A genetic algorithm with neighborhood search procedures for unrelated parallel machine scheduling problem with sequence-dependent setup times

Abstract

Purpose The purpose of this paper is to present a hybrid meta-heuristic based on genetic algorithms (GAs), simulated annealing, variable neighborhood descent and path relinking for solving the variant of the unrelated parallel machine scheduling problem considering sequence-dependent setup times. Design/methodology/approach The authors carried out computational experiments on literature problem instances proposed by Vallada and Ruiz (2011) and Arnaout et al. (2010) to test the performance of the proposed meta-heuristic. The objective function adopted was makespan minimization, and the authors used relative deviation, average and population standard deviation as performance criteria. Findings The results indicate the competitivity of the proposed approach and its superiority in comparison with several other algorithms. In small instances proposed by Vallada and Ruiz (2011) and on small and large instances proposed by Arnaout et al. (2010), the proposed approach presented the best results in most tested problem instances. Practical implications In small instances proposed by Vallada and Ruiz (2011) and on small and large instances proposed by Arnaout et al. (2010), the proposed approach presented the best results in most tested problem instances. Originality/value The proposed approach presented high-quality results, with an innovative hybridization of a GA and neighborhood search algorithms, tested in diverse instances of literature. Furthermore, the case study demonstrated that the proposed approach is recommended for solving real-world problems.

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

“…Huang and Yang [51] suggested a hybrid GA integrated with SA by modifying the initialization method and genetic operations, as well as employing an external elitism memory library. Abreu and Prata [56] presented a hybrid metaheuristic based on GA, SA, variable neighborhood descent, and path relinking to solve a variant of an unrelated parallel machine scheduling problem. Fattahi et al [58] combined a PSO algorithm for global exploration of the search space, and a parallel variable neighborhood search algorithm for local search in the vicinity of solutions obtained in each iteration.…”

“…The job sequence represents the job allocation inside the machines. Allocation problems that combine resource and job sequences are typical in JS-FMS planning and scheduling [20,21,24,31,32,36,40,42,46,48,50,53,56,57,59,62]; this is because the workflow of a job shop is unidirectional or recursive, as there are no constraints on the machines that perform only the first operation of a job or the last operation of the job [8]. Meanwhile, the product sequence concentrates on the sequence of products when a specific product enters a machine.…”

“…Some of the studies applied SD [19,29,30,41,42,44,45,49,53,56,57], SPT [20,22], and FIFO [22,35,63]. A few studies applied more than two dispatching rules for machine and job allocation.…”

“…Various setup times, diverse processing times, maintenance activities, and machine breakdowns are representative uncertainty factors that act as constraints. Some articles impose SDST as an uncertainty factor in their dispatching rules [19,26,29,30,32,41,44,45,49,53,56,57,76]. This time dependency from SDST can make it difficult and uncertain to determine resource and job allocations.…”

Uniform Parallel Machine Scheduling with Sequence Dependent Setup Times: A Randomized Heuristic

“…For the unrelated parallel machine scheduling problem, many researchers addressed the problem of R/ST sd /C max in the literature. Abreu and Prata [17] presented a hybrid meta-heuristic based on GA, SA, VND and path relinking. The proposed algorithm presented competitive results with an innovative hybridization of GA and neighborhood search algorithms, tested in diverse instances of literature.…”