A multi-objective invasive weeds optimization algorithm for solving multi-skill multi-mode resource constrained project scheduling problem. (8th May 2016)

• Record Type: Journal Article
• Authors: Maghsoudlou, Hamidreza; Afshar-Nadjafi, Behrouz; Niaki, Seyed Taghi Akhavan
• Is Part Of: Computers & chemical engineering. Volume 88(2016)
• Page Start: 157
• Page End: 169
• Publication Date: 2016-05-08
• Subjects: Multi-skill -- Multi-mode -- RCPSP -- Time–cost–quality trade off -- MOIWO -- TOPSIS; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.02.018
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: The multi-objective and multi-skilled MRCPSP is considered. A parameters tuned invasive weeds optimization algorithm procedure is proposed. The effectiveness of the proposed method is investigated based on 30 test problems. Abstract: A new multi-skill multi-mode resource constrained project scheduling problem with three objectives is studied in this paper. The objectives are: (1) minimizing project's makespan, (2) minimizing total cost of allocating workers to skills, and (3) maximizing total quality of processing activities. A meta-heuristic algorithm called multi-objective invasive weeds optimization algorithm (MOIWO) with a new chromosome structure guaranteeing feasibility of solutions is developed to solve the proposed problem. Two other meta-heuristic algorithms called non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) are used to validate the solutions obtained by the developed MOIWO. The parameters of the developed algorithms are calibrated using Taguchi method. The results of the experiments show that the MOIWO algorithm has better performance in terms of diversification metric, the MOPSO algorithm has better performance regarding mean ideal distance, while NSGA-II algorithm has better performance in terms of spread of non-dominance solution and spacing metrics.