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Abstract This study develops an integrated delivery assignment and route planning strategy for food banking operations, considering food supply and demand constraints, food item restrictions, and vehicle capacity constraints. A mixed‐integer linear model is formulated to maximize the total demand served and minimize the total travel cost imposed on delivery volunteers. An integrated solution algorithm is developed that includes Lagrangian relaxation and column generation. The algorithm decomposes the problem into assignment and routing components and solves each iteratively. The proposed methodology is applied to a case study in Wake County, NC. A series of sensitivity analyses are conducted to draw insights. The numerical results demonstrate the proposed methodology's capacity to solve complex problems in food delivery operations efficiently. 

Since the start of the COVID-19 pandemic, disruptions have been experienced in many supply chains, particularly in personal protective equipment, testing kits, and even essential household goods. Effective vaccines to protect against COVID-19 were approved for emergency use in the United States in late 2020, which led to one of the most extensive vaccination campaigns in history. We continuously collect data on vaccine allocation, shipment and distribution, administration, and inventory in the United States, covering the entire vaccination campaign. In this article, we describe some data sets that we collaborated to obtain. We are publishing the data and making them freely available to researchers, media organizations, and other stakeholders so that others may use the data to develop insights about the distribution and wastage of vaccines during the current pandemic or to provide an informed future pandemic response. This article gives an overview of vaccine distribution logistics in the United States, describes the data we obtain, outlines how they may be accessed and used by others, and describes some high-level analyses demonstrating some aspects of the data (for data collected during January 1, 2021–March 31, 2021). This article also provides directions for future research using the collected data. Our goal is two-fold: (i) We would like the data to be used in many creative ways to inform the current and future pandemic response. (ii) We also want to inspire other researchers to make their data publicly available in a timely manner. 

Abstract Connected autonomous vehicles (CAVs) have the potential to deal with the steady increase in road traffic while solving transportation related issues such as traffic congestion, pollution, and road safety. Therefore, CAVs are becoming increasingly popular and viewed as the next generation transportation solution. Although modular advancements have been achieved in the development of CAVs, these efforts are not fully integrated to operationalize CAVs in realistic driving scenarios. This paper surveys a wide range of efforts reported in the literature about the CAV developments, summarizes the CAV impacts from a statistical perspective, explores current state of practice in the field of CAVs in terms of autonomy technologies, communication backbone, and computation needs. Furthermore, this paper provides general guidance on how transportation infrastructures need to be prepared in order to effectively operationalize CAVs. The paper also identifies challenges that need to be addressed in near future for effective and reliable adoption of CAVs.