A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem
Expert Systems with Applications: An International Journal
An evolutionary squeaky wheel optimization approach to personnel scheduling
IEEE Transactions on Evolutionary Computation
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
A knowledge-based evolutionary assistant to software development project scheduling
Expert Systems with Applications: An International Journal
Lower bounds for the multi-skill project scheduling problem with hierarchical levels of skills
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
IEEE Transactions on Evolutionary Computation
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Multi-objective optimization model for multi-project scheduling on critical chain
Advances in Engineering Software
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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.