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Two stochastic optimization methods for shift design with uncertain demand
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DOI：10.1016/j.omega.2022.102789.png)
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Abstract
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The purpose of this paper is to investigate the shift design problem with a probability constraint on de-mand 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 situa-tions increases, the superiority of the two-stage heuristic algorithm in terms of solution speed becomes progressively more significant. (c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Stochastic demand
Shift design
Probability constraint
Sample average approximation
Two-stage heuristic
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