Stochastic optimization models for location and inventory prepositioning of disaster relief supplies

Shehadeh, Karmel S.; Tucker, Emily L.

doi:10.1016/j.trc.2022.103871

Cited by 31 publications

(4 citation statements)

References 62 publications

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“…To achieve accurate identification and positioning of target box cargo in the real environment, the camera is used as the eye to achieve accurate detection of box cargo in the environment, in order to better complete subsequent steps such as positioning and picking up [9]. This article introduces machine vision technology and plans to use the RealSenseD435 depth camera to identify and locate the target box.…”

Section: Design Of Accurate Location Methods For Intelligent Warehous...mentioning

confidence: 99%

An accurate location method for intelligent warehouse materials based on RFID technology and machine vision

Pan,

Li,

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Due to the similarity between storage materials, there are errors and difficulties in controlling efficiency when positioning them. Therefore, a research on accurate positioning methods for intelligent storage materials based on RFID technology and machine vision is proposed. Machine vision technology was introduced, and the RealSenseD435 depth camera was used to identify the target box. The weighted average method was used to process the image, and the distribution of pixel gradient fields in the image was used as a feature parameter for the storage materials. In the positioning stage, RFID technology was introduced, using the extracted pixel gradient field of the warehouse material image as RFID tags. Based on the rigorous relationship between the phase and the distance from the warehouse material, the position of the target material was determined. In the test results, the positioning error of the design method for different types of materials remained stable within 0.15m, and the time cost remained stable within 10.0m.

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Section: Design Of Accurate Location Methods For Intelligent Warehous...mentioning

confidence: 99%

An accurate location method for intelligent warehouse materials based on RFID technology and machine vision

Pan,

Li,

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…Among the recent contributions, Shehadeh and Tucker [31] focused on determining the location of humanitarian aid warehouses in both the pre-and post-disaster phases, considering uncertainty, and positioning inventory. Seraji et al [32] optimized the placement of emergency shelters and coordinated the movement of aid vehicles using two-stage multi-objective metaheuristics.…”

Section: Literature Reviewmentioning

confidence: 99%

Quick-Response Model for Pre- and Post-Disaster Evacuation and Aid Distribution: The Case of the Tula River Flood Event

Santana-Robles,

Hernández-Gress,

Martínez-López

et al. 2024

Logistics

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In humanitarian logistics, efficiently evacuating people during disasters poses a significant challenge. Comprehensive plans are essential for moving individuals from affected areas, encompassing both pre- and post-disaster phases. These plans must optimize resource usage, including public transportation, and consider those with mobility challenges. This paper focuses on evacuating and assisting individuals in temporary shelters, specifically in the Tula River region, Hidalgo State, Mexico. Despite the recurring nature of this challenge, there is a noticeable gap in the scientific literature addressing quick-response methods for decision-makers to adapt existing spaces as temporary shelters and efficiently evacuate people from risk areas. To bridge this gap, we introduce a methodology aiming to minimize evacuation and aid distribution costs. Leveraging established algorithms like integer linear programming, the model determines shelter activation, while the vehicle routing problem assesses aid delivery strategies. Our research identified optimal evacuation routes from 13 affected areas to 34 shelters and aid distribution from collection centers to affected zones. We analyzed the average evacuation times for different demand scenarios: original, increased by 10%, and decreased by 10%, considering the transport units allocated and the distances on Google Maps. This study also evaluates the costs associated with humanitarian aid distribution under varying collection strategies involving state and municipal governments.

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“…In [9], the authors also propose a robust optimization framework for selecting routes to restore in efforts to accommodate humanitarian relief operations. Another task that has attracted interest from the robust optimization community is the pre‐positioning and locating of inventory that can be used to provide relief to affected areas [54, 57].…”

Section: Literature Review and Contributionsmentioning

confidence: 99%

A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

Faiz,

Vogiatzis,

Liu

et al. 2024

Networks

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Providing first aid and other supplies (e.g., epi‐pens, medical supplies, dry food, water) during and after a disaster is always challenging. The complexity of these operations increases when the transportation, power, and communications networks fail, leaving people stranded and unable to communicate their locations and needs. The advent of emerging technologies like uncrewed autonomous vehicles can help humanitarian logistics providers reach otherwise stranded populations after transportation network failures. However, due to the failures in telecommunication infrastructure, demand for emergency aid can become uncertain. To address the challenges of delivering emergency aid to trapped populations with failing infrastructure networks, we propose a novel robust computational framework for a two‐echelon vehicle routing problem that uses uncrewed autonomous vehicles (UAVs), or drones, for the deliveries. We formulate the problem as a two‐stage robust optimization model to handle demand uncertainty. Then, we propose a column‐and‐constraint generation approach for worst‐case demand scenario generation for a given set of truck and UAV routes. Moreover, we develop a decomposition scheme inspired by the column generation approach to generate UAV routes for a set of demand scenarios heuristically. Finally, we combine the decomposition scheme within the column‐and‐constraint generation approach to determine robust routes for both trucks (first echelon vehicles) and UAVs (second echelon vehicles), the time that affected communities are served, and the quantities of aid materials delivered. To validate our proposed algorithms, we use a simulated dataset that aims to recreate emergency aid requests in different areas of Puerto Rico after Hurricane Maria in 2017.

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Stochastic optimization models for location and inventory prepositioning of disaster relief supplies

Cited by 31 publications

References 62 publications

An accurate location method for intelligent warehouse materials based on RFID technology and machine vision

An accurate location method for intelligent warehouse materials based on RFID technology and machine vision

Quick-Response Model for Pre- and Post-Disaster Evacuation and Aid Distribution: The Case of the Tula River Flood Event

A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

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