 Two stochastic optimization methods for shift design with uncertain demandZhiying Wu, Guoning Xu, Qingxin Chen and Ning Mao Omega, 2023, vol. 115, issue C Abstract: The purpose of this paper is to investigate the shift design problem with a probability constraint on demand satisfaction and to design the corresponding stochastic model so that the staffing for each shift can cope with stochastic demand. To solve this stochastic model, we propose two solution methods: a method involving average sample approximation and a two-stage heuristic algorithm based on statistics with a greedy strategy. Numerical results show that both methods can solve the stochastic model of this paper well, and the sample average approximation method outperforms the two-stage heuristic algorithm in terms of cost optimization. However, as the number of scenarios used to approximate realistic situations increases, the superiority of the two-stage heuristic algorithm in terms of solution speed becomes progressively more significant. Keywords: Stochastic demand; Shift design; Probability constraint; Sample average approximation; Two-stage heuristic (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:115:y:2023:i:c:s0305048322001967 Ordering information: This journal article can be ordered from DOI: 10.1016/j.omega.2022.102789 Access Statistics for this article Omega is currently edited by B. Lev More articles in Omega from Elsevier |
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