Scatter search for trainees to software project requirements stable allocation

Mangesh Gharote (), Rahul Patil () and Sachin Lodha ()
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Mangesh Gharote: IIT Bombay
Rahul Patil: IIT Bombay
Sachin Lodha: TATA Consultancy Services Limited

Journal of Heuristics, 2017, vol. 23, issue 4, No 4, 257-283

Abstract: Abstract In this paper, we study a centralized, stable matching scheme, which allocates trainees to software project requirements, to minimize retraining and relocation costs when the preference lists of the project requirements may contain ties of arbitrary lengths. This particular trainees’ assignment problem is important because the allocation decisions not only influence the costs but also impact software project deliverables and intern attrition rates. It is also an NP-hard problem because of the inclusion of the ties, and the costs in the stable allocation model. We, therefore, have designed a GRASP-based scatter search method, to solve the large size instances of our assignment problem efficiently. The GRASP method uses randomized algorithms to generate initial trial solutions. A repair heuristic based on regret minimization idea is designed to convert an unstable solution to a stable solution during an improvement phase. Computational experiments suggest that the proposed algorithm significantly reduces run time compared to the CPLEX, and produces solutions that are at an average 4.5% away from the best CPLEX solutions for the large size problem instances. Moreover, our scatter search method consistently provides better quality solutions than the two state of the art methods from the prior literature.

Keywords: Workforce allocation in software projects; GRASP; Scatter search; Stable matching; Preferences with ties and cost objective (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10732-017-9343-z

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