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A competitive genetic algorithm for resource‐constrained project scheduling
Author(s) -
Hartmann Sönke
Publication year - 1998
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/(sici)1520-6750(199810)45:7<733::aid-nav5>3.0.co;2-c
Subject(s) - computer science , mathematical optimization , job shop scheduling , genetic algorithm , heuristic , scheduling (production processes) , minification , algorithm , schedule , machine learning , artificial intelligence , mathematics , operating system
Abstract In this paper we consider the resource‐constrained project scheduling problem (RCPSP) with makespan minimization as objective. We propose a new genetic algorithm approach to solve this problem. Subsequently, we compare it to two genetic algorithm concepts from the literature. While our approach makes use of a permutation based genetic encoding that contains problem‐specific knowledge, the other two procedures employ a priority value based and a priority rule based representation, respectively. Then we present the results of our thorough computational study for which standard sets of project instances have been used. The outcome reveals that our procedure is the most promising genetic algorithm to solve the RCPSP. Finally, we show that our genetic algorithm yields better results than several heuristic procedures presented in the literature. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 733–750, 1998