HYBRID SFLA-TABU SEARCH ALGORITHM FOR OPTIMAL PROJECT SCHEDULING AND STAFFING
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Date
2019
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Volume Title
Publisher
12th AICTTRA Conference Proceedings Ile Ife.
Abstract
Planning a large scale software project involves the objectives of optimal ordering of a set of activities and an allocation of staff to activities. Current adopted method presents difficulty in reaching optimal good solutions when the two objectives are combined. This study proposes a hybrid SFLA-TABU search algorithm to solve the project scheduling and staffing problem with the two objective combined. The hybrid algorithm retains the framework of SFLA algorithm but employs the neighborhood structure method of tabu search and its avoidance of already explored area in the solution space to move towards optimal solution within the local memetic evolution. The algorithm was applied on three randomly generated problem instances representing small, medium and large sized problems. Results showed that the proposed algorithm was able to produce good optimal solutions with average fitness values 0.44, 0.56 and 0.15 in small, medium and large sized problems respectively. The hybrid algorithm outperformed the baseline algorithms in 100% of the problem instances and findings from the experiment revealed theoretically, the scalability of the proposed approach in handling various sizes of software project.
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Keywords
Software Project Management, Software Project scheduling, Staffing, Optimization