Learning-based scheduling of flexible manufacturing systems using ensemble methods
نویسندگان
چکیده
منابع مشابه
Scheduling of Flexible Manufacturing Systems Using Case-based Reasoning
A common method of dynamically scheduling jobs in Flexible Manufacturing Systems (FMSs) is to employ scheduling rules. However, the problem associated with this method is that the performance of the rules depends on the state of the system, but there is no rule that is superior to all the others for all the possible states the system might be in. It would therefore be highly desirable to employ...
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Dispatching rules are usually applied to schedule jobs in Flexible Manufacturing Systems (FMSs) dynamically. Despite their frequent use, one of the drawbacks that they display is that the state the manufacturing system is in dictates the level of performance of the rule. As no rule is better than all the other rules for all system states, it would highly desirable to know which rule is the most...
متن کاملLearning-Based Scheduling of Flexible Manufacturing Systems Using Case-Based Reasoning
A common method of dynamically scheduling jobs in Flexible Manufacturing Systems (FMSs) is to employ dis patching rules. However, the problem associated with this method is that the performance of the rules depends on the state of the system, but there is no rule that is superior to all the others for all the possible states the system might be in. It would therefore be highly desirable to empl...
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Scheduling of production in Flexible Manufacturing Systems (FMSs) has been extensively investigated over the past years and it continues to attract the interest of both academic researchers and practitioners. The generation of new and modified production schedules is becoming a necessity in today’s complex manufacturing environment. Genetic algorithms are used in this paper to obtain an initial...
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The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. Most of the scheduling algorithms are designed to be established in offline environment. However, we are challenged with three characteristics in real cases: First, problem data of jobs are not known in advance. Second, most of the shop’s parameters tend to be stochastic. Third, th...
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ژورنال
عنوان ژورنال: Computers & Industrial Engineering
سال: 2018
ISSN: 0360-8352
DOI: 10.1016/j.cie.2018.09.034