Taking advantage of solving the resource constrained multi-project scheduling problems using multi-modal genetic algorithms

• Published in: Soft computing (Berlin, Germany) Vol. 20; no. 5; pp. 1879 - 1896
• Main Authors: Pérez, E., Posada, M., Lorenzana, A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2016; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Critical path; Effectiveness; Engineering; Genetic algorithms; Heuristic; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Optimization; Project management; Resource scheduling; Robotics; Scheduling; Multi-modal problems; Multi-project management; Niching algorithms; Genetic algorithms
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-015-1610-z

In this paper, for the first time, multi-modal genetic algorithms (MMGAs) are proposed to optimize the resource constrained multi-project scheduling problem (RCMPSP). In problems where the landscape has both multiple local and global optima, such as the RCMPSP, a MMGAs approach can provide managers with an advantage in decision-making because they can choose between alternative solutions equally good. Alternative optima are achieved because the diversification techniques of MMGAs introduce diversity in population, decreasing the possibility of the optimization process getting caught in a unique local or global optimum. To compare the performance of a MMGAs approach with other alternative approaches, commonly accepted by researchers to solve the RCMPSP such as classical genetic algorithms and dispatching heuristics based on priority rules, we analyse two time-based objective functions (makespan and average percent delay) and three coding systems [random keys (RK), activity list (AL), and a new proposal called priority rule (PR)]. We have found that MMGAs significantly improve the efficacy (the algorithm’s capability to find the best optimum) and the multi-solution-based efficacy (the algorithm’s capability to find multiple optima) of the other two approaches. For makespan the PR is the best code in terms of the efficacy and multi-solution-based efficacy , and the RK is the best code for the average percent delay.