Salp swarm algorithm based on blocks on critical path for reentrant job shop scheduling problems

Abstract

In this paper, salp swarm algorithm based on blocks on critical path (SSA_BCP) is presented to minimize the makespan for reentrant job shop scheduling problem (RJSSP), which is a typical NP-complete combinational optimization problem. Firstly, the mathematical model of RJSSP based on the disjunctive graph is established. Secondly, the extended reentrant-smallest-order-value (RSOV) encoding rule is designed to transform SSA’s individuals from real vectors to job permutations so that SSA can be used to perform global search for finding high-quality solutions or regions in the solution space. Thirdly, four kinds of neighborhood structures are defined after defining the insert operation based on block structure, which can be used to avoid search in the invalid regions. Then, a high-efficient local search integrating multiple neighborhoods is proposed to execute a thorough search from the promising regions found by the global search. Simulation results and comparisons show the effectiveness of the proposed algorithm.

Zaixing Sun

Ph.D

I am currently pursuing a PhD degree in computer science and technology with the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China. I am also a visiting student with the Evolutionary Computation Research Group, Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. My research interests include cloud computing, intelligent optimisation and scheduling.