Mathematical programs to schedule service employees at minimum cost represent each feasible schedule, or tour, with an integer variable. In some service organizations, policies governing employee scheduling practices may permit millions of different tours. A common heuristic strategy is to reformulate the problem from a small working subset of the feasible tours. Solution quality depends on the number and types of schedules included in the model. This paper describes a working subset heuristic based on column generation. The method is general and can accommodate a mix of full- and part-time employees. Experiments revealed its formulations had objective values indistinguishable from those of models using all feasible tours, and significantly lower than those generated by alternative working subset procedures.column generation, staffing and scheduling, service operations

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The U.S. service sector loses 2.3% of all scheduled labor hours to unplanned absences, but in some industries, the total cost of unplanned absences approaches 20% of payroll expense. The principal reasons for unscheduled absences (personal illness and family issues) are unlikely to abate anytime soon. Despite this, most labor scheduling systems continue to assume perfect attendance. This oversight masks an important but rarely addressed issue in services management: how to recover from short-notice, short-term reductions in planned capacity.In this article, we model optimal responses to unplanned employee absences in multiserver queueing systems that provide discrete, pay-per-use services for impatient customers. Our goal is to assess the performance of alternate absence recovery strategies under various staffing and scheduling regimes. We accomplish this by first developing optimal labor schedules for hypothetical service environments with unreliable workers. We then simulate unplanned employee absences, apply an absence recovery model, and compute system profits.Our absence recovery model utilizes recovery strategies such as holdover overtime, call-ins, and temporary workers. We find that holdover overtime is an effective absence recovery strategy provided sufficient reserve capacity (maximum allowable work hours minus scheduled hours) exists. Otherwise, less precise and more costly absence recovery methods such as call-ins and temporary help service workers may be needed. We also find that choices for initial staffing and scheduling policies, such as planned overtime and absence anticipation, significantly influence the likelihood of successful absence recovery. To predict the effectiveness of absence recovery policies under alternate staff ing/scheduling strategies and operating environments, we propose an index based on initial capacity reserves.

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