Chemical-reaction optimization for solving fuzzy job-shop scheduling problem with flexible maintenance activities

This paper proposes a hybrid chemical-reaction optimization (HCRO) algorithm for solving the job-shop scheduling problem with fuzzy processing time. The flexible maintenance activities under both resumable and non-resumable situations are also considered to make the problem more close to the reality. In the proposed algorithm, each solution is represented by a chemical molecule. Four elementary reactions, i.e., on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis, are imposed. A well-designed crossover function is introduced in the synthesis and decomposition operators. In order to balance the exploitation and exploration, HCRO divides the evolution phase into two loop bodies: the first loop body contains on-wall ineffective collision and inter-molecular ineffective collision, while the second loop body includes all the four elementary reactions. Tabu search (TS) based local search is embedded in the proposed algorithm to enhance the convergence capability. A novel decoding approach is utilized to schedule each operation, while considering each flexible preventive maintenance activity on each machine. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed HCRO algorithm is shown against three efficient algorithms from the literature, i.e., SMGA (Sakawa and Mori, 1999), GPSO (Niu et al., 2008), and RKGA (Zheng et al., 2010).