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International Journal of Supply and Operations Management، جلد ۲، شماره ۳، صفحات ۹۰۵-۹۲۴
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| عنوان فارسی |
An Efficient Genetic Agorithm for Solving the Multi-Mode Resource-Constrained Project Scheduling Problem Based on Random Key Representation |
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| چکیده فارسی مقاله |
In this paper, a new genetic algorithm (GA) is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP. |
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| کلیدواژه‌های فارسی مقاله |
Combinatorial Optimization ، Multi-mode project scheduling ، Resource constraints ، Genetic Algorithm ، Random key representation ، |
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| عنوان انگلیسی |
An Efficient Genetic Agorithm for Solving the Multi-Mode Resource-Constrained Project Scheduling Problem Based on Random Key Representation |
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| چکیده انگلیسی مقاله |
In this paper, a new genetic algorithm (GA) is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP. |
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| کلیدواژه‌های انگلیسی مقاله |
Combinatorial Optimization , Multi-mode project scheduling , Resource constraints , Genetic Algorithm , Random key representation , |
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| نویسندگان مقاله |
| mohammad hassan sebt
amirkabir university of technology, tehran, iran
سازمان اصلی تایید شده: دانشگاه صنعتی امیرکبیر (Amirkabir university of technology)
| mohammad reza afshar
amirkabir university of technology, tehran, iran
سازمان اصلی تایید شده: دانشگاه صنعتی امیرکبیر (Amirkabir university of technology)
| yagub alipouri
amirkabir university of technology, tehran, iran
سازمان اصلی تایید شده: دانشگاه صنعتی امیرکبیر (Amirkabir university of technology)
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| نشانی اینترنتی |
http://system.khu.ac.ir/ijsom/browse.php?a_code=A-10-100-58&slc_lang=en&sid=en |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
Artificial intelligence & expert system |
| نوع مقاله منتشر شده |
Research paper |
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