Browsing by Author "Batista, Ana"

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    Managing the unknown: A distributionally robust model for the admission planning problem under uncertain length of stay
    (2021) Batista, Ana; Pozo, David; Vera, Jorge
    The admission planning problem in the inpatient service aims to provide patient access and to guarantee expected levels of bed utilization. However, uncertainty in the patient's length of stay and bed availability challenge the accomplishment of that objective. This research addresses the off-line admission planning problem with uncertain length of stay. We study the coordinated decisions of scheduling and allocation for the patient-to-room admission problem assuming heterogeneous patient types and time-varying capacity. The objective is to maximize the weighted sum of the patient's admission benefit while reducing the cost of overstay. We present a distributionally robust optimization (DRO) framework that is distribution-free; it considers that known information is limited only to the first moment and the support set of the true probability distribution. The framework is robust against the infinite set of probability distribution functions that could represent the stochastic process of the patient's length of stay. To test the performance of the proposed DRO approach, we compared it with benchmark models employing a real data set from a public hospital in Chile. The results show that our approach outperforms the evaluated models in both reliability and computational efficiency. We provide insights to practitioners and hospital decision-makers to anticipate admission decisions while considering the randomness of the length of stay at the tactical-operational level.
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    Multiskilled personnel assignment problem under uncertain demand: A benchmarking analysis
    (2022) Augusto Henao, Cesar; Batista, Ana; Felipe Porto, Andres; Gonzalez, Virginia, I
    The personnel assignment problem in different service industries aims to minimize the staff surplus/shortage costs. However, uncertainty in the staff demand challenges the accomplishment of that objective. This research studies the personnel assignment problem considering uncertain demand and multiskilled workforce configured through a 2-chaining strategy. We develop a two-stage stochastic optimization (TSSO) approach to calculate the multiskilling requirements that minimize the training costs and the expected costs of staff surplus/shortage. Later, we evaluate and compare the performance of the TSSO approach solutions with the solutions of two alternative optimization approaches under uncertainty -robust optimization (RO) and closed-form equation (CF). These two alternative approaches were published in Henao et al. [1] and Henao et al. [2], respectively. In addition, we compare the performance of the TSSO approach solutions with the solution of the deterministic (DT) approach and the solutions of myopic multiskilling approaches. To make performance comparisons between the different approaches, we used both real and simulated data derived from a retail store operating in Chile. The results show that, for different demand variability levels, TSSO, RO, and CF always belong to the set of approaches with the lowest average total cost. That is, in this group, there are no statistical differences from one approach to another, so these approaches are the most cost-effective. We also provide insights to retail decision-makers for addressing two key aspects. First, the methodology allows to address two fundamental multiskilling issues: how much multiskilling to add and how it should be added. Second, it is provided understanding on how to select the most suitable approach according to the balance between the conservatism and the reliability associated with the solutions delivered by each approach. Finally, we identify some methodological challenges for future research, such as the evaluation of k-chaining strategies with k >= 2.