考虑多用途能力,开发灾害条件下的综合血液供应链网络

IF 1.8 Q3 MANAGEMENT
Mohsen Jami, Hamidreza Izadbakhsh, Alireza Arshadi Khamseh
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引用次数: 0

摘要

目的 本研究旨在最大限度地降低灾害条件下整个血液供应链网络(BSCN)的血液输送成本和时间。换句话说,将血液采集、加工和配送三个层面的所有战略、战术和运营决策整合在一起,从而在适当的时间满足需求。建议的模型将所有永久性设施和应急设施的位置分为三个层次:血液采集、加工和分发。该模型还采用了其他重要决策,包括多功能设施、紧急运输、库存和分配。研究结果研究结果表明,该模型在降低总成本和缩短血液运送时间方面具有很高的效率,从而实现了血液供应链的低碳传输。他们考虑到设施的多用途能力(如野战医院负责血液采集、加工和分发三个用途),因此将永久性设施和应急设施定位在血液采集、加工和分发、辅助设施、应急运输和有污染的路线上的交通三个层面,从而提出了一个新模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing an integrated blood supply chain network in disaster conditions considering multi-purpose capabilities
Purpose This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time. Design/methodology/approach This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN. Findings The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain. Originality/value The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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