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A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems
المؤلفون المشاركون
Wong, Kuan Yew
Piroozfard, Hamed
Hassan, Adnan
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-04-12
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications.
The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex and NP-hard problems.
This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan.
In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules.
In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation.
A machine based precedence preserving order-based crossover was proposed to generate the offspring.
Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity.
In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library.
Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Piroozfard, Hamed& Wong, Kuan Yew& Hassan, Adnan. 2016. A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems. Journal of Optimization،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1110153
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Piroozfard, Hamed…[et al.]. A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems. Journal of Optimization No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1110153
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Piroozfard, Hamed& Wong, Kuan Yew& Hassan, Adnan. A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems. Journal of Optimization. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1110153
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1110153
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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