Journal of Quality Engineering and Management

Abstract Purpose: This study aims to develop a mathematical model for flexible job shop scheduling. The primary focus is on optimizing three objectives: the makespan, the maximum machine workload, and the total workload. The ultimate goal is to enhance productivity and flexibility in manufacturing systems.
Methodology: Two metaheuristic algorithms, NSGA-II and MOGWO, were used to solve the model. The model was first validated on a small scale, and then a sensitivity analysis was conducted on larger instances. The performance of the algorithms was compared based on accuracy and solution quality metrics.
Findings: The results indicate that MOGWO performs better on medium-sized problems, whereas in large-scale cases, the difference between the two algorithms is not significant. The highest sensitivity was observed among the objectives regarding production and maintenance costs. Additionally, a resource-allocation pattern and an optimal sequence of operations were derived.
Originality/Value: The originality of this research lies in developing and applying a multi-objective mathematical model for flexible job-shop scheduling that considers real-world constraints, including costs and resource limitations. The simultaneous use and detailed comparison of NSGA-II and MOGWO across different problem sizes is another contribution. Furthermore, the proposed operational pattern improves the applicability of the results in industrial environments.

Abstract The purpose of this paper is to design a multi-objective mathematical programming model for scheduling multifunctional machines in a production cell. For this purpose, a multiobjective invasive weed algorithm was proposed and its solution results were compared with multi-objective and genetic particle swarm algorithms. Algorithm parameters were adjusted according to Taguchi method. The innovation of this article, on the one hand, is in implementing the idea of machine processing speed in the production of parts with different qualities. In other words, to ensure quality, processing speed and loading rate are adjusted in the machine, and on the other hand, a multi-objective algorithm with a new chromosome structure was designed to optimize the model. To analyze the performance of solution algorithms, thirty sample problems with different dimensions were designed and performed ten times each. The analysis of the results showed that the multi-objective invasive weed-based algorithm was able to solve and answer problems more than other algorithms.