Resource Allocation with Sigmoidal Demands: Mobile Healthcare Units and Service Adoption
نویسندگان
چکیده
Problem definition: Achieving broad access to health services (a target within the sustainable development goals) requires reaching rural populations. Mobile healthcare units (MHUs) visit remote sites offer these However, limited exposure, literacy, and trust can lead sigmoidal (S-shaped) adoption dynamics, presenting a difficult obstacle in allocating MHU resources. It is tempting allocate resources line with current demand, as seen practice. maximize long term, this may be far from optimal, insights into allocation decisions are limited. Academic/practical relevance: We present formal model of long-term optimization sum functions. develop optimal propose pragmatic methods for estimating our model’s parameters data available demonstrate potential approach by applying family planning MHUs Uganda. Methodology: Nonlinear functions machine learning, especially gradient boosting, used. Results: Although problem NP-hard, we provide closed form solutions particular cases that elucidate allocation. Operationalizable heuristic allocations, grounded insights, outperform allocations based on demand. Our estimation approach, designed interpretability, achieves better predictions than standard application. Managerial implications: Incorporating future evolution driven community interaction saturation effects, key maximizing Instead proportionally assigning more visits high group should prioritized. Optimal among prioritized aims at equalizing demand end horizon. Therefore, generally allocated where cumulative higher counterintuitively, often those currently lower. History: This paper has been accepted Manufacturing & Service Operations Management Special Section Responsible Research Management. Supplemental Material: The e-companion https://doi.org/10.1287/msom.2021.1020 .
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ژورنال
عنوان ژورنال: Manufacturing & Service Operations Management
سال: 2022
ISSN: ['1523-4614', '1526-5498']
DOI: https://doi.org/10.1287/msom.2021.1020