Modeling for the equitable and effective distribution of donated food under capacity constraints

Measuring access to food banks and food pantries: A scoping review of spatial analysis approaches

Food banks and food pantries are crucial in supporting access to food and addressing food insecurity for millions of people. This scoping review identified eighteen articles that applied spatial analysis approaches to measure access to food banks and food pantries. The review summarizes the methods and primary findings of these studies, and examines how these address different dimensions of food access. Findings suggest that the majority of studies measured the accessibility and availability dimensions of food access, and two addressed accommodation. Through a discussion of these studies' methods and broader literature on food environments, we highlight opportunities to integrate advanced geospatial and mixed methods to support an empirically grounded and broader understanding of food bank and pantry access in future research. This will yield a more holistic picture of food environments and provide practical implications for site selection, resource allocation, and food assistance operations.

A multi-period optimal distribution model of emergency resources for responding to COVID-19 under uncertain conditions

Ideally, optimal emergency resource allocation would have been vital for effective relief work during the COVID-19 outbreak. However, the suddenness of the epidemic and uncertainty of its spread added some difficulties to distributing emergency resources. First, this study introduces triangular fuzzy numbers to describe the uncertainty of supply and demand of emergency resources, and interval numbers to describe the time required for resource transportation under disaster conditions. To minimize the total delivery time and difference in the total satisfaction rate, this study constructs an optimal model for emergency resource distribution under uncertain conditions that considers both efficiency and equity. Subsequently, an improved genetic algorithm (IMGA) is proposed to obtain the optimal decision scheme. Finally, a case study on emergency resource distribution during the COVID-19 pandemic is conducted for model verification. The results demonstrate that the proposed model can improve the efficiency and effect of emergency resource distribution. The model allocates some emergency resources to each demand site during each emergency period, which can help avoid large losses caused by extreme shortages of resources at a certain demand point. The emergency resource allocation scheme considers the transportation time and degree of impact, which is beneficial for enhancing the flexibility of decision-making and practical applicability of distribution operations. A comparative analysis of the algorithms shows that the proposed IMGA is an effective method for managing emergency resource distribution optimization problems because it has higher solving efficiency, better convergence, and stronger stability. These findings can provide decision support for the optimal distribution of large-scale, multiperiod emergency resources during the COVID-19 pandemic.

Food bank operations: review of operation research methods and challenges during COVID-19

Food banks have played a crucial role in mitigating food insecurity in affluent countries for over four decades. Throughout the years, academics have researched food banks for a variety of operational problems, resulting in several research papers on the topic. However, despite significant academic interest, the operational challenges and optimization of food bank operations remain under-researched. This study aims to conduct a systematic literature review on food bank operations and provide evidence-based recommendations for addressing prevalent challenges, and provide decision-makers with practical recommendations. In addition, this investigation seeks to investigate the impact of the COVID-19 pandemic on food bank operations. We conducted a comprehensive analysis of academic publications on food bank operations using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in order to get a deeper comprehension of the problems confronting food bank operations. Using a keyword search strategy with the logical operators "AND" and "OR," two search methods were utilized to identify relevant articles on food bank operations management, supply chain, distribution, and production in our first search. In our second search, we discovered articles in the "Operations Research & Management Science" (OR &MS) category of Web of Science containing food bank-related keywords such as food charity, food donation, and food aid. The database searches yielded 246 hits, and the article content was scanned to eliminate irrelevant articles by removing non-English articles and duplicated studies, leaving 55 articles for further examination. Our extensive examination of Operations Research (OR) methodologies reveals that Mixed-Integer Linear Programming (MILP) models are the most commonly used methodology, followed by Linear Program (LP), Dynamic Program (DP), and Data Envelopment Analysis (DEA) techniques. The key findings of this study emphasize the operational challenges food banks encountered during and after the COVID-19 pandemic, including supply chain disruptions, increased demand, and volunteer shortages. To address these issues, effective solutions, including the management of food donations and volunteer scheduling, were proposed. Our findings have practical implications for decision-makers in food bank management, highlighting the importance of adopting evidence-based solutions. Finally, Limitations and prospective research directions in food bank management are discussed, with an emphasis on the need for ongoing research in this crucial area.

A systematic literature review of food banks’ supply chain operations with a focus on optimization models

Purpose

Food banks play an increasingly important role in society by mitigating hunger and helping needy people; however, research aimed at improving food bank operations is limited.

Design/methodology/approach

This systematic review used Web of Science and Scopus as search engines, which are extensive databases in Operations Research and Management Science. Ninety-five articles regarding food bank operations were deeply analyzed to contribute to this literature review.

Findings

Through a systematic literature review, this paper identifies the challenges faced by food banks from an operations management perspective and positions the scientific contributions proposed to address these challenges.

Originality/value

This study makes three main contributions to the current literature. First, this study provides new researchers with an overview of the key features of food bank operations. Second, this study identifies and classifies the proposed optimization models to support food bank managers with decision-making. Finally, this study discusses the challenges of food bank operations and proposes promising future research avenues.

Vaccine Allocation and Distribution: A Review with a Focus on Quantitative Methodologies and Application to Equity, Hesitancy, and COVID-19 Pandemic

This review focuses on vaccine distribution and allocation in the context of the current COVID-19 pandemic. The implications discussed are in the areas of equity in vaccine distribution and allocation (at a national level as well as worldwide), vaccine hesitancy, game-theoretic modeling to guide decision-making and policy-making at a governmental level, distribution and allocation barriers (in particular in low-income countries), and operations research (OR) mathematical models to plan and execute vaccine distribution and allocation. To conduct this review, we adopt a novel methodology that consists of three phases. The first phase deploys a bibliometric analysis; the second phase concentrates on a network analysis; and the last phase proposes a refined literature review based on the results obtained by the previous two phases. The quantitative techniques utilized to conduct the first two phases allow describing the evolution of the research in this area and its potential ramifications in future. In conclusion, we underscore the significance of operations research (OR)/management science (MS) research in addressing numerous challenges and trade-offs connected to the current pandemic and its strategic impact in future research.

Identifying future partner agencies: helping Brazos Valley Food Bank in the fight against food insecurity

Brazos Valley Food Bank (BVFB) is a non-profit organization in the Bryan-College Station area of Texas. It distributes food supplies through partner agencies and special programs to eradicate hunger in Brazos Valley. However, a big gap exists between the meals distributed by BVFB and the size of the food-insecure population. This research is motivated by BVFB's desire to reach more people by recruiting more sustainable partner agencies. We used Geographic Information Systems (GIS) to map food desert areas lacking access to nutritious food. We combined expert knowledge with multi-criteria decision-making (MCDM) to address the challenges and time consumption of manually identifying sustainable partner agencies for local food delivery. We identified evaluation criteria for all agencies based on BVFB managers' preferences using a qualitative approach, and then applied three quantitative decision-making models: the Weighted Sum Model (WSM), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Multi-criteria Optimization and Compromise Solution (VIKOR) models to obtain ranking results. We compared the quantitative models' rankings to BVFB managers' manual choices and discussed the impacts of our research. The key innovation of the research is to develop a mixed method by combining expert knowledge with mathematical decision models and GIS to support spatial decision making in food distribution. Although our results were specific to BVFB, these procedures can be applied to food banks in general. Future studies include finetuning our models to measure and address human biases, wider applications and more data collections.

A mathematical programming approach for equitable COVID-19 vaccine distribution in developing countries

Developing countries scramble to contain and mitigate the spread of coronavirus disease 2019 (COVID-19), and world leaders demand equitable distribution of vaccines to trigger economic recovery. Although numerous strategies, including education, quarantine, and immunization, have been used to control COVID-19, the best method to curb this disease is vaccination. Due to the high demand for COVID 19 vaccine, developing countries must carefully identify and prioritize vulnerable populations and rationalize the vaccine allocation process. This study presents a mixed-integer linear programming model for equitable COVID-19 vaccine distribution in developing countries. Vaccines are grouped into cold, very cold, and ultra-cold categories where specific refrigeration is required for their storage and distribution. The possibility of storage for future periods, facing a shortage, budgetary considerations, manufacturer selection, order allocation, time-dependent capacities, and grouping of the heterogeneous population are among the practical assumptions in the proposed approach. Real-world data is used to demonstrate the efficiency and effectiveness of the mathematical programming approach proposed in this study.

A Game Theoretic Framework for Surplus Food Distribution in Smart Cities and Beyond

Food waste is currently a major challenge for the world. It is the precursor to several socioeconomic problems that are plaguing modern society. To counter and to, simultaneously, stand by the undernourished, surplus food redistribution has surfaced as a viable solution. Information and Communications Technology (ICT)-mediated food redistribution is a highly scalable approach and it percolates into the lives of the masses far better. Even if ICT is not brought into the picture, the presence of food surplus redistribution in developing countries such as India is scarce and is limited to only a few of the major cities. The discussion of a surplus food redistribution framework under strategic settings is a less discussed topic around the globe. This paper aims to address a surplus food redistribution framework under strategic settings, thereby facilitating a smoother exchange of surplus food in the smart cities of developing countries and beyond. As ICT is seamlessly available in smart cities, the paper aims to focus the framework in these cities. However, this can be extended beyond the smart cities to places with greater human involvement.

A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.

Collaborative Organization Models for Sustainable Development in the Agri-Food Sector

It is widely recognized that the adoption of collaborative organization models in the agri-food sector can help farmers in rural areas to reach sustainable development goals. In any case, a holistic and coherent view of sustainability, organizational models and supporting technologies in the agri-food sector is still not present in the scientific literature. With this paper, we aim to fill this gap and to propose a framework that is useful to help scholars and practitioners in analyzing and designing sustainable Collaborative Networks in the agri-food sector

A Conceptual Framework to Analyze Food Loss and Waste within Food Supply Chains: An Operations Management Perspective

Alongside the development of the circular economy and sustainable food supply chains (FSCs), research on food loss and waste (FLW) reduction and prevention has drawn much attention from academia, practitioners, and governments. The significance of FLW has been highlighted in the literature due to its impact on society, economy, and the environment. We propose a conceptual framework to systematically examine FLW issues within FSCs in the field of operations management (OM). We discuss various types and distribution modes of FSCs where FLW occurs, definitions of FLW, the impacts of FLW, and measures to reduce FLW in the OM field. We further introduce the methodologies that have been applied in existing FLW studies. The contribution of this paper is threefold. First, it proposes a conceptual framework to identify FLW problems within FSCs. Second, it helps to comprehensively understand FLW occurrence and thus stimulate research focusing on FLW from different perspectives. Third, it motivates researchers to discuss FLW issues by applying different methodologies.

Weather Risk–Reward Contract for Sustainable Agri-Food Supply Chain with Loss-Averse Farmer

Compared with the traditional agri-food supply chain (AFSC) whose only goal is to maximize economic benefits, the sustainable agri-food supply chain (SAFSC) starts to attract more attention. Typical challenges faced by SAFSC development are unfair pricing of produce, yield uncertainty caused by adverse weather, as well as conflict and cooperation between stakeholders and sustainable activities of SAFSC. In this paper, we establish a two-echelon decision-making model consisting of a loss-averse farmer and a loss-neutral company. A guaranteed price mechanism is contrived to mitigate the effects of uncertain procurement price on the farmers’ profit. It is found that this mechanism can improve the sustainable investment level but fails to reach the optimal level of the SAFSC system. Thus, a risk–reward contract taking into account the weather index (temperature) and the degree of loss aversion is designed. Results show that this contract can settle the distortion of the sustainable investment level and effectively motivate farmers to participate in the sustainable agricultural practice. Furthermore, we derive the conditions on the contract parameters under which both the company and the farmer are motivated to exert efforts to stand by sustainable agricultural practice.