An investigation of shift and break flexibility with real-time break assignments using a rolling horizon approach

Hur, Youngbum; Bard, Jonathan F.; Frey, M.; Kiermaier, Ferdinand

doi:10.1007/s10696-018-9305-2

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…The second phase then iteratively schedules breaks for each shift. Hur et al (2019) propose five different integer programming models for shift selection and staffing in which breaks may be scheduled in real-time while minimizing understaffing or maximizing the number of breaks which may be assigned. Additionally, a rolling horizon approach is proposed designed to accommodate short-term demand forecasts in order to efficiently manage fluctuations in demand.…”

Section: Related Workmentioning

confidence: 99%

Demand smoothing in shift design

2020

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Shift design is an essential step in workforce planning in which staffing requirements must be obtained for a set of shifts which best cover forecasted demand given as a demand pattern. Existing models for this challenging optimization problem perform well when these demand patterns fluctuate around an average without any strong variability in demand. However, when demand is irregular, these models inevitably generate solutions with a significant amount of over-or understaffing or an excessive use of short shifts. The present paper explores a strategy which involves modifying the demand patterns such that the variable workload may be better matched using an acceptable number of shifts. Integer programming is employed to solve the resulting optimization problem. A computational study of the proposed model reveals interactions between different problem parameters which control the scope of demand modification and the type of the selected shifts. Moreover, the potential impact from an economic point-of-view is discussed and the time before profitability of the approach is evaluated. These insights enable operations management to better understand the trade-off between solution quality and different types of flexibility which may be realized in an organization.

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Section: Related Workmentioning

confidence: 99%

Demand smoothing in shift design

2020

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“…Scheduling of meal and rest breaks is not often tackled in the airline crew scheduling literature. Recently, Hur et al [15], [16] investigate a daily shift scheduling problem with flexible breaks under stochastic demand that allows for break adjustments on the day of operation. Kiermaier et al [17] studies the problem of assigning multiple breaks to shifts in the context of large-scale tour scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where [Z − η] + := max{0, Z − η},α ∈ (0, 1]. With the term ([Z − η] + ) in the linearization formulation (16) and the assumption that the probability distribution space is discrete finite, the conditional value-at-risk of the random variable Z at the confidence level α can be rewritten as:…”

Section: B Risk-averse Modelmentioning

confidence: 99%

Stochastic Check-in Employee Scheduling Problem

et al. 2020

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This work addresses the problem of assigning airline check-in employees to tasks related to departing flights under uncertain circumstance at an international terminal of a large airport. The uncertainty of flight departing time mainly stems from delays and traffic control. Airlines specifics the minimum and target number of staff for each flight task in each time period. Taking uncertain tasks starting time into account and introducing risk measure (e.g., conditional value-at-risk) into optimization objective function, we present a risk-averse two-stage stochastic model with the mixed-integer recourse that minimizes both staffing costs and risk measurement to respond the time uncertainty. The first-stage decision is to make personal-area allocation plan; and the particular personnel-task scheduling decisions are determined in the second stage as the actual flight departing times are realized. To solve the NP-hard problem, a progressive hedging algorithm is proposed. Numerical experiments are carried out to demonstrate the efficiency and effectiveness of the proposed approach as compared with a sample average approximation method. Finally, we utilize a real-world case to illustrate how the proposed method can be used to obtain a near-optimal solution for practical problem. INDEX TERMS Employee scheduling, stochastic programming, airport ground optimization, risk measure, progressive hedging algorithm.

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