Solving resource constrained multiple project scheduling problems by random key‐based genetic algorithm

Abstract

SUMMARYIn this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-rkGA) to solve the resourceconstrained multiple project scheduling problem (rcmPSP) which is well known as an NP-hard problem and the objective in this paper is to minimize total complete time in the project. It is difficult to treat the rc-mPSP problems with traditional optimization techniques. The new approach proposed is based on the hybrid genetic algorithm (flc-rkGA) with fuzzy logic controller (FLC) and random-key encoding. For these rc-mPSP problems, we demonstrate that the proposed flc-rkGA to solve the rc-mPSP problem yields better results than several heuristic genetic algorithms presented in the computation result.

An Effective Multi-objective EDA for Robust Resource Constrained Project Scheduling with Uncertain Durations

“…3. Comparison of coverage measure by by moEDA, awGA, NSGA-II, SPEA2 and awGA compared with adaptive weight Genetic Algorithm (awGA) proposed by Gen and Cheng [18] from hybrid GA [21] , adaptive GA [22] and random-key based GA [23] approaches, Non-dominated Sorting Genetic Algorithm II (NSGA-II) by Deb, et al [19] and Strength Pareto Evolutionary Algorithm 2 (SPEA2) by Zitzler, et al [20] . In this paper, the one large-scale instance j30 of 30 activities x 3 modes in psplib are adopted.…”