Two-stage stochastic programming approach for limited medical reserves allocation under uncertainties

Zhang, Yuwei; Li, Zhenping; Jiao, Pengbo; Zhu, Shen

doi:10.1007/s40747-021-00495-7

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Other key developments in this area include the introduction of a two-stage stochastic programming model designed to accommodate uncertainties inherent in medical waste management [41]; developing a two-stage optimization model that integrates economic, environmental, and social dimensions into designing a reverse logistics network [42]; and developing mixed-integer models to reduce vehicle dispatch and management costs inherent in medical waste management [1].…”

Section: Xin Et Al (2022) [26]mentioning

confidence: 99%

An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

Dabo,

Hosseinian-Far

2023

Logistics

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Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability.

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Section: Xin Et Al (2022) [26]mentioning

confidence: 99%

An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

Dabo,

Hosseinian-Far

2023

Logistics

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“…The objectives of optimization include distance [ 41 ], death number [ 42 ], risk [ 43 ], cost [ 31 ], and fairness [ 44 ]. The optimization methods include dynamic planning [ 45 ], random planning [ 46 ], and multi-objective planning [ 47 , 48 ]. In reality, multiple conflicting optimization objectives exist, namely, multi-objective optimization.…”

Section: Related Workmentioning

confidence: 99%

Patient allocation method in major epidemics under the situation of hierarchical diagnosis and treatment

Huang

Wang

et al. 2022

BMC Med Inform Decis Mak

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Objectives Patients are classified according to the severity of their condition and graded according to the diagnosis and treatment capacity of medical institutions. This study aims to correctly assign patients to medical institutions for treatment and develop patient allocation and medical resource expansion schemes among hospitals in the medical network. Methods Illness severity, hospital level, allocation matching benefit, distance traveled, and emergency medical resource fairness were considered. A multi-objective planning method was used to construct a patient allocation model during major epidemics. A simulation study was carried out in two scenarios to test the proposed method. Results (1) The single-objective model obtains an unbalanced solution in contrast to the multi-objective model. The proposed model considers multi-objective problems and balances the degree of patient allocation matching, distance traveled, and fairness. (2) The non-hierarchical model has crowded resources, and the hierarchical model assigns patients to matched medical institutions. (3) In the “demand exceeds supply” situation, the patient allocation model identified additional resources needed by each hospital. Conclusion Results verify the maneuverability and effectiveness of the proposed model. It can generate schemes for specific patient allocation and medical resource amplification and can serve as a quantitative decision-making tool in the context of major epidemics.

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“…In their recent work, Zhang et al [20] addressed the challenge of allocating limited medical reserves in the context of a public health emergency. It takes into account uncertainties in both demand and donated supplies, as well as the priorities of healthcare centers.…”

Section: Introductionmentioning

confidence: 99%

A Bilevel DEA Model for Efficiency Evaluation and Target Setting with Stochastic Conditions

Georgiou,

Kaparis,

Vretta

et al. 2024

Mathematics

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The effective allocation of limited resources and the establishment of targeted goals play a pivotal role in enhancing the overall efficiency of large enterprises and organizations. To achieve optimal organizational efficiency, managers seek dynamic strategies that adapt to the constraints of limited and uncertain historical data. This paper introduces an evaluation of organizational efficiency through a stochastic framework, employing a bilevel data envelopment analysis (DEA) approach. This decision-making process is centralized within a decision-making unit (DMU) overseeing subordinate decision-making units (subDMUs). Discrete scenarios, each associated with a realization probability, define the uncertain parameters in the bilevel DEA-based model. This stochastic approach allows for recourse actions upon scenario realization leading to an enhanced overall organizational strategy. Decision-makers acting within uncertain and dynamic environments can benefit from this research since it allows the investigation of efficiency assessment under alternative scenarios in the presence of volatility and risk. The potential impact of applying this methodology varies depending on the specific domain. Although, the context of this paper focuses on banking, in general, enhancing resource allocation and target setting under stochasticity, contributes to advancing sustainability across all its three dimensions (economic, environmental, social). As mentioned earlier, the practical application of our approach is demonstrated via a case study in the banking sector.

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Two-stage stochastic programming approach for limited medical reserves allocation under uncertainties

Cited by 8 publications

References 33 publications

An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

Patient allocation method in major epidemics under the situation of hierarchical diagnosis and treatment

A Bilevel DEA Model for Efficiency Evaluation and Target Setting with Stochastic Conditions

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