Sufficient Working Subsets for the Tour Scheduling Problem

Easton, Fred F.; Rossin, Donald F.

doi:10.1287/mnsc.37.11.1441

Cited by 67 publications

(49 citation statements)

References 23 publications

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“…For each staffing/scheduling problem we used a custom C++ program to create the set of all feasible tours and the marginal revenue matrix r ti . Due to the large number of feasible tours, we formulated each staffing/scheduling problem with a working subset of feasible tours that were selected by Easton and Rossin's (1991) column generation procedure. IBM's (2000) OSL version 3.0 provided the shadow prices for the column generation algorithm and also solved the final integer program.…”

Section: Experimental Design and Resultsmentioning

confidence: 99%

“…In general, the cardinality of the set of feasible tours increases with the amount of scheduling flexibility permitted by the organization's work rules. Mechanisms for efficiently generating feasible employee schedules from these parameters are discussed in Mabert and Watts (1982); Burns and Koop (1987); Easton and Rossin (1991); Jarrah, Bard, and deSilva (1994); Jacobs and Brusco (1996); and Brusco and Jacobs (1998); among others.…”

Section: Staffing/scheduling and Absence Recoverymentioning

confidence: 99%

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Schedule Recovery: Unplanned Absences in Service Operations*

Easton

Goodale

2005

Decision Sciences

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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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Section: Experimental Design and Resultsmentioning

confidence: 99%

Section: Staffing/scheduling and Absence Recoverymentioning

confidence: 99%

Schedule Recovery: Unplanned Absences in Service Operations*

Easton

Goodale

2005

Decision Sciences

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“…Thompson [22] provides a reference for several of these prior computational studies along with the problem sizes and solution techniques. Easton and Rossin [8] use a column generation approach for solving the LP relaxation of the set covering type model for the tour scheduling problem. Then, as a heuristic, they determine the best integer solution using only the shifts generated to optimize the linear relaxation.…”

Section: Introductionmentioning

confidence: 99%

Optimal shift scheduling: A branch-and-price approach

Mehrotra¹,

Murphy²,

Trick³

2000

Naval Research Logistics

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“…It covers both the days-off selection as well as the daily shift scheduling problem for a number of weekly working tours. To solve this complex optimization problem, different heuristic solution procedures based on linear programming and decomposition have been proposed (e.g., [18][19][20][21]). For a comparison of the approaches, see [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Decision Support Model for Scheduling Clinical Studies and Assigning Medical Personnel

Grunow

Günther

Yang

2004

Health Care Manage Sci

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Clinical studies for the development of new drugs in the pharmaceutical industry consist of a number of individual tasks which have to be carried out in a pre-defined chronological order. Each task requires certain types of medical personnel. This paper investigates the scheduling of clinical studies to be performed during a short-term planning horizon, the allocation of workforce between the studies, and the assignment of individual employees to tasks. Instead of developing a complex monolithic decision model, a hierarchical modelling approach is suggested. In the first stage, a compact integer optimization model is solved in order to determine the start-off times of the studies and the required staffing while taking the limited availability of personnel into account. The objective is to minimize total staffing costs. The assignment of individual employees to tasks is then made in the second stage of the procedure using a binary optimization model.

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Sufficient Working Subsets for the Tour Scheduling Problem

Cited by 67 publications

References 23 publications

Schedule Recovery: Unplanned Absences in Service Operations*

Schedule Recovery: Unplanned Absences in Service Operations*

Optimal shift scheduling: A branch-and-price approach

Development of a Decision Support Model for Scheduling Clinical Studies and Assigning Medical Personnel

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