A multi-objective co-evolutionary algorithm of scheduling on parallel non-identical batch machines. (1st April 2021)

• Record Type: Journal Article
• Authors: Wang, Yan; Jia, Zhao-hong; Li, Kai
• Is Part Of: Expert systems with applications. Volume 167(2021)
• Publication Date: 2021-04-01
• Subjects: Co-evolution -- Parallel batch machines -- Adaptive search -- Makespan -- Total energy consumption; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.114145
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: This paper investigates the problem scheduling a set of jobs on parallel batch processing machines with different capacities and non-identical processing powers for minimizing the makespan and the total energy consumption, where the jobs have non-identical sizes, dynamical arrival time and different processing time. To address the bi-objective optimization problem, a three-populations co-evolutionary algorithm is proposed, which is based on exploration and coordination searches among three colonies. For guaranteeing the diversity of solutions, an adaptive search strategy based on the largest angle among adjacent solutions is designed, and a new method is proposed to select ants to update pheromone trails for improving the convergence of solutions. Finally, the proposed algorithm is compared with the existing multi-objective algorithms through extensive simulated experiments, and the simulated results are statistically analyzed. And the experimental results show that the proposed algorithm outperforms all the compared algorithms, which verify the validity of the algorithm proposed in this paper. Highlights: A co-evolutionary algorithm CBTP-MMAS is proposed to solve the problem. A new strategy MMAS-P using users' preferences is proposed to construct solutions. An adaptive search strategy is designed to improve the uniformity of solutions. A strategy that selects ants to update experiences is designed to improve convergence.