The capacity matching problem of the third-party shared manufacturing platform with capacity time windows and order splitting

Abstract

The development of sharing economy and new information technologies has promoted the emergence of third-party shared manufacturing platforms (TPSMPs). In the shared manufacturing context, one main challenge to TPSMPs is the matching of manufacturing enterprises with insufficient production capacity (capacity demanders) and those with overcapacity (capacity suppliers), where available capacities of capacity suppliers are within time ranges, and orders of capacity demanders can be split. In this capacity matching problem with capacity time windows and order splitting (CMPCTW-OS), each capacity demander's order needs to be delivered on time, while each capacity supplier can also match with multiple capacity demanders and fulfil orders of the capacity demanders by sequence. A mathematical model for the CMPCTW-OS is developed to maximise the total profit of the TPSMP. Then, we design a two-stage heuristic algorithm to solve this model. In the first stage, the inserting algorithm (IA) is used to obtain an initial feasible solution. In the second stage, the iterated local search (ILS) is applied to optimise and improve the initial feasible solution. Finally, in numerical simulation experiments, the effectiveness of IA-ILS has been verified by comparison with the GUROBI solver.

KEYWORDS:

• Third-party platform
• shared manufacturing
• capacity matching
• time windows
• order splitting

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the corresponding author, [[email protected]], upon reasonable request.

Notes

1 https://www.mfg.com/

2 https://tgc.1688.com/

Additional information

Funding

This research is supported by the National Natural Science Foundation of China [Grant Number 72072016] and the Major Program of the National Social Science Foundation of China [Grant Number 20&ZD084].

Notes on contributors

Xumei Zhang

Xumei Zhang is a professor at the School of Economics and Business Administration, Chongqing University, China. Her research interests include e-commerce, supply chain management, and operations management. Her work has been published in International Journal of Production Research, European Journal of Operational Research, International Journal of Production Economics, Expert Systems with Applications, International Journal of Electronic Commerce and others. Dr. Zhang’s research is supported by the National Natural Science Foundation of China and the National Key Research and Development Programme.

Duanyang Cao

Duanyang Cao is a Ph.D. student of operations management at the School of Economics and Business Administration, Chongqing University, China. Her research interests include platform operation management and supply chain management.

Bin Dan

Bin Dan is a professor at the School of Economics and Business Administration, Chongqing University, China. His research interests include supply chain management, e-commerce, and platform strategy. His publications have appeared in International Journal of Production Research, European Journal of Operational Research, International Journal of Production Economics, Economic Modelling, International Journal of Electronic Commerce, Computers & Industrial Engineering, and others. Dr. Dan’s research is supported by the National Natural Science Foundation of China.

Jianfeng Rui

Jianfeng Rui is a Ph.D. student of operations management at the School of Economics and Business Administration, Chongqing University, China. His research interests focus on platform operation management and supply chain management.

Shengming Zhang

Shengming Zhang is a Ph.D. student of operations management at the School of Economics and Business Administration, Chongqing University, China. His research interests include platform operation management and supply chain management.