Int. J. Operational Research, Vol. 22, No. 1, 2015 31 Copyright © 2015 Inderscience Enterprises Ltd. An application of a generalised assignment problem: assigning recruiters to geographical locations Alan McKendall* and Wafik Iskander Department of Industrial and Management Systems Engineering, West Virginia University, 325A Mineral Resources Building, Morgantown, WV 26506, USA Email: Alan.McKendall@mail.wvu.edu Email: Wafik.Iskander@mail.wvu.edu *Corresponding author Sherron McKendall and Ann Chester Health Science and Technology Academy, Robert C. Byrd Health Sciences Center, West Virginia University, Morgantown, WV 26506, USA Email: smckendall@hsc.wvu.edu Email: achester@hsc.wvu.edu Abstract: In order to increase the number of underrepresented students pursuing college degrees in health sciences fields in the state of West Virginia, the Health Sciences and Technology Academy (HSTA), a pre-college enrichment programme, was established. Due to a limited budget, a limited number of recruiters are available to recruit as many West Virginia High School students who satisfy the programme’s selection criteria. As a result, recruiters are assigned to geographical locations (populations of potential HSTA students) such that the total value of the student populations assigned is maximised with respect to the programme selection criteria. This problem is defined as a generalised assignment problem (GAP), since more than one student population can be assigned to a recruiter such that the capacity of the recruiter is not exceeded. In this paper, a mathematical model, a construction algorithm, and a tabu search heuristic are presented for the proposed GAP. Keywords: generalised assignment problem; GAP; tabu search; meta-heuristic; integer programme; adjacency constraint. Reference to this paper should be made as follows: McKendall, A., Iskander, W., McKendall, S. and Chester, A. (2015) ‘An application of a generalised assignment problem: assigning recruiters to geographical locations’, Int. J. Operational Research, Vol. 22, No. 1, pp.31–47. Biographical notes: Alan McKendall is an Associate Professor in the Department of Industrial and Management Systems Engineering at West Virginia University. His main research interest is in developing efficient algorithms for optimisation problems such as logistics, sequencing, and scheduling problems.