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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Vydáno v:Expert systems with applications Ročník 40; číslo 7; s. 2421 - 2434
Hlavní autoři: Yannibelli, Virginia, Amandi, Analía
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier Ltd 01.06.2013
Elsevier
Témata:
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
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
ISSN:0957-4174, 1873-6793
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Shrnutí:► 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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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.10.058