Computational Methods to Optimize High-Consequence Variants of the Vehicle Routing Problem for Relief Networks in Humanitarian Logistics

PDF Version Also Available for Download.

Description

Optimization of relief networks in humanitarian logistics often exemplifies the need for solutions that are feasible given a hard constraint on time. For instance, the distribution of medical countermeasures immediately following a biological disaster event must be completed within a short time-frame. When these supplies are not distributed within the maximum time allowed, the severity of the disaster is quickly exacerbated. Therefore emergency response plans that fail to facilitate the transportation of these supplies in the time allowed are simply not acceptable. As a result, all optimization solutions that fail to satisfy this criterion would be deemed infeasible. This creates ... continued below

Creation Information

Urbanovsky, Joshua C August 2018.

Context

This dissertation is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. More information about this dissertation can be viewed below.

Who

People and organizations associated with either the creation of this dissertation or its content.

Publisher

Rights Holder

For guidance see Citations, Rights, Re-Use.

  • Urbanovsky, Joshua C

Provided By

UNT Libraries

The UNT Libraries serve the university and community by providing access to physical and online collections, fostering information literacy, supporting academic research, and much, much more.

Contact Us

What

Descriptive information to help identify this dissertation. Follow the links below to find similar items on the Digital Library.

Degree Information

Description

Optimization of relief networks in humanitarian logistics often exemplifies the need for solutions that are feasible given a hard constraint on time. For instance, the distribution of medical countermeasures immediately following a biological disaster event must be completed within a short time-frame. When these supplies are not distributed within the maximum time allowed, the severity of the disaster is quickly exacerbated. Therefore emergency response plans that fail to facilitate the transportation of these supplies in the time allowed are simply not acceptable. As a result, all optimization solutions that fail to satisfy this criterion would be deemed infeasible. This creates a conflict with the priority optimization objective in most variants of the generic vehicle routing problem (VRP). Instead of efficiently maximizing usage of vehicle resources available to construct a feasible solution, these variants ordinarily prioritize the construction of a minimum cost set of vehicle routes. Research presented in this dissertation focuses on the design and analysis of efficient computational methods for optimizing high-consequence variants of the VRP for relief networks. The conflict between prioritizing the minimization of the number of vehicles required or the minimization of total travel time is demonstrated. The optimization of the time and capacity constraints in the context of minimizing the required vehicles are independently examined. An efficient meta-heuristic algorithm based on a continuous spatial partitioning scheme is presented for constructing a minimized set of vehicle routes in practical instances of the VRP that include critically high-cost penalties. Multiple optimization priority strategies that extend this algorithm are examined and compared in a large-scale bio-emergency case study. The algorithms designed from this research are implemented and integrated into an existing computational framework that is currently used by public health officials. These computational tools enhance an emergency response planner's ability to derive a set of vehicle routes specifically optimized for the delivery of resources to dispensing facilities in the event of a bio-emergency.

Language

Identifier

Unique identifying numbers for this dissertation in the Digital Library or other systems.

Collections

This dissertation is part of the following collection of related materials.

UNT Theses and Dissertations

Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process. Some ETDs in this collection are restricted to use by the UNT community.

What responsibilities do I have when using this dissertation?

When

Dates and time periods associated with this dissertation.

Creation Date

  • August 2018

Added to The UNT Digital Library

  • Sept. 26, 2018, 6:16 p.m.

Usage Statistics

When was this dissertation last used?

Yesterday: 0
Past 30 days: 2
Total Uses: 4

Interact With This Dissertation

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Urbanovsky, Joshua C. Computational Methods to Optimize High-Consequence Variants of the Vehicle Routing Problem for Relief Networks in Humanitarian Logistics, dissertation, August 2018; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc1248473/: accessed November 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .