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Abstract
Reconfigurable manufacturing systems (RMS) offer the potential to improve systemic responsiveness and flexibility to better cope with dynamic environments. However, the inherent modularity of RMS and dynamic environments pose challenges in optimising system configurations. To address this issue, a two-stage stochastic programming model is established to minimise configuration cost, reconfiguration cost, expected inventory and back-order cost. To efficiently handle a large number of variables, a set-covering model is obtained by using Danzig-Wolfe (DW) decomposition along with its corresponding pricing subproblem. This paper proposes a solution algorithm based on the column generation framework, which can quickly obtain a good feasible solution. To further improve the algorithm performance for larger instances, a column selection method is introduced to identify additional columns that have the potential to reduce the objective function value of the integer solution during the column generation iterations. These columns are then added to the set-covering model. The process of column selection is accelerated by employing the Graph Neural Network (GNN) algorithm. Furthermore, GNN trained on data from small instances can be directly applied to larger instances as well. The effectiveness of the proposed model and algorithm is verified by numerical experiments.
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No potential conflict of interest was reported by the author(s).
Data availability statement
The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.
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Notes on contributors
Feng Cui
Feng Cui is a Ph.D. candidate in Management Science and Engineering from Antai College of Economics and Management, SJTU, Shanghai, China. His main research interests include manufacturing operations management.
Zhibin Jiang
Zhibin Jiang received his Ph.D. degree in Engineering Management from the City University of Hong Kong in 1999. He is currently a Distinguished Professor with the Antai College of Economics and Management, SJTU, Shanghai, China. He is also the Dean of the Sino-US Global Logistics Institute of SJTU. He is a fellow of IISE and an associate editor of the International Journal of Production Research. His research interests include operations management in manufacturing and healthcare systems.
Xin Zhou
Xin Zhou is a Ph.D. candidate in Management Science and Engineering from Antai College of Economics and Management, SJTU, Shanghai, China. His main research interests include manufacturing operations management.
Junli Zheng
Junli Zheng received her Ph.D. in Industrial Engineering from Shanghai Jiao Tong University. She is currently a senior engineer with Sino-US Global Logistics Institute, SJTU. She is also the member of Shanghai Industrial Engineering Committee and the member in Society of Naval Architects. She got a talent incentive programme of ‘Leading Talent of Innovation and Entrepreneurship’ in Hefei. Her research interest is enterprise operations management including logistics, production planning, and cost management.
Na Geng
Na Geng received her Ph.D. degree in Industrial Engineering from the Ecole Nationale Superieure des Mines de Saint-Etienne, Saint-Étienne, France, and Shanghai Jiao Tong University (SJTU), Shanghai, China, in 2010. She is currently a Professor with the Sino-US Global Logistics Institute, SJTU. Her research interests include production and service operations management. Dr. Geng has been Associate Editor of the IEEE Transactions on Automation Science and Engineering, Flexible Service and Manufacturing Journal, and Health Care Management Science.