The order scheduling problem of product-service system with time windows. (July 2019)

• Record Type: Journal Article
• Authors: Zhang, Yang; Dan, Yiran; Dan, Bin; Gao, Huali
• Is Part Of: Computers & industrial engineering. Volume 133(2019)
• Page Start: 253
• Page End: 266
• Publication Date: 2019-07
• Subjects: Product-service system -- Order scheduling -- Time window -- Metaheuristics; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.04.055
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: The product-service system (PSS) order scheduling problem with time windows is addressed. A novel mathematical model for this problem is presented. New effective metaheuristics to solve this problem are developed. Abstract: A product-service system (PSS) is a system offering a mix of tangible products and intangible services to meet the personalized needs of customers. This paper studies a PSS order scheduling problem with time windows (PSSOSPTW) for a PSS provider composed of a manufacturing plant and a service centre. In this problem, the product of each PSS order is first produced by the manufacturing plant, and then the installation service is provided by the service centre. A mathematical model for the PSSOSPTW is presented to minimize the total storage and tardiness cost. To solve this NP-hard problem, an idle time insertion algorithm is proposed to obtain the optimal start and completion times of production and service of PSS orders for a given order sequence. Three metaheuristics based on iterated local search, simulated annealing and tabu search are developed to search for the best sequence through incorporating the idle time insertion algorithm. All metaheuristics utilize an adapted version of the NEH heuristic to generate initial solutions, and apply the idea of variable neighbourhood search to find local optimum. The performance of the proposed algorithms is evaluated by comparison, and the effects of some instance factors are also examined. The results show that our algorithms perform very similarly in general but quite differently in various situations. Besides, the problem size, tardiness factor, due date range factor and time window width range factor have noticeable impact on their performance.