Energy-cost-aware resource-constrained project scheduling for complex product system with activity splitting and recombining

• Published in: Expert systems with applications Vol. 173; p. 114754
• Main Authors: Du, Baigang, Tan, Tian, Guo, Jun, Li, Yibing, Guo, Shunsheng
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.07.2021; Elsevier BV
• Subjects: Activity splitting and recombining; Algorithms; Clustering; Complex product system; Energy consumption; Energy costs; Entropy (Information theory); Fuzzy clustering; Improved NSGA-II; Mathematical models; Optimization; Project management; Project scheduling; Resource scheduling; Scheduling; Splitting; Time of use; Project scheduling; Improved NSGA-II; Fuzzy clustering; Complex product system; Activity splitting and recombining
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.114754

•The energy-oriented activity splitting and recombining proactively.•Dynamic fuzzy clustering strategy for the time-varying resource consumption level.•Metaheuristic solution for project scheduling with different granularity levels. Energy-cost-aware scheduling in manufacturing process has recently been considered as an effective way to use energy. In addition, applying time-of-use is regarded as one of the optimal uses of energy which the governments extend to control energy consumption. Complex product system (CoPS) has more complex and huge amounts of multiple manufacturing activities with higher energy sensibility in project scheduling than commodity products. This paper presents a new variant of resource-constrained project scheduling problem (RCPSP), which is named energy-cost-aware resource-constrained project scheduling problem with activity splitting and recombining (eRCPSP-AS&R) for CoPS. In the proposed model, a dynamic activity splitting and recombining strategy for CoPS is used by considering energy consumption. Also, a bi-objective (project delay and energy consumption cost) mathematical model is established. The solution framework of eRCPSP-AS&R model consists of two parts: activity splitting and recombining, project scheduling. The dynamic strategy of activity splitting and recombining is used to perform energy-cost-aware based on hybrid intuitionistic fuzzy information entropy, TOPSIS and fuzzy clustering method. Moreover, an improved NSGA-II algorithm is employed to solve the Pareto-optimal solutions for the proposed bi-objective optimization problem. Series of computational experiments are conducted and verified the overall performance of proposed eRCPSP-AS&R model and approaches.