الگوریتم های ژنتیکی برای برنامه ریزی نگهداری و تعمیرات پیشگیرانه یکپارچه و زمانبندی تولید برای یک دستگاه واحد
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22378||2005||8 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers in Industry, Volume 56, Issue 2, February 2005, Pages 161–168
Despite the inter-dependent relationship between them, production scheduling and preventive maintenance planning decisions are generally analyzed and executed independently in real manufacturing systems. This practice is also found in the majority of the studies found in the relevant literature. In this paper, heuristics based on genetic algorithms are developed to solve an integrated optimization model for production scheduling and preventive maintenance planning. The numerical results on several problem sizes indicate that the proposed genetic algorithms are very efficient for optimizing the integrated problem.
Production scheduling and preventive maintenance (PM) planning are among the most common and significant problems faced by the manufacturing industry. Production schedules are often interrupted by equipment failures, which could be prevented by proper preventive maintenance. However, recommended PM intervals are often delayed in order to expedite production. Despite the trade-offs between the two activities, they are typically planned and executed independently in real manufacturing settings even if manufacturing productivity can be improved by optimizing both production scheduling and PM planning decisions simultaneously. Numerous studies have been conducted in these two areas in the past decades. Shapiro  and Pinedo  reviewed various papers in production scheduling. Similarly, Sherif and Smith  and Dekker  reviewed several studies using maintenance optimization models. However, almost all relevant studies considered production scheduling and PM planning as two independent problems and therefore solve them separately. Only a few studies have tried to combine and solve both problems simultaneously. Graves and Lee  presented a single-machine scheduling problem with the objective to minimize the total weighted completion time of jobs. However, only one maintenance activity can be performed during the planning horizon. Lee and Chen  extended Graves and Lee's research to parallel machines, but still permitting only one maintenance action. Qi et al.  considered a similar single-machine problem with the possibility for multiple maintenance actions, but the risk of not performing maintenance is not explicitly included in the model. Cassady and Kutanoglu  developed an integrated mathematical model for a single-machine problem with total weighted expected completion time as the objective function. Their model allows multiple maintenance activities and explicitly captures the risk of not performing maintenance. In this paper, we develop genetic algorithm heuristics to solve the integrated production scheduling and preventive maintenance planning problem for a single machine introduced in Cassady and Kutanoglu . The following section, Section 2, contains an overview of the integrated production scheduling and PM planning problem. Section 3 briefly describes the proposed genetic algorithm procedures. The experimental results of multiple problem sizes appear in Section 4. The conclusions are summarized in Section 5.
نتیجه گیری انگلیسی
In this study, the genetic algorithm procedure is successfully applied to the integrated optimization model for production scheduling and preventive maintenance planning proposed by Cassady and Kutanoglu . Three GA-based algorithms are developed. Their performance is evaluated using multiple instances of small, medium, and large size problems. Based on the results, we conclude that the proposed genetic algorithms can be used to effectively solve the integrated problem. Future work includes the development of similar GA-based heuristics to solve extensions of this problem such as due-date related objective functions and multiple machine systems.