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hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem

引用

EXPERT SYSTEMS WITH APPLICATIONS 2013年第7期40卷 2421-2434页

作者： Yannibelli, Virginia Amandi, Analia UNCPBA Fac Cs Exactas ISISTAN Res Inst RA-7000 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn RA-1033 Buenos Aires DF Argentina

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. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective project scheduling multi-objective hybrid algorithm multi-objective simulated annealing algorithm multi-objective evolutionary algorithm Non-dominated solutions

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