Degree Name

Master of Information Systems by Research


School of Economics and Information Systems - Faculty of Commerce


The aim of this paper is to present a new genetic algorithm approach for large scale multiple resource-constrained project-scheduling problems. This research area is very common in industry especially when a set of activities needs to be finished as soon as possible subject to two sets of constraints, (?) Precedence constraints, and (?) Resource constraints. The literature reveals that previous researches have developed numerous scheduling methods and techniques to overcome the complex nature of this problem. However, there are still no promising methods that guarantee optimal solutions as well as computational feasibility. Genetic Algorithms (GA) are very promising Artificial Intelligence approaches to this problem in terms of the computational feasibility and the quality of solutions. However, the most common models of GA are very difficult to implement in scheduling problems. On the other hand, using specific and proper design of GA can make scheduling problems tractable. The emphasis in this research is on investigating the complexity of scheduling problems and developing a new GA approach to solve this problem in such ways that the advantages of GA are fully utilized and the design of GA is based on the nature of scheduling problems. In order to make this research more practical and challenging, we extend the type of resources constraints to multiple types rather than only one resource type. Computational results are also reported for the most famous classical problems taken from the operational research literature.

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