Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Ling, Haifeng; Su, Zheng-Lian; Jiang, Xunlin; Zheng, Yujun

doi:10.3390/healthcare9020126

Cited by 8 publications

(4 citation statements)

References 39 publications

(43 reference statements)

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“…Other optimization models considered the allocation and sharing of specific critical resources, such as ventilators [26] , [174] and ICU beds [175] . Additionally, medical supply chain networks [176] and medical resource allocation models have been developed and implemented to optimize resource [177] – [179] or patient transfer [27] . In [27] , the most optimal load sharing strategy (transfer of ventilators vs. transfer of patients requiring intensive care) was determined, where the minimized cost function was dependent on the number of ICU units above capacity.…”

Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

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This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

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Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

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“…For order selection optimization, we propose an evolutionary algorithm based on the WWO metaheuristic [50] that takes inspiration from shallow water wave models for solving optimization problems. In particular, WWO has demonstrated superior performance on a variety of selection problems that have same or similar structure of solution space [3,8,24,41,49,53]. In WWO, each solution is analogous to a wave and is assigned with a wavelength inversely proportional to the solution fitness.…”

Section: Water Wave Optimization For Order Selectionmentioning

confidence: 99%

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

Zhang

et al. 2021

Complex Intell. Syst.

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The last years have seen a rapid growth of the takeaway delivery market, which has provided a lot of jobs for deliverymen. However, increasing numbers of takeaway orders and the corresponding pickup and service points have made order selection and path planning a key challenging problem to deliverymen. In this paper, we present a problem integrating order selection and delivery path planning for deliverymen, the objective of which is to maximize the revenue per unit time subject to maximum delivery path length, overdue penalty, reward/penalty for large/small number of orders, and high customer scoring reward. Particularly, we consider uncertain order ready time and customer satisfaction level, which are estimated based on historical habit data of stores and customers using a machine-learning approach. To efficiently solve this problem, we propose a hybrid evolutionary algorithm, which adapts the water wave optimization (WWO) metaheuristic to evolve solutions to the main order selection problem and employs tabu search to route the delivery path for each order selection solution. Experimental results on test instances constructed based on real food delivery application data demonstrate the performance advantages of the proposed algorithm compared to a set of popular metaheuristic optimization algorithms.

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“…To mitigate the risks associated with shortages or medical supply expiration (supply risk), the government typically adopts a strategy of multiple joint purchases and reserves for various emergency medical supplies ( 1 , 2 ). Additionally, the government takes into account various factors such as supplier capacity (e.g., supply quantity, stability of continuous supply, and responsiveness), reputation, and the scale of disasters ( 3 ). In order to address the uncertainties of emergency medical supplies demand, the government proactively establishes flexible contracts with multiple suppliers ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-scenario flexible contract coordination for determining the quantity of emergency medical suppliers in public health events

Hou,

Zhang,

Zhang

2024

Front. Public Health

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Determining the optimal number of emergency medical suppliers for the government to contract with in the context of public health events poses a challenging problem. Having too many suppliers can result in increased costs, while having too few suppliers can potentially expose the government to supply risks. Striking the right balance between these two factors is crucial in ensuring efficient and reliable emergency response and management. This study examines the process of determining the appropriate number of suppliers in emergency medical supply chain. By incorporating option contracts and employing the total cost of government procurement as the objective function, the analysis focuses on the impact of relevant parameters on the optimal number of suppliers. Furthermore, the study investigates the optimal supplier quantities under different types of option contracts. The proposed decision model for determining the optimal number of suppliers in this paper considers three key factors: the supply risk associated with emergency medical supplies, the reserve cost of government procurement, and the responsiveness of emergency medical supplies. Additionally, a method is introduced for selecting the quantity of emergency medical suppliers based on flexible contracts. This approach offers a scientific foundation for the government to effectively address the challenge of supplier quantity selection when faced with risks related to shortages, expiration, and the combination of both.

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Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Cited by 8 publications

References 39 publications

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

Multi-scenario flexible contract coordination for determining the quantity of emergency medical suppliers in public health events

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