روش جدیدی برای برنامه ریزی تولید سیستم های تولید انعطاف پذیر با استفاده از یک الگوریتم ژنتیک چند هدفه کارآمد
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|15999||2005||9 صفحه PDF||سفارش دهید||5110 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Machine Tools and Manufacture, Volume 45, Issues 7–8, June 2005, Pages 949–957
In this paper, a novel approach using an efficient multi-objective genetic algorithm EMOGA is proposed to solve the problems of production planning of flexible manufacturing systems (FMSs) having four objectives: minimizing total flow time, machine workload unbalance, greatest machine workload and total tool cost. EMOGA makes use of Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. High efficiency of EMOGA arises from that multiple objectives can be optimized simultaneously without using heuristics and a set of good non-dominated solutions can be obtained providing additional degrees of freedom for the exploitation of resources of FMSs. Experimental results demonstrate effectiveness of the proposed approach using EMOGA for production planning of FMSs.
A flexible manufacturing system (FMS) is a production system consisting of a set of identical and/or complementary numerically controlled machines which are connected through an automated guided vehicle (AGV) system. Since FMS is capable of producing a variety of part types and handling flexible routing of parts instead of running parts in a straight line through machines, FMS gives great advantages through its flexibility such as dealing with machine and tool breakdowns, changes in schedule, product mix, and alternative routes. Flexible manufacturing is of increasing importance in advancing factory automation that keeps a manufacturer in a competitive edge. While FMS offers many strategic and operational benefits over conventional manufacturing systems, its efficient management requires solutions to complex product planning problems with multiple objectives and constraints. The aim of production planning is to develop a cost effective and operative production plan over planning phases. Decisions regarding production planning problems have to be made before the start of actual production, and consist of organizing the limited production resource constraints efficiently. Generally, production planning of FMSs consists of many optimization problems, such as routing optimization, equipment optimization and machine optimization .
نتیجه گیری انگلیسی
In this paper, a novel approach to production planning of flexible manufacturing systems (FMSs) using an efficient multi-objective genetic algorithm EMOGA is proposed. The investigated multi-objective production planning problem (MOPPP) has four objectives: minimizing total flow time, machine workload unbalance, greatest machine workload and total tool cost. The advantages of the proposed approach are that EMOGA can optimize multiple objectives without decomposing problems into sub-problems, and EMOGA makes use of Pareto dominance relationship to solve problems without using relative preferences of multiple objectives. While prior domain knowledge for the decomposition of problems or relative preferences of multiple objectives are not available, the proposed approach is an expedient method to solve production planning of FMSs, compared with the decomposition and preference-based approaches. In addition, the proposed approach can obtain a set of non-dominated solutions for decision makers in a single run. Decision makers can easily distinguish between the costs of different production plans and choose more than one satisfactory production plans at a time. These additional degrees of freedom could provide a better exploitation of the resources of FMSs. Experimental results demonstrated that the quality of non-dominated solutions obtained by EMOGA is better than that of SPEA in terms of convergence speed and accuracy using the same number of function evaluations. The results indicate that the proposed approach is a generalized and efficient approach to solving MOPPPS.