Energy-cost-aware resource-constrained project scheduling for complex product system with activity splitting and recombining. (1st July 2021)

• Record Type: Journal Article
• Authors: Du, Baigang; Tan, Tian; Guo, Jun; Li, Yibing; Guo, Shunsheng
• Is Part Of: Expert systems with applications. Volume 173(2021)
• Publication Date: 2021-07-01
• Subjects: Project scheduling -- Complex product system -- Activity splitting and recombining -- Fuzzy clustering -- Improved NSGA-II; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114754
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: 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. Abstract: 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.