Integrated stochastic disassembly line balancing and planning problem with machine specificity

Abstract

The disassembly is a fundamental basis in converting End-of-Life (EOL) products into useful components. Related research becomes popular recently due to the increasing awareness of environmental protection and energy conservation. Yet, there are many opening questions needed to be investigated, especially the efficient coordination of different-level decisions under uncertainty is a big challenge. In this paper, a novel integrated stochastic disassembly line balancing and planning problem is studied to minimise the system cost, where component yield ratios and demands are assumed to be uncertain. In this work, machine specificities are considered for task processing, such as price, ability, and capacity. For the problem, a two-stage non-linear stochastic programming model is first constructed. Then, it is further transformed into a linear formulation. Based on problem property analysis, a valid inequality is proposed to reduce the search space of optimal solutions. Finally, a sample average approximation (SAA) and an L-shaped algorithm are adopted to solve the problem. Numerical experiments on randomly generated instances demonstrate that the valid inequality can save around 11% of average computation time, and the L-shaped algorithm can save around 64% of average computation time compared with the SAA algorithm without a big sacrifice of the solution quality.

Keywords:

• Integrated stochastic disassembly line balancing and planning
• machine specificity
• two-stage stochastic programming
• valid inequality
• SAA and L-shaped

Acknowledgements

We sincerely thank the editor, area editor, and anonymous reviewers for their efforts and contributions on improving this paper.

Disclosure statement

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

Additional information

Funding

This work was partly supported by the National Natural Science Foundation of China [grant numbers: 71832001, 71771048, 71571134].

Notes on contributors

Junkai He

Junkai He received the B.S. degree in Information Management and Information System from Henan University of Economics and Law, Zhengzhou, China, 2014; the M.S. degree in Management Science and Engineering from Donghua University, Shanghai, China, 2017; the Ph.D. degree in Mathematics and Computer Science from Univ Evry, University of Paris Saclay, Evry, France, 2020. His research interests include disassembly line balancing and planning, production planning, stochastic programming.

Feng Chu

Feng Chu received the B.S. degree in Electrical Engineering from Hefei University of Technology, Hefei, China, 1986; the M.S. degree in Metrology, Automatic Control, and Electrical Engineering from National Polytechnic Institute of Lorraine, Lorraine, France, 1991; the Ph.D. degree in Automatic Control, Computer Science, and Production Management from University of Metz, Metz, France, 1995. She is currently a Distinguished Professor and the leader of AROBAS team in lab IBISC at Univ Evry, University of Paris Saclay, Evry, France. Her research interests include complex manufacturing systems, intelligent transportation systems, logistics optimisation, production scheduling, and disassembly lines.

Alexandre Dolgui

Alexandre Dolgui received the M.S. degree/Engineer in Automated Systems of Data Processing and Management from Minsk Radioengineering Institute, Minsk, Belarus, 1983; the Ph.D. degree in Engineering Cybernetics and Computer Aided Production Management from Institute of Engineering Cybernetics, Minsk, Belarus, 1990; the Dr.Habil. degree in Industrial Engineering from University of Technology of Compiègne, France, 2000. He is now a Fellow of IISE, a Distinguished Professor and the Head of the Automation, Production, and Computer Sciences Department at the IMT Atlantique, campus in Nantes, France. His research focuses on complex manufacturing systems, assembly/disassembly lines, combinatorial design of machining lines, process planning, and supply chain optimisation under uncertainties. He is the Editor in Chief of the International Journal of Production Research.

Feifeng Zheng

Feifeng Zheng received the B.S. degree in Information Management, the M.S. degree in Management Science and Engineering, and the Ph.D. degree in Management Science and Engineering from Xi'an Jiaotong University, Xi'an, China, in 1998, 2003, and 2006, respectively. He is currently a Professor and the vice dean of Glorious Sun School of Business and Management at Donghua University, Shanghai, China. His research interests include on-line scheduling, production scheduling, and container terminal resource scheduling.

Ming Liu

Ming Liu received the B.S. degree in Management Science and Engineering and the Ph.D. degree in Management Science and Engineering from Xi'an Jiaotong University, Xi'an, China, in 2005 and 2010, respectively. He is currently an Associate Professor at Tongji University, Shanghai. His research interests include logistics optimisation, production scheduling, and intelligent transportation systems.