Department of Industrial Engineering,Sharif University of Technology
In this paper, a genetic algorithm for solving a class of project scheduling problems, called Resource Investment Problems, is presented. Tardiness of the project is permitted with a defined penalty. The decision variables are the level of resources and the start times of the activities. The objective is to minimize the sum of resources and delay penalty costs, subject to the activities' precedence relations and some other constraints. A revised form of the Akpan heuristic method for this problem is used to find better chromosomes. Elements of the algorithm, such as chromosome structure, unfitness function, crossover, mutation, immigration and local search operations, are explained. The performance of this genetic algorithm is compared with that of other published algorithms for Resource Investment Problems. Also, more than 700 problems are solved using an enumerating algorithm and their optimal solutions are used for the performance tests of the genetic algorithm. The tests results are quite satisfactory.