Vehicle Routing for Food Rescue Programs: A Comparison of Different Approaches

Gunes, Canan; Hoeve, Willem‐Jan van; Tayur, Sridhar

doi:10.1007/978-3-642-13520-0_21

Cited by 25 publications

(8 citation statements)

References 3 publications

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“…The operational challenges of food rescue organizations have received much attention. Nair et al (2018) and Gunes, van Hoeve, and Tayur (2010) study matching the donor and recipient with a routing problem. This is related to the more general problem of online matching (Karp, Vazirani, and Vazirani 1990;Mehta, Waggoner, and Zadimoghaddam 2015).…”

Section: Related Workmentioning

confidence: 99%

Improving Efficiency of Volunteer-Based Food Rescue Operations

Shi

Yao

et al. 2020

AAAI

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Food waste and food insecurity are two challenges that coexist in many communities. To mitigate the problem, food rescue platforms match excess food with the communities in need, and leverage external volunteers to transport the food. However, the external volunteers bring significant uncertainty to the food rescue operation. We work with a large food rescue organization to predict the uncertainty and furthermore to find ways to reduce the human dispatcher's workload and the redundant notifications sent to volunteers. We make two main contributions. (1) We train a stacking model which predicts whether a rescue will be claimed with high precision and AUC. This model can help the dispatcher better plan for backup options and alleviate their uncertainty. (2) We develop a data-driven optimization algorithm to compute the optimal intervention and notification scheme. The algorithm uses a novel counterfactual data generation approach and the branch and bound framework. Our result reduces the number of notifications and interventions required in the food rescue operation. We are working with the organization to deploy our results in the near future.

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Section: Related Workmentioning

confidence: 99%

Improving Efficiency of Volunteer-Based Food Rescue Operations

Shi

Yao

et al. 2020

AAAI

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“…Gunes et al. () focused on unsplittable supply and demand, where each node was visited once and Dror et al. () dealt with a homogeneous fleet, each being different than our OBVRP.…”

Section: Literature Reviewmentioning

confidence: 99%

Evacuating People with Mobility‐Challenges in a Short‐Notice Disaster

et al. 2015

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In past disasters, arrangements have been made to evacuate people without their own transportation, requiring them to gather at select locations to be evacuated. Unfortunately, this type of plan does not help those people who are unable to move themselves to the designated meeting locations. In the United States, according to the Post‐Katrina Emergency Management Reform Act of 2006, state or local governments have the responsibility to coordinate evacuation plans for all populations. These include those with disabilities. However, few, if any, have plans in place for those who are mobility‐challenged. The problem of evacuating mobility‐challenged people from their individual locations in a short‐notice disaster is a challenging combinatorial optimization problem. In order to develop the model and select a solution approach, we surveyed related literature. Based on our review, we formulate the problem and develop an Ant Colony Optimization (ACO) algorithm to solve it. We then test two different versions of the ACO algorithm on five stylized datasets with several different parameter settings.

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“…Some problems consider a global scale with time measured in days and weeks [7], while others focus on the minute-by-minute details of delivering supplies from local warehouses directly to the survivors [6,8,1,12]. This paper considers the so-called "last mile" of distribution which involves warehouse selection and customer delivery at the city and state scales.…”

Section: Non-standard Objective Functions -A Makespan Time Objective Inmentioning

confidence: 99%

“…This paper considers the so-called "last mile" of distribution which involves warehouse selection and customer delivery at the city and state scales. Humanitarian logistics applications have been mostly formulated as mixed integer programming (MIP) models, which often do not scale to real-world instances [8,6,12]. Moreover, MIP solvers have been shown to have severe difficulties with some of their unique features even when problem sizes are small (e.g., minimizing the latest delivery time in VRPs [10]).…”

Section: Non-standard Objective Functions -A Makespan Time Objective Inmentioning

confidence: 99%