Investigation Of Genetic Operators And Priority Heuristics for Simulation Based Optimization Of Multi-Mode Resource Constrained Multi-Project Scheduling Problems (MMRCMPSP)

Abstract

Solving NP-hard Problems like Multi-Mode Resource Constrained Multi-Project Scheduling Problems (MMRCMPSP) needs efficient search and optimization strategies. The combination of different approaches such as a meta-heuristic (Genetic Algorithm) for the mode assignment and a Heuristic (Priority Rules) for the job selection allows a 2-step solving-process. In this paper, we present such an approach for solving MMRCMPSP implemented with a simulation-based optimization tool. We investigate the influence of specific parameters of the algorithm to figure out which parameters mostly affect the result of MMRCMPSP.

Estimation of Distribution Algorithm for solving the Multi-mode Resource Constrained Project Scheduling Problem

“…Considering the complexity involved in solving these problems, many authors make use of soft computing methods, especially meta-heuristics techniques [2,21]. Some authors [22], [23] propose algorithms based on Particle Swarm Optimization (PSO), others propose techniques based on Tabu Search [24], [25], while the most used meta-heuristic is the based on Genetic Algorithms (GA) [26], [27], [16], [28]. Ayodele [29] [30] and collaborators apply an Estimation of Distribution Algorithm (EDA), but based on static learning, where new individuals are generated from exploring the most promising areas in the search space, based on the distribution of the best individuals of the previous generation.…”