Optimizing a bi-objective preemptive multi-mode resource constrained project scheduling problem: NSGA-II and MOICA algorithms
Subject Areas : Business and Marketing
Javad
Hasanpour
1
(Msc, Department of industrial Engineering, Quchan University of Advanced Technology, Quchan, Iran)
Mohammad
Ghodoosi
2
(Msc, Department of industrial Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran)
Zahra Sadat
Hosseini
3
(PhD Student of Industrial Engineering, Yazd University ,Yazd, Iran)
Keywords: Multi-objective Project Scheduling, Resource Constraint, Preemptive, Net Present Value, Meta-heuristic Algorithm,
Abstract :
The aim of a multi-mode resource-constrained project scheduling problem (MRCPSP) is to assign resource(s) with the restricted capacity to an execution mode of activities by considering relationship constraints, to achieve pre-determined objective(s). These goals vary with managers or decision makers of any organization who should determine suitable objective(s) considering organization strategies. We also introduce the preemptive extension of the problem which allows activity splitting. In this paper the preemption multi-mode resource-constrained project scheduling problem (P-MMRCPSP) with Minimum makespan and the maximization of net present value (NPV) has been considered. Since the considered model is NP-Hard, The performance of our proposed model is evaluated by comparison with two well-known algorithms; non-dominated sorting genetic algorithm (NSGA II), multi-objective imperialist competitive algorithm (MOICA). These metaheuristics have been compared on the basis of a computational experiment performed on a set of instances obtained from standard test problems constructed by the ProGen project generator, where, additionally, cash flows were generated randomly with the uniform distribution. Since the effectiveness of most meta-heuristic algorithms significantly depends on choosing the proper parameters. A Taguchi experimental design method (DOE) was applied to set and estimate the proper values of GAs parameters for improving their performances. The computational results show that the proposed MOICA outperforms the NSGA-II.