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European Journal of Operational Research
Volume 177, Issue 2, 1 March 2007, Pages 924-938
 
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doi:10.1016/j.ejor.2006.01.008    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Elsevier B.V. All rights reserved.

Production, Manufacturing and Logistics

Balancing staffing and switching costs in a service center with flexible servers

Rajan Battaa, Corresponding Author Contact Information, E-mail The Corresponding Author, Oded Bermanb, E-mail The Corresponding Author and Qian Wangc, E-mail The Corresponding Author

aDepartment of Industrial and Systems Engineering, University at Buffalo (SUNY), 342 Bell Hall, Buffalo, NY 14260, USA bJoseph L. Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, Ont., Canada M5S 3E6 cManagement School of Graduate University, Chinese Academy of Sciences, 80 Zhongguancun East Road, Haidian District, Beijing 100080, PR China

Received 12 October 2004; 
accepted 5 January 2006. 
Available online 2 March 2006.

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Abstract

In this paper, we study the problem of balancing staffing and switching costs in a service center with multiple types of customers and time-dependent service demand. We assume that servers are flexible and can be switched from serving one type of customers to another. The objective is to minimize the total staffing and switching costs subject to service level constraints. Column generation heuristics are developed based on a formulation from the perspective of servers. Computational experiments show that the heuristics perform well. Furthermore, an extension of the model is presented for the situation where there is a penalty if some of the service requests are not covered (satisfied).

Keywords: Workforce planning; Column generation

Article Outline

1. Introduction
2. Problem description and notation
3. Single server type with zero switching time
4. Formulation and column generation heuristics
4.1. Assignment path
4.2. Integer programming formulation
4.3. Lower bound through LP relaxation
4.3.1. Master problem
4.3.2. Pricing problem
4.3.3. Initial solution
4.3.4. Iteration strategy
4.4. Upper bound
4.4.1. Heuristic 1 (H1)
4.4.2. Heuristic 2 (H2)
5. Computational results
6. Extension to maximize service coverage
7. Summary and future work
Acknowledgements
Appendix A. Appendix
A.1. Formulation (P): from the perspective of groups
A.2. Formulation (PC): from the perspective of groups with penalty
References





 
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