rate and batch sizes of new and remanufactured products on the total expected cost were analyzed. The research results showed that the ACS-RSM algorithm performed well regarding computational efficiency and solution quality. There were two major findings through the practical case study. The first finding was that with increase of recovery rate of used product, total expected cost of the HMRS declined dramatically until a certain point. When the recovery rate was greater than 91%, the total expected cost kept almost constant. The second finding was that when the batch sizes of the new product and remanufactured product rose, the total expected cost had an obvious increase and the running time of the ACS-RSM algorithm decreased monotonically. The study yields an effective decision-making tool for optimizing the production plan of the stochastic HMRS with demand substitution. Highlights: A multi-period mixed integer stochastic programming model for the hybrid manufacturing/remanufacturing system (HMRS) with stochastic demand and return and with downward substitution. Development of an ant colony system algorithm combined with the random sampling method to determine optimal production plans for the stochastic HMRS. Business measures for manufacturing/remanufacturing enterprises to coordinate manufacturing and remanufacturing. A real-world case to validate the model and algorithm and to provide managerial insights.