عنوان مقاله
مدل عصبی-فازی برای طرح‌ریزی فرآیندهای یکپارچه ترکیبی جدید و سیستم‌های زمان‌بندی
فهرست مطالب
مقدمه
مدل یکپارچه برای سفارشی‌سازی انبوه
مسله یکپارچه‌سازی و برنامه‌کاربردی رویکرد GA
ایجاد شبکه عصبی مصنوعی با زمان‌بند بدست‌آمده از الگوریتم ژنتیک
طراحی تجربی و نتایج آن
نتیجه گیری
بخشی از مقاله
ایجاد شبکه عصبی مصنوعی با زمان‌بند بدست‌آمده از الگوریتم ژنتیک
درطول اجرای GA، درکنار زمان‌بند نهایی، داده تولیدشده مانند "زمان پردازش"، "بار ماشین"، " زمان پردازش باقی‌مانده"، "دنباله کار قبلی" ، و " دنباله ماشین قبلی" برای هر عملیات محاسبه و ذخیره می‌شود. نتایج ما نشان‌می‌دهند که از میان داده‌های موجود، "زمان پردازش"، "بار ماشین"، و " زمان‌باقی‌مانده پردازش" مهمترین هستند و باید به‌عنوان ورودی سیستم ANN بعد از زمان‌بندی انتخاب شوند.
کلمات کلیدی:
A neuro-fuzzy model for a new hybrid integrated Process Planning and Scheduling system Alper Seker a,⇑ , Serpil Erol b , Reha Botsali a a The Scientific and Technological Research Council of Turkey, Kavaklıdere, 221 06100 Ankara, Turkey bDepartment of Industrial Engineering, Gazi University, Maltepe, 06570 Ankara, Turkey article info Keywords: Process Planning Genetic Algorithm Scheduling Fuzzy Neural Network abstract In customized mass production, isolation of Process Planning (PP) and Scheduling stages has a critical effect on the efficiency of production. In this study, to overcome this isolation problem, we propose an integrated system that does PP and Scheduling in parallel and responds to fluctuations in job floor on time. One common problem observed in integration models is the increase in computational time in conjunction with the increase of problem size. Therefore in this study, we use a hybrid heuristic model combining both Genetic Algorithm (GA) and Fuzzy Neural Network (FNN). To improve GA performance and increase the efficiency of searching, we use a clustered chromosome structure and test the performance of GA with respect to different scenarios. Data provided by GA is used in constructing an FNN model that instantly provides new schedules as new constraints emerge in the production environment. Introduction of fuzzy membership functions in Artificial Neural Network (ANN) model allows us to generate fuzzy rules for production environment. 2013 Elsevier Ltd. All rights reserved. 1. Introduction Process Planning (PP) and Scheduling are production system components determining how and when to produce with respect to available resources. In today’s manufacturing environment, generally PP and Scheduling are considered isolated activities from each other, and consequently these two production activities are carried out by different departments in a factory. This isolation creates a large time gap between PP and Scheduling, which in turn decreases the total production efficiency. The work done up to date in the field of Scheduling and PP mostly focuses on mass production environment. On the other hand, the isolation of PP from Scheduling and the resulting time gap between these activities is a critical problem that requires more attention. Being inspired by this fact, in this study our objective is to provide a model that does PP and Scheduling simultaneously in a customized mass production environment. Integration of PP and Scheduling is an NP-hard (non-deterministic polynomial-time hard) problem and it is not possible to find the optimal solution in polynomial time. Generally, companies do not have the luxury of spending hours/days to iteratively plan production activities that dynamically change throughout the production process. Therefore, companies mostly prefer PP and Scheduling activities to be planned and coordinated by heuristics in reasonable time. To achieve coordination of PP and Scheduling activities, the necessity of reconfiguring PP and Scheduling departments arises. Tan and Khoshnevis (Tan & Khoshnevis, 2000) studied this issue, but their study has certain limitations. On the other hand, in the literature a more popular approach for this integration problem is to focus on the exchange of information between PP and Scheduling activities (Gaalman, Slomp, & Suresh, 1999; Gindy, Saad, & Yue, 1999; Guo et al., 2009; Shen, Wang, & Hao, 2006; Wang et al., 2009; Zhang, Saravanan, & Fuh, 2003). Process plans usually provide inputs to scheduling just after the product design is completed and this corresponds to an earlier time than the start of production. In the meantime, job floor conditions change dynamically because of several reasons such as replacement of old machines, crises, strikes, disruptions in the supply chain, etc. A survey on this issue shows that 30% of the process plans need to be revised just before the production plans (Detand, Kruth, & Kempenaers, 1992). For detailed process plans and schedules, alternative production routes should be defined and chosen, followed by planning of operations for the chosen routes. The purpose of this study is to provide efficient production plans that are generated for the integrated problem of PP and Scheduling. Determining the alternative operations, machines, and sequence of operations during production are all parts of this integrated problem. As another objective, we do not want to sacrifice system flexibility and computational time for solving this integrated problem. 0957-4174/$ - see front matter 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.eswa.2013.03.043 ⇑ Corresponding author. Address: The Scientific and Technological Research Council of Turkey, Atatürk Bulvarı No. 221, Kavaklıdere, 06100 Ankara, Turkey. Tel.: +90 312 468 53 00 4550. E-mail addresses: alper.seker@tubitak.gov.tr (A. Seker), serpiler@gazi.edu.tr (S. Erol), rehabotsali@gmail.com (R. Botsali).
