روش پذیرش حد آستانه مبتنی بر فهرست برای مشکلات برنامه ریزی تولید کارگاهی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|18905||2002||13 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 77, Issue 2, 21 May 2002, Pages 159–171
Process plants typically produce a family of related products that require similar processing techniques. The most important problem encountered in such manufacturing systems is scheduling of operations so that demand is fulfilled within a pre-described time horizon imposed by production planning. The typical scheduling operation that process plants involve can be formulated as a general job shop scheduling problem. The aim of this study is to describe a new metaheuristic method for solving the job shop scheduling problem of process plants, termed as list-based threshold accepting (LBTA) method. The main advantage of this method over the majority of other metaheuristics is that it produces quite satisfactory solutions in reasonable amount of time by tuning only one parameter of the method. This property makes the LBTA a reliable and a practical tool for every decision support system designed for solving real life scheduling problems. The LBTA is described in detail, tested over classical benchmark problems found in literature and a while characteristic job shop scheduling case study for dehydration plant operations is presented.
Scheduling of process operations is of utmost importance for the operational integrity of a process plant. Scheduling operations focus on the customized problem of meeting demand in the form of orders that are related to the character of each plant. The majority of scheduling operations in process plants can be formulated in terms of job shop scheduling. Job shop problems are known to be problems of extreme computational complexity where exact algorithms (such as Branch and Bound techniques) are doomed to fail. An effective alternative is heuristic schemes. Nowadays, researchers strive to find modern local search heuristics, termed as metaheuristics, producing high quality solutions for large problems that unfortunately are of hard computational complexity for utilizing exact algorithms (that in turn guarantee optimality). The development of such a metaheuristic method for solving the job shop scheduling problems of process plants, termed as list-based threshold accepting (LBTA), is the main contribution of this paper. The LBTA belongs to the class of threshold accepting algorithms. Its main difference over a typical threshold-accepting algorithm is that the threshold values used in the implementation of the move acceptance criterion are determined by a list that is rejuvenated and adapted according to the topology of the solution space of the problem. In this paper, the LBTA method is described in detail and its performance and characteristic case studies for the job shop scheduling problem of dehydration plants are presented.
نتیجه گیری انگلیسی
Scheduling operations focus on the customized problem of meeting demand in the form of orders that are related to the character of each plant, and assigning jobs to machines so that certain precedence and operational constraints are satisfied. Scheduling operations in many manufacturing systems can be formulated in terms of job shop scheduling problem formulation that can be effectively solved by a new metaheuristic algorithm, termed as the LBTA algorithm. The main advantage of this algorithm over the majority of other metaheuristics is that it produces quite satisfactory solutions in reasonable amount of time by tuning only one parameter of the algorithm. This property makes this algorithm a reliable and practical tool for every decision support system developed for solving real life job shop scheduling problems. The algorithm was successfully tested in classical benchmark problems taken for the literature. A characteristic real-world case study from scheduling the operations of dehydration plants is presented to illustrate the effectiveness of the proposed approach.