پیاده سازی الگوریتم بهینه سازی دسته میگوها برای مسئله بالانس خطوط مونتاژ مدل های چندگانه با در نظر گرفتن اثر یادگیری و فراموشی کارگران (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
یکی از دغدغه تولیدکنندگان، بحث تنوع سلیقه های مشتریان است و برای مدیریت این شرایط با کمترین تغییر در محصولات تولیدی، به خطوطی به اصطلاح چندگانه نیاز است که انعطاف لازم را برای تولید این محصولات دارا باشد. از سویی خیلی از محصولات نیازمند عملیات مونتاژند؛ از این رو، به عنوان یک نوآوری در این مقاله، مدل ریاضی جدیدی برای بالانس خط مونتاژ مدل های چندگانه ارائه شده که در آن عملیات مونتاژ توسط کارگران و به شکل دستی صورت گرفته است؛ اما برای برنامه ریزی دقیق تر، تفاوت هایی که کارگران از منظر اثر یادگیری و فراموشی دارند، بر بالانس خط مونتاژ منظور شده است. هدف این پژوهش، حداقل کردن تعداد ایستگاه های کاری به ازای یک زمان سیکل معین است تا علاوه بر پوشش سلایق مختلف مشتریان، به طور غیرمستقیم نیز هزینه های احداث ایستگاه ها، استخدام و به کارگیری نیروی انسانی حداقل شود. به دلیل ساختار NP-hard مسئله، از الگوریتم بهینه سازی دسته میگوها استفاده شده است که پیش از این برای مسائل مشابه این موضوع نیز به کار نرفته است. به بیان دیگر برای حل مسائل مختلف در ابعاد کوچک از نرم افزار گمز استفاده شد و برای مسائل با ابعاد متوسط و بزرگ از الگوریتم دسته میگوها به عنوان الگوریتم پیشنهادی و الگوریتم ازدحام توده ذرات، به عنوان الگوریتم رقیب بهره گرفته شد. تجزیه وتحلیل بر مجموعه داده های استاندارد مسائل بالانس خط مونتاژ مختلف، نشان داده است الگوریتم دسته میگوها در زمان، حل بسیار کمتری نسبت به گمز دارد و الگوریتم بهینه سازی توده ذرات توانسته است به پاسخ های بهینه و یا نزدیک به بهینه دست یابد که این موضوع نشان دهنده کارایی الگوریتم پیشنهادی در حل این دسته از مسائل است.Krill herd optimization algorithm for multi-model assembly line balancing problem with learning and forgetting effects of workers
Purpose: One of the topics for manufacturers is to discuss the diversity of customer tastes. To manage this situation with the least change in products, multiple assembly lines make the necessary flexibility to produce the products. In multi-model assembly lines, different product types in different batches are produced and there is a setup time to prepare assembly lines between two types of products to produce another product type. This paper aims to investigate multi-model assembly lines and their sequencing, balancing, and worker assignment due to the existence of various tasks for workers according to learning and disremembering effects. Frequent changes in the product design of multi-model assembly lines according to customer demands can reduce the learning effect of workers and increase task times, while in another view, repeating tasks, particularly for products with more demands can increase the learning effect and reduce the task times. Therefore, in this study, the effects of workers' learning and disremembering multi-model assembly line balancing, sequencing, and worker assignment are investigated to minimize the number of workstations for a given cycle time not only to cover the different tastes of customers, but also indirectly minimize the costs of building stations, hiring, and employing manpower. Design/methodology/approach: In this paper, as an innovation, a mixed-integer mathematical model for multi-model assembly line balancing, sequencing, and worker assignment with different workers' skill levels and learning and disremembering rates has been developed to minimize the number of stations. Based on the nature of the multi-model, random demand for each product has been considered. After mathematical modeling, different small-sized problems have been solved by the GAMS software. Results and sensitivity analysis underlined the validity of the proposed model. Since this problem is typically NP-hard, GAMS software cannot solve medium and large-sized problems in a reasonable time. Therefore, the Krill herd optimization and Particle Swarm Optimization (PSO) algorithms have been used for medium and large-sized problems, which have not been used earlier in similar cases. The Krill herd optimization algorithm has been used as the proposed algorithm and PSO has been used as a competing algorithm. The parameters of both algorithms have been adjusted by the Taguchi method, and the best level has been selected for each parameter. Findings: 12 test problems were solved with different sizes. Results indicated that only five GAMS problems could reach the optimal solution. For better comparison of the Krill herd optimization and the particle swarm optimization algorithm, each test problem was run 30 times and minimum, maximum, and average objective function and their running times were reported. The results indicated that the objective function of both metaheuristic algorithms was the same but the Krill herd optimization algorithm can achieve optimal or near-optimal answers in less time than GAMS and the PSO algorithm declared the efficiency of the proposed algorithm in solving these problems. Research limitations/implications: One of the limitations in this research was the lack of cooperation of factories whose assembly lines were similar to the problem considered in this study, and in this regard, the real-world data was not accessible. Therefore, the standard test problems were used that existed in the famous database of assembly line balancing problems. Since the problem in this paper was new, some other required data, and different examples in different ways needed to be considered, randomly. Another limitation of using this research in a real-world situation was the challenge of exact determination of learning and disremembering rate of each worker which can be solved by using experts in the field of assessment and training. Originality/value: In this paper, a mathematical model was developed for multi-model assembly l
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