Coordinated production and delivery scheduling for service-oriented manufacturing systems with deterioration effect

Abstract

Service-oriented manufacturing (SOM) is a new industrial model that combines manufacturing with service. To enhance the competitiveness of manufacturers in SOM systems, we investigate a coordinated production and delivery scheduling problem considering a third-party logistics (3PL) provider. The considered problem can be divided into two phases, including production phase and delivery phase. In the production phase, jobs are processed by multiple manufacturers located in different geographical zones. Jobs are with deteriorating processing time and set-up time. Several structural properties of the problem in the production phase are proposed, based on which a heuristic algorithm is designed to make decisions on job batching and batch sequencing. After being processed, the finished jobs are immediately delivered to a customer by manufacturers’ own vehicles or the 3PL provider. A dynamic programming algorithm is developed to solve the problem in the delivery phase. The integrated scheduling problem is proved to be NP-hard. Hence, we develop a general variable neighbourhood search (GVNS) to solve the problem in a reasonable time. The basic variable neighbourhood descent (BVND) is used as a local search method in the GVNS. Finally, the computational experiments are conducted and the results verify the effectiveness and stability of the proposed approach.

KEYWORDS:

• Coordinated scheduling
• third-party logistics
• deterioration effect
• general variable neighbourhood search
• heuristic algorithm
• dynamic programming algorithm

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (Nos. 72101071, 72071056, 72101077, 71922009, 72201089), Key Research and Development Project of Anhui Province (2022a05020023), Natural Science Foundation of Anhui Province(2108085QG287, 2108085QG291), the Ministry of education of Humanities and Social Science project(22YJC630050), China Postdoctoral Science Foundation(2022M710996), Key Scientific Research Foundation of the Education Department of Province Anhui (KJ2020A0069), Base of Introducing Talents of Discipline to Universities for Optimization and Decision-making in the Manufacturing Process of Complex Product (111 project: B17014).

Notes on contributors

Ting Zhao

Ting Zhao is currently working towards her Ph.D. degree at the School of Management, Hefei University of Technology. The researcher’s areas of interest include production scheduling and supply chain collaborative optimisation.

Shaojun Lu

Shaojun Lu serves as a lecturer in Hefei University of Technology. He is mainly engaged in the research work of high-end equipment preventive maintenance optimisation, high-end equipment manufacturing process optimisation, supply chain manufacturing collaborative optimisation in uncertain environment, etc.

Hao Cheng

Hao Cheng serves as an associate professor at the School of Management, Hefei University of Technology. His recent research interests include intelligent decision support system, algorithm design, artificial intelligence, etc.

Mingyu Ren

Mingyu Ren is currently a Ph.D. candidate at the School of Management, Hefei University of Technology. His research interests encompass combinatorial optimisation, supply chain management, and metaheuristic algorithm design.

Xinbao Liu

Xinbao Liu serves as a Professor and doctoral supervisor in Hefei University of Technology. His research interests include decision science and technology, intelligent decision support system, complex product manufacturing process optimisation, etc.