نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری، گروه مدیریت صنعتی، پردیس کیش، دانشگاه تهران، تهران، ایران.
2 استاد، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.
3 استادیار، پژوهشکده توسعه و برنامه‌ریزی، جهاد دانشگاهی، تبریز، ایران.
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction: Production planning, scheduling, and sequencing form the core functions of manufacturing companies. The evolving and fluctuating demands of the market have turned production into a challenge, as companies must deliver high-quality products using minimal resources while responding to the uncertain demands of the market. Therefore, the need for efficient production planning, scheduling, and sequencing has become a crucial research area for both companies and researchers in recent decades. This paper addresses the modeling and solution of a production planning and scheduling problem related to human-robot collaboration under fuzzy conditions. The proposed model aims to determine decisions such as the optimal production quantity, human-robot allocation for product manufacturing on each line, processing time, and product production scheduling. To achieve integrated decisions for production planning and scheduling in human-robot collaboration, three objective functions are considered: maximizing the net present value, minimizing the maximum completion time of product manufacturing, and minimizing the total early and tardy times.
Methods: Since the demand quantity and processing time are considered as uncertain parameters in this problem, a pessimistic fuzzy programming approach is used to handle these parameters. To solve the three-objective model, the epsilon-constraint method, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-objective Particle Swarm Optimization (MOPSO), and Multi-objective Whale Optimization Algorithm (MOWOA) are applied. Thus, for solving the problem for small sizes and sensitivity analysis of the mathematical model, the epsilon-constraint method is used, and for solving larger-sized problems, metaheuristic algorithms are employed.
Results and Discussion: The analysis of the mathematical model under uncertainty reveals that by reducing the maximum completion time of product manufacturing, both the net present value and the total early and tardy times decrease. The model’s control using fuzzy programming and the uncertainty rate also shows that increasing this parameter leads to a reduction in net present value and an increase in the maximum completion time of product manufacturing. Furthermore, the analysis of various numerical examples in different sizes indicates that the solution quality of the algorithms MOWOA, NSGA-II, and MOPSO is superior to the epsilon-constraint method. Among these algorithms, MOWOA achieves the highest number of efficient solutions with the least branch distance metric and distance from the ideal point.
Conclusion: The analyses indicate that the highest total early and tardy times occur when the uncertainty rate is set at 0.5. Additionally, sensitivity analysis of the bank interest rate shows that a 4% increase in the interest rate results in a 15.68% reduction in the net present value. The bank interest rate has no impact on the method of maximum completion time of product manufacturing or the total early and tardy times. The analysis of numerical examples with various sizes also demonstrated that the epsilon-constraint method could not solve larger numerical examples, and the quality of the results obtained from metaheuristic algorithms was superior to the exact method. Moreover, the number of efficient solutions, the widest spread, and the solution time were better in the metaheuristic algorithms than in the epsilon-constraint method. Among the metaheuristic algorithms, MOWOA showed better performance than the other solution methods.
کلیدواژه‌ها [English]
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