تعيين تعداد ماشين‌ و حجم بافر‌ها در خطوط توليد نامطمين به‌منظور بهينه‌سازي نرخ توليد

DETERMINING THE NUMBER OF MACHINES AND BUFFER CAPACITY IN FAILURE-PRONE PRODUCTION LINES TO OPTIMIZE PRODUCTION RATE

در اين نوشتار به‌منظور افزايش نرخ توليد در خطوط توليد نامطمين (امكان خرابي ماشين‌آلات وجود دارد)، مدل‌سازي مسيله‌ي تعيين تعداد ماشين‌ها و بافر‌هاي بين ماشين‌ها بررسي، و يك متدولوژي براي حل مسيله ارايه مي‌شود. هدف از اين مطالعه بيشينه‌سازي نرخ توليد با كم‌ترين هزينه‌ي افزايش ماشين‌آلات و كم‌ترين مقدار بافر‌هاي ميان ايستگاه‌هاست. متدولوژي پيشنهادي اين مطالعه برخلاف تحقيقات پيشين با رويكردي واقع‌بينانه‌تر به خطوط توليد، فرض مي‌كند كه زمان پردازش ماشين‌آلات، نرخ خرابي و تعمير ماشين‌آلات به‌صورت زمان‌هاي تصادفي بوده و مي‌توانند از هر تابع توزيعي تبعيت كنند. به‌منظور بهينه‌سازي (نزديك بهينه) تعداد ماشين‌آلات و بافر‌ها از تكنيك‌هاي شبيه‌سازي، طراحي آزمايش‌ها، متدولوژي سطح پاسخ، الگوريتم ژنتيك و جست‌وجوي خطي بهره مي‌برد.

A production line consists of machines connected in series and separated by buffer capacity. Each part is required to be processed on each machine during a time called the service or process time. Material flow may be disrupted by machine failure or by differences between the service times of the stations. The inclusion of buffers increases the average production rate of the line by limiting the propagation of distributions, but at an additional cost of capital investment, floor space of the line and inventory. On the other hand, the inclusion of parallel machines in a station increases its reliability and results in higher production rate. Determining buffer size and number of parallel machines in a station is a challenging problem. This paper formulates the problem of determining the optimal (or near optimal) number of machines and buffer capacities in failure-prone production and assembly lines to optimize production rate. This paper also provides a methodology to solve this problem. The objective is to maximize production rate with minimum machine purchase cost and minimum total buffer size (A multi-objective formulation). The majority of solution methods assume that the process times, time between failures and repair times, are deterministic or exponentially distributed. This paper relaxes these restrictions by proposing a simulation based methodology that can consider general distribution functions for all parameters of production lines. Considering the large number of factors in such problems (machines and buffers of each station), we first use a two level fractional factorial design to determine the more significant factors, and second, use a response surface design to build a response surface metamodel as a production rate estimator, based on different configurations of buffer capacity and number of machines. We use the Lp-metric method as one of the powerful methods for multi-objective problem solving that generates different solutions based on objective weights. Finally, we use a genetic algorithm combined with the lines search method to solve the multi objective model and to determine the optimal (or near optimal) number of machines and buffer capacities in each station.