دانلود مقاله ISI انگلیسی شماره 27218
عنوان فارسی مقاله

الگوریتم تکاملی برای برنامه ریزی عملیات پیشرفته و زمان بندی در چند کارخانه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
27218 2005 15 صفحه PDF سفارش دهید 5210 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Evolutionary algorithm for advanced process planning and scheduling in a multi-plant
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computers & Industrial Engineering, Volume 48, Issue 2, March 2005, Pages 311–325

کلمات کلیدی
- برنامه ریزی فرایند و برنامه ریزی - زنجیره ای چند بوته - با محدودیت مقدم - الگوریتم تکاملی -
پیش نمایش مقاله
پیش نمایش مقاله  الگوریتم تکاملی برای برنامه ریزی عملیات پیشرفته و زمان بندی در چند کارخانه

چکیده انگلیسی

Integration of process planning and scheduling is one of the most important functions to support flexible planning in a multi-plant. The planning and scheduling are actually interrelated and should be solved simultaneously. In this paper, we propose an advanced process planning and scheduling model for the multi-plant. The objective of the model is to decide the schedules for minimizing makespan and operation sequences with machine selections considering precedence constraints, flexible sequences, and alternative machines. The problem is formulated as a mathematical model, and an evolutionary algorithm is developed to solve the model. Numerous experiments are carried out to demonstrate the efficiency of the proposed approach.

مقدمه انگلیسی

Many manufacturers now try to optimize the total system to cope with a global manufacturing. This trend brings the idea of supply chain, which is to optimize not only the plant operations but also the whole activities from a supplier to a customer. As a result, manufacturing companies nowadays are migrating from separated planning processes toward the more coordinated and integrated planning processes to provide high quality products at lower cost. Integration of process planning and scheduling is one of the most important problems for supporting the total optimization. The two functions are interrelated because both of them take part in the assignment of factory machines to production tasks. Hence, the actual process planning and scheduling problem should be solved concurrently, but the problem has more complexities due to the alternative machines and alternative operations sequences. We define the problem as an advanced process planning and scheduling problem (APPS). Therefore, the APPS should focus upon the following issues: (1) How to make a flexible process plan considering shop floor status and design information? (2) How to make an efficient schedule considering the job shop's dynamic situations and the complexity of the machine constraints? (3) How to make an appropriate integrated model which includes various constraints? In the traditional approaches, process planning and scheduling are done sequentially, where the process plan is determined before the actual scheduling is performed. But, this simple approach ignores the relationship between scheduling and process planning. Recently, some research results for the integrated process planning and scheduling are presented. Tan (2000) presents a review of the research in the process planning and scheduling area and discusses the extent of applicability of various approaches. Hankins, Wysk, and Fox (1984) discuss the advantages of using alternative operations sequences to improve the productivity of the shop floor. They show that the efficient planning considering the alternative machines results in reduced lead-time and in improved overall machine utilization. Nasr and Elsayed (1990) present two heuristics to determine an efficient schedule for the n jobs, m machines problem with alternative machine routings for each operation. The objective they adopted is to minimize the mean flow time. Palmer, 1996 and Sundaram and Fu, 1988 solve the IPPS problem using a simulated annealing. Brandimarte and Calderini (1995) develop a two-phase hierarchical tabu search. Saygin and Kilic (1999) propose a frame for IPPS with the objective of reducing the completion time. To solve the problem, a heuristic method is proposed. Morad and Zalzala (1999) develop an evolutionary algorithm (EA)-based method to tackle the IPPS problem. However, the major weakness of the models so far introduced lies in that they consider the alternative machines for each operation with a fixed sequence or the non-constraint operational sequence when constructing a schedule. In this paper, we consider an APPS problem for a multi-plant composed of a network of production facilities, and of multiple products flow through manufacturers. The multi-plant means extending the integration concept beyond on production site by means of stronger distribution management capabilities, electronic data interchange, and coordinated multiple plant management. We develop a model incorporating the alternative machines in the chain. The alternative machines have different capabilities and require unequal processing time for an operation. The operations sequences for each job include precedence constraints. In order to obtain good approximate solutions, we develop an EA-based heuristic approach.

نتیجه گیری انگلیسی

In this paper, a model is developed to solve the APPS problem in multiple plants chain composed of a network of production facilities, of multiple product flow through two manufacturers. The objective is to determine optimal schedule of machine assignments and operations sequences of all parts so that the makespan is minimized. The problem is formulated as a mixed integer programming model considering part mix, an unordered set of machining, alternative machines for each operation, machining times, and transportation times for all pairs of machines and inter plants. To solve the problem, an EA approach is developed. To demonstrate the efficiency of the proposed EA approach on the APPS problem, the numerical experiment is carried out. From the results of the experiments, we see that the population size and the number of generations are the main factors that affect the performance of the EA approach in the integrated problem. We find that the EA approach can find a good solution with very high probability.

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