Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem
► We address a multi-objective project scheduling problem. ► Objectives: minimize project makespan and maximize effectiveness of human resources. ► To solve the problem, we propose a multi-objective hybrid algorithm. ► Integrating a simulated annealing algorithm and an evolutionary algorithm. ► The...
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| Veröffentlicht in: | Expert systems with applications Jg. 40; H. 7; S. 2421 - 2434 |
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01.06.2013
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | ► We address a multi-objective project scheduling problem. ► Objectives: minimize project makespan and maximize effectiveness of human resources. ► To solve the problem, we propose a multi-objective hybrid algorithm. ► Integrating a simulated annealing algorithm and an evolutionary algorithm. ► The proposed hybrid algorithm outperforms the existing algorithm for the problem.
In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm. |
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| AbstractList | In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm. ► We address a multi-objective project scheduling problem. ► Objectives: minimize project makespan and maximize effectiveness of human resources. ► To solve the problem, we propose a multi-objective hybrid algorithm. ► Integrating a simulated annealing algorithm and an evolutionary algorithm. ► The proposed hybrid algorithm outperforms the existing algorithm for the problem. In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm. |
| Author | Yannibelli, Virginia Amandi, Analía |
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| Keywords | Non-dominated solutions Multi-objective project scheduling Multi-objective evolutionary algorithm Multi-objective hybrid algorithm Multi-objective simulated annealing algorithm Human capital Adaptive algorithm Software development Evolutionary algorithm Project management Priority Adaptability Multiobjective programming Staff management Scheduling Makespan Optimization Search algorithm Human activity Genetic algorithm Simulated annealing Problem solving Hybrid model Human resource management |
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| Snippet | ► We address a multi-objective project scheduling problem. ► Objectives: minimize project makespan and maximize effectiveness of human resources. ► To solve... In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project... |
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| SubjectTerms | Algorithms Applied sciences Computer science; control theory; systems Decision theory. Utility theory Evolutionary algorithms Exact sciences and technology Expert systems Inventory control, production control. Distribution Multi-objective evolutionary algorithm Multi-objective hybrid algorithm Multi-objective project scheduling Multi-objective simulated annealing algorithm Non-dominated solutions Operational research and scientific management Operational research. Management science Optimization Scheduling Scheduling, sequencing Searching Simulated annealing Software Software engineering Tradeoffs |
| Title | Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem |
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