Project scheduling with multiple modes: A genetic algorithm

In this paper we consider the resource-constrained project scheduling probl em with multiple execution modes for each activity and makespan minimizatio n as objective. We present a new genetic algorithm approach to solve this p roblem. The genetic encoding is based on a precedence feasible list of acti vities and a mode assignment. After defining the related crossover, mutatio n, and selection operators, we describe a local search extension which is e mployed to improve the schedules found by the basic genetic algorithm. Fina lly, we present the results of our thorough computational study. We determi ne the best among several different variants of our genetic algorithm and c ompare it to four other heuristics that have recently been proposed in the literature. The results that have been obtained using a standard set of ins tances show that the new genetic algorithm outperforms the other heuristic procedures with regard to a lower average deviation from the optimal makesp an.