Ant colony optimization with parameter adaptation for multi-mode resource-constrained project scheduling

Authors:
Chuan-Wen Chiang;Yu-Qing Huang;Wen-Yen Wang
Affiliations:
(Corresponding author) Department of Computer and Communication Engineering, National Kaohsiung First University of Science and Technology, No. 2, Jhuoyue Rd., Nanzih District, Kaohsiung City 811, ...;Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, No. 2, Jhuoyue Rd., Nanzih District, Kaohsiung City 811, Taiwan;Department of Information Engineering, Kun Shan University, No. 949, Da-Wan Rd., Yung-Kang, Tainan County 71003, Taiwan
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Fuzzy theory and technology with applications
Year:
2008

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Abstract

An effective algorithm capable of solving the multi-mode resource-constrained project scheduling problem (MRCPSP) is an essential component for project planning and control since it can fully exploit the available resources and minimize the makespan of a given project. The MRCPSP is extremely complex and is known to be NP-hard in the strong sense. On the basis of the principles of ant colony optimization (ACO), we therefore propose a constructive-oriented iterative algorithm to acquire satisfactory solutions of the MRCPSP within a reasonable amount of computation time. The proposed algorithm, namely ACO-MRCPSP, attempts to identify a project schedule with minimum completion time without violating precedence and resource constraints. ACO-MRCPSP is characterized by its use of a self-adaptive parameter control strategy to guide artificial ants to effectively construct feasible solutions for the MRCPSP. The performance of the proposed algorithm is evaluated by comparing it against other existing metaheuristic implementations, such as simulated annealing (SA) and genetic algorithms (GAs), in terms of overall completion time for a set of project instances obtained form the Project Scheduling Library (PSPLIB). Experimental results indicate that ACO-MRCPSP is a significant improvement compared with the previous attempts at solving the MRCPSP.