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Particle Swarm Optimization Algorithm for Integrated Lot-sizing and Scheduling in Flowshop Production Environment | ||
| Advances in Industrial Engineering | ||
| مقاله 7، دوره 48، شماره 2، دی 2014، صفحه 215-228 اصل مقاله (775.01 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jieng.2014.52915 | ||
| نویسندگان | ||
| Reza Ramezanian* ؛ Mohsen Shafiei Nikabadi؛ Sahar Fallah Sanami | ||
| Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, I.R. Iran | ||
| چکیده | ||
| Production planning and scheduling are the most important issues of the production industries, which have a considerable influence on the productivity of the production systems. Although, production planning and scheduling are in two different medium-term and short-term decision making levels, there are a very close relationship between them. Ignoring this important feature in production planning aggravates costs and reduces productivity of system. Accordingly, in this paper, scheduling constraints have been considered in production planning in order to take into account, the interconnection between these two levels The purpose of this paper is to study the multi-product and multi-period production systems in the flowshop environement so that the production and scheduling constraints are considered integrated. A more efficient mixed integer programming model with big bucket time approach is proposed to formulate the problem, which can simultaneously achieve a production plan and schedule and that is one of the main novelty of the paper. The objective function includes the cost of production, inventory, shortage and setups. Due to the high computational complexity, particle swarm optimization algorithm is proposed to solve the problem. To evaluate the efficiency of the algorithm, two mixed integer programming-based approaches with rolling horizon framework is proposed and the results are compared with each othre. . In addition, Taguchi method is used for tunning the parameters of implemented meta-heuristic.The presented algorithms explore the solution space for both lot-sizing and scheduling and find a combination of production plan and sequence that is feasible and close to optimum. Computational experiments are performed on randomly generated instances to show the efficiency of the solution methods. Computational experiments demonstrate that the performed methods have good-quality results for the test problems. Computational experiences show that the proposed algorithms can find good quality solution for the problem in a reasonable time. Also, the computational experiences confirm the efficiency of meta-heuristic against exact and heuristic methods. The average of objective value for PSO, heuristic 1 and heuristic 2 are 98.21, 104.20 and 108.29 (×103), respectively. | ||
| کلیدواژه‌ها | ||
| Multi-stage production system؛ Integrated lot-sizing and scheduling؛ Mathematical model؛ Mixed-integer programming based algorithm؛ Particle swarm optimizatin | ||
| عنوان مقاله [English] | ||
| الگوریتم بهینه‌سازی گروه ذرات برای تعیین اندازة انباشته و زمانبندی یکپارچه در محیط تولیدی جریان کارگاهی | ||
| نویسندگان [English] | ||
| رضا رمضانیان؛ محسن شفیعی نیک آبادی؛ سحر فلاح‌صنمی | ||
| استادیار دانشکدة مهندسی صنایع دانشگاه صنعتی خواجه نصیرالدین طوسی | ||
| چکیده [English] | ||
| هدف این پژوهش مطالعة سیستم‌های تولیدی چندمحصولی و چنددوره‌ای در محیط جریان کارگاهی است؛ طوری که محدودیتهای تولید و توالی عملیات به صورت یکپارچه لحاظ شود. مدل برنامهریزی عدد صحیح مختلط برای مسئله پیشنهاد میشود. تابع هدف شامل هزینههای تولید، موجودی، کمبود، و راهاندازی است. با توجه به پیچیدگی زیاد محاسباتی، الگوریتم بهینه‌سازی گروه ذرات برای حل پیشنهاد میشود. جهت بررسی کارایی الگوریتم، دو روش بر پایة برنامهریزی عدد صحیح، که به صورت تکرارشونده با ایجاد مدلهای کوچکتر به حل مدل میپردازد، پیشنهاد و نتایج با هم مقایسه میشود. به علاوه، روش تاگوچی برای تنظیم پارامترهای روش فراابتکاری به کار می‌رود. الگوریتمهای حل موردنظر ترکیبی شدنی و نزدیکبهبهینه از برنامهریزی تولید و زمانبندی مییابند. نتایج، بر مجموعهای از مسائل با اندازههای مختلف، کارایی روش فراابتکاری را نسبت به حل دقیق و روشهای ابتکاری ثابت میکند. متوسط مقدار هدف برای روشهای 1PSO، ابتکاری 1، و ابتکاری 2 به ترتیب 21/98، 20/104، و 29/108(103×) است. | ||
| کلیدواژه‌ها [English] | ||
| الگوریتم بهینه‌سازی گروه ذرات, تعیین اندازة انباشته و زمانبندی یکپارچه, روش ابتکاری بر پایة برنامهریزی عدد صحیح مختلط, سیستم تولیدی چندمرحله‌ای, مدلسازی ریاضی | ||
| مراجع | ||
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