An Exact Approach for Multitask‐Oriented Service Composition and Scheduling in Cloud Manufacturing With Unavailable Time Windows and Subtask Rejection

• Published in: Naval research logistics
• Main Authors: Yin, Yunqiang, Jia, Kunze, Cheng, Tai Chiu Edwin, Li, Xiang
• Format: Journal Article
• Language: English
• Published: 01.08.2025
• ISSN: 0894-069X; 1520-6750
• DOI: 10.1002/nav.70009

Service composition and scheduling in cloud manufacturing (CMfg) has attracted increasing attention due to CMfg's platform's superior ability in integrating distributed resources and distributing integrated resources. Existing studies assume that the manufacturing cloud services (MCSs) of enterprises are all available throughout the planning horizon, yet an MCS may be occupied during specific time intervals. To deal with this issue, we study the multitask‐oriented service composition and scheduling problem (SCSP) in CMfg with unavailable time windows and subtask rejection (MTO‐SCSP‐UTWSR), whereby some subtasks of the CMfg platform's customers may be rejected, which will incur a penalty cost due to the limited service capacity of the MCSs. The customers are divided into subtask‐rejection‐sensitive customers and subtask‐rejection‐insensitive customers, and the total subtask rejection cost of each subtask‐rejection‐sensitive customer cannot exceed a predefined threshold. The objective is to determine the customer‐enterprise allocation scheme, the MCS composition for serving the subtasks allocated to each enterprise, and the subtask schedule for each MCS, subject to some service flexibility constraints, so as to minimize the sum of the customer‐enterprise allocation cost, opening cost of MCSs, and total subtask rejection cost of the subtask‐rejection‐insensitive customers. To solve the problem, we develop a Benders decomposition‐based hybrid algorithm in which the Benders subproblems are solved by a tailored branch‐and‐price‐and‐cut algorithm, which incorporates several enhancement techniques. Extensive numerical studies demonstrate the superiority of the developed algorithm in solving the problem and quantify the impacts of some model parameters on the solution structure and algorithm performance.