Determining the optimal food hub location in the fresh produce supply chain

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2024-08-13 DOI:10.1108/jm2-02-2024-0042

Houtian Ge, Jing Yi, Stephan J. Goetz, Rebecca Cleary, Miguel I. Gómez

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引用次数: 0

Abstract

Purpose

Using recent US regional data associated with food system operations, this study aims at building optimization and econometric models to incorporate varying influential factors on food hub location decisions and generate effective facility location solutions.

Design/methodology/approach

Mathematical optimization and econometric models have been commonly used to identify hub location decisions, and each is associated with specific strengths to handle uncertainty. This paper develops an optimization model and a hurdle model of the US fresh produce sector to compare the hub location solutions between these two modeling approaches.

Findings

Econometric modeling and mathematical optimization are complementary approaches. While there is a divergence between the results of the optimization model and the econometric model, the optimization solution is largely confirmed by the econometric solution. A combination of the results of the two models might lead to improved decision-making.

Practical implications

This study suggests a future direction in which model development can move forward, for example, to explore and expose how to make the existing modeling techniques easier to use and more accessible to decision-makers.

Social implications

The models and results provide information that is currently limited and is useful to help inform sustainable decisions of various stakeholders interested in the development of regional food systems, regional infrastructure investment and operational strategies for food hubs.

Originality/value

This study sheds light on how the application of complementary modeling approaches improves the effectiveness of facility location solutions. This study offers new perspectives on elaborating key features to encompass facility location issues by applying interdisciplinary approaches.

查看原文本刊更多论文

确定新鲜农产品供应链中的最佳食品枢纽位置

设计/方法/途径数学优化模型和计量经济模型已被普遍用于确定中心选址决策，这两种模型都具有处理不确定性的特定优势。本文建立了美国生鲜农产品行业的优化模型和障碍模型，以比较这两种建模方法的枢纽选址解决方案。虽然优化模型和计量经济模型的结果存在差异，但优化方案在很大程度上得到了计量经济方案的证实。本研究提出了模型开发的未来方向，例如，探索和揭示如何使现有的建模技术更易于使用，更便于决策者使用。社会影响该模型和结果提供了目前有限的信息，有助于为对区域粮食系统发展、区域基础设施投资和粮食枢纽运营战略感兴趣的各利益相关方的可持续决策提供信息。本研究提供了新的视角，通过应用跨学科方法，阐述了涵盖设施选址问题的关键特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

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.