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1.
Front Public Health ; 12: 1334583, 2024.
Article in English | MEDLINE | ID: mdl-38572003

ABSTRACT

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.


Subject(s)
Public Health
2.
Math Biosci Eng ; 20(9): 16212-16235, 2023 Aug 14.
Article in English | MEDLINE | ID: mdl-37920010

ABSTRACT

Third-party logistics companies face a challenging task in minimizing inventory transportation costs due to the complexities of managing numerous suppliers. Effectively optimizing costs becomes a formidable problem for such companies. This empirical research has yielded strategies for minimizing the inventory transportation cost specifically for company D. Through a rigorous optimization process, the findings presented in this paper demonstrate an average reduction of 7.18% in company D's inventory transportation cost. By jointly optimizing inbound logistics inventory transportation under VMI-TPL mode, this study extends the theory of supplier managed inventory and improves the inbound logistics mode. The results of this study can provide quantitative support and decision-making references for the project operation management of company D and similar enterprises.

3.
PLoS One ; 16(2): e0247566, 2021.
Article in English | MEDLINE | ID: mdl-33621257

ABSTRACT

After an earthquake, affected areas have insufficient medicinal supplies, thereby necessitating substantial distribution of first-aid medicine from other supply centers. To make a proper distribution schedule, we considered the timing of supply and demand. In the present study, a "sequential time window" is used to describe the time to generate of supply and demand and the time of supply delivery. Then, considering the sequential time window, we proposed two multiobjective scheduling models with the consideration of demand uncertainty; two multiobjective stochastic programming models were also proposed to solve the scheduling models. Moreover, this paper describes a simulation that was performed based on a first-aid medicine distribution problem during a Wenchuan earthquake response. The simulation results show that the methodologies proposed in this paper provide effective schedules for the distribution of first-aid medicine. The developed distribution schedule enables some supplies in the former time windows to be used in latter time windows. This schedule increases the utility of limited stocks and avoids the risk that all the supplies are used in the short-term, leaving no supplies for long-term use.


Subject(s)
Computer Simulation , Earthquakes , Emergencies , First Aid/methods , Personnel Staffing and Scheduling , Emergency Service, Hospital , Humans , Time Factors
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