Solving a new robust reverse job shop scheduling problem by meta-heuristic algorithms

The growing concerns of companies to economic savings, optimal utilization of resources, and increased environmental protection regulations prompt the manufacturers to be more focused on the recycling of the products that are at the end of their useful life. This study considers a job shop scheduling problem with reverse flows under uncertainty. Since the main parameter of the model (i.e., the processing time of operations) is tainted with a great degree of uncertainty in real-world applications, a robust programming approach is utilized. This paper proposes a computationally efficient model. Due to the complexity and difficulty of solving the presented model, an exact solution method for small-sized instances and simulated annealing (SA) and discrete harmony search (DHS) algorithms for medium- and large-sized instances are proposed. The model performance is evaluated by comparing the computational results with the literature. Furthermore, the performance of the proposed meta-heuristic algorithms is evaluated by comparing the resulted solutions with the exact method for small-sized instances and with three other meta-heuristics algorithms, such as discrete particle swarm optimization (DPSO) and invasive weed optimization (DIWO), and iterated greedy (IG) algorithms, for medium- and large-sized instances. The satisfying results show that the presented model and proposed algorithms ensure good quality solutions within a reasonable time for all test problems and the SA algorithm outperforms the DIWO, DPSO, DHS, and IG algorithms in most cases.

Automated summary

The study highlights the importance of companies focusing on economic savings, optimal resource utilization, and environmental protection regulations, particularly emphasizing the significance of product recycling. An efficient computational model is proposed, evaluated using exact solution methods for small instances and simulated annealing and discrete harmony search algorithms for medium- and large-sized instances.