پیاده‌سازی الگوریتم بهینه‎‍سازی دسته میگوها برای مسئلۀ بالانس خطوط مونتاژ مدل‎‍های چندگانه با در نظر گرفتن اثر یادگیری و فراموشی کارگران

یکی از دغدغۀ تولیدکنندگان، بحث تنوع سلیقه‌های مشتریان است و برای مدیریت این شرایط با کمترین تغییر در محصولات تولیدی، به خطوطی به‌اصطلاح چندگانه نیاز است که انعطاف لازم را برای تولید این محصولات دارا باشد. از سویی خیلی از محصولات نیازمند عملیات مونتاژند؛ از این ‌رو، به‌عنوان یک نوآوری در این مقاله، مدل ریاضی جدیدی برای بالانس خط مونتاژ مدل‌های چندگانه ارائه شده که در آن عملیات مونتاژ توسط کارگران و به شکل دستی صورت گرفته است؛ اما برای برنامه‌ریزی دقیق‌تر، تفاوت‌هایی که کارگران از منظر اثر یادگیری و فراموشی دارند، بر بالانس خط مونتاژ منظور شده است. هدف این پژوهش، حداقل‌کردن تعداد ایستگاه‌های کاری به ازای یک‌ زمان سیکل معین است تا علاوه بر پوشش سلایق مختلف مشتریان، به‌طور غیرمستقیم نیز هزینه‎‍های احداث ایستگاه‎‍ها، استخدام و به‌کارگیری نیروی انسانی حداقل شود. به‌دلیل ساختار nphard مسئله، از الگوریتم بهینه‌سازی 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 نرم‌افزار گمز استفاده شد و برای مسائل با ابعاد متوسط و بزرگ از الگوریتم دسته میگوها به‌عنوان الگوریتم پیشنهادی و الگوریتم ازدحام تودۀ ذرات، به‌عنوان الگوریتم رقیب بهره گرفته شد. تجزیه‌وتحلیل بر مجموعه داده‎‍های استاندارد مسائل بالانس خط مونتاژ مختلف، نشان داده است الگوریتم دسته میگوها در زمان، حل بسیار کمتری نسبت‌به گمز دارد و الگوریتم بهینه‌سازی تودۀ ذرات توانسته است به پاسخ‎‍های بهینه و یا نزدیک به بهینه دست ‌یابد که این موضوع نشان‎‍دهندۀ کارایی الگوریتم پیشنهادی در حل این دسته از مسائل است.

Krill herd optimization algorithm for multimodel 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 multimodel 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 multimodel 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 multimodel 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 multimodel 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 mixedinteger mathematical model for multimodel 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 multimodel, random demand for each product has been considered. After mathematical modeling, different smallsized problems have been solved by the GAMS software. Results and sensitivity analysis underlined the validity of the proposed model. Since this problem is typically NPhard, GAMS software cannot solve medium and largesized problems in a reasonable time. Therefore, the Krill herd optimization and Particle Swarm Optimization (PSO) algorithms have been used for medium and largesized 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 nearoptimal 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 realworld 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 realworld 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 multimodel assembly line balancing, sequencing, and worker assignment according to the learning and disremembering effect. Since the problem was NPHard, as well as GAMS software, two metaheuristic algorithms were applied for a similar problem, and their efficiency was compared with each other. The twomentioned algorithms have not been used in previous studies. Both academic researchers and production managers can benefit from applying the findings of this study.