بهینه‌سازی خطوط تولید چند محصولی با رویکرد شبیه‌سازی و برنامه‌ریزی چند هدفه

این مطالعه، به بررسی خطوط تولید چند محصولی نامطمئن با چیدمان ترکیبی (سری-موازی) با احتمال خرابی ماشین‌آلات در هر ایستگاه کاری می‌پردازد. هدف از این پژوهش، توسعه یک مدل ریاضی چند‌هدفه شامل بیشینه‌سازی نرخ تولید و کمینه‌سازی هزینه‌های ناشی از کاهش زمان پردازش ایستگاه‌های کاری و هم‌چنین حجم کل بافرها، با توجه به مقادیر بهینه میانگین زمان پردازش هر محصول در هر ایستگاه و حجم بافر میان ایستگاه های کاری می‌باشد. بدین منظور جهت پیکره‌بندی ساختار مدل ریاضی از تکنیک‌هایی نظیر شبیه‌سازی، طراحی آزمایشات و رویه سطح پاسخ و جهت حل آن از الگوریتم‌های فرا ابتکاری ژنتیک مرتب‌سازی نامغلوب(nsga-ii) و ژنتیک رتبه‌بندی نامغلوب(nrga) استفاده شده است. اعتبار مدل ریاضی چند هدفه و کاربرد متدولوژی پیشنهادی حل مدل، در یک مطالعه موردی بررسی گردیده‌است. درنهایت، عملکرد الگوریتم‌های به‌ کارگرفته‌شده در تحقیق حاضر مورد ارزیابی قرار گرفته‌است. نتایج نشان می دهد مدل ریاضی چند هدفه پیشنهادی جهت بهینه سازی خطوط تولید نامطمئن معتبر بوده و توانایی دستیابی به نقاط بهینه (نزدیک به بهینه) در سایر مسائل مشابه با ابعاد بزرگ تر و پیچیدگی بیشتر را دارا می باشد.

an optimization of multi-product assembly lines using simulation and multi-objective programming approach

this paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. the aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate of the system and the minimization of the total cost of reducing mean processing times and the total buffer capacities with respect to the optimal values of the mean processing time of each product in each workstation and the buffer capacity between workstations. for this purpose, in order to configure the structure of the mathematical model, simulation, design of experiments and response surface methodology are used and to solve it, the meta-heuristic algorithms including non-dominated sorting genetic algorithm ii (nsga-ii) and non-dominated ranked genetic algorithm (nrga) are implemented. the validity of the multi-objective mathematical model and the application of the proposed methodology for solving the model is examined on a case study. finally, the performance of the algorithms used in this study is evaluated. the results show that the proposed multi-objective mathematical model is valid for optimizing unreliable production lines and has the ability to achieve optimal (near optimal) solutions in other similar problems with larger scale and more complexity.introductiona production line consists of a sequence of workstations, in each of which parts are processed by machines. in this setup, each workstation includes a number of similar or dissimilar parallel machines, and a buffer is placed between any two consecutive workstations. in production lines, the buffer capacity and processing time of machinery have a significant impact on the system’s performance. the presence of buffers helps the system to maintain production despite possible conditions or accidents, such as machinery failure or changes in processing time. previous research has investigated production lines without any possibility of machinery failure, referred to as &safe production lines.& however, in real production lines, machinery failure is inevitable. therefore, several studies have focused on &uncertain production lines,&assuming the existence of a probability of failure in a deterministic or exponential distribution. this research examines uncertain production lines with a combined layout, resulting from the combination of parallel deployment of machines within each workstation, if necessary, and serial deployment of workstations. the objective of this research is to determine the optimal values (or values close to optimal) of the average processing time of each product in each workstation, as well as the volume of buffers, as decision variables. the approach aims to maximize the system’s output while minimizing the costs associated with reducing the processing time of workstations and minimizing the total volume of buffers between stations. moreover, simulation can be applied without interrupting the production line or consuming significant resources. in this research, due to the high cost and time involved, implementing the proposed changes on the system is not cost-effective for investigating the changes in the production system’s output rate. therefore, the simulation technique has been utilized to optimize the production line.research methodthe present study aims to develop a multi-objective mathematical model, based on simulation, to optimize multi-product production lines. in the first step, the structure of the multi-objective mathematical model is defined, along with the basic assumptions. to adopt a realistic approach in the model structure, the simulation technique has been employed to address the first objective function, which is maximizing the output rate of the production line.