Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study

Abstract

In this paper, several concepts and techniques of a job shop manufacturing system are adjusted and utilized in a real-world pipe spool fabrication shop scheduling problem. In the present study, an improved hybrid genetic algorithm is developed to create a feasible and active schedule for the operational level of pipe spool fabrication shop with the aims of minimizing maximum completion time of all jobs. In the proposed algorithm, an enhanced solution encoding and decoding are used to represent a schedule for the fabrication shop. In order to generate high-quality initial solutions, an operation order-based global selection is designed, where this operator considers operations processing time and workload of machines while assigning machines to each of the proper operations. In the reproduction phase of this algorithm, the precedence preserving order-based crossover, uniform crossover, swapping operator, and intelligent mutation operator are used to produce the offspring and mutants for the sequencing and job routing sub-problems. In addition, a local search operator is utilized to explore the neighborhood of the solution in order to improve the solution quality further. Stepwise delineation of the proposed approach is presented and a set of benchmark problem instances that is collected from the literature, are solved. Computational outcomes demonstrate the efficacy and effectiveness of the proposed algorithm as compared to other approaches from the literature. Finally, the proposed algorithm is implemented with the collected data from an industrial fabrication shop. The principal results indicate that the productivity of pipe spool fabrication shop, with particular constraints, is increased 85.27% by applying the proposed metaheuristic approach.