Sponsor
Portland State University. Department of Civil & Environmental Engineering
First Advisor
Avinash Unnikrishnan
Term of Graduation
Spring 2021
Date of Publication
6-2-2021
Document Type
Thesis
Degree Name
Master of Science (M.S.) in Civil & Environmental Engineering
Department
Civil and Environmental Engineering
Language
English
Subjects
Ambulance service -- Location -- Mathematical models, Conditional expectations (Mathematics), Emergency medical services
DOI
10.15760/etd.7587
Physical Description
1 online resource (xi, 109 pages)
Abstract
A classical maximum coverage facility location problem (MCLP) tries to maximize the coverage by serving all possible demands within the specified coverage radius. Depending on the applications, several families of MCLP variants have been developed - each with its unique set of additional constraints. One specific application is locating Emergency medical service (EMS) units. EMS units such as ambulances or drones with emergency supplies are located using models such as the maximum expected survival location problem (MEXSLP). However, a classical MEXSLP model tends to locate EMS units near densely populated regions, resulting in increased response times for lowly populated regions. If equity is to be taken into consideration, the EMS units would be placed more uniformly across the region which might result in reduced overall coverage.
In this thesis, a conditional expectation measure called the conditional ß-mean (CBM) is considered and integrated into the facility location models. First, the CBM measure is integrated into the MCLP, resulting in the development of a new MCLP model. Using an alternative and more intuitive definition for CBM, another modified MCLP model incorporated with CBM is developed. These new MCLP models incorporated with CBM are single objective facility location models with flexible coverage radius constraints. Two standard test cases and a Portland Metropolitan region dataset are used to test the MCLP models. A simple greedy based heuristic is developed, and its performance is compared to a state-of-the-art Mixed Integer Programming solver Gurobi. The integration of CBM into the MCLP model has improved its coverage. The proposed heuristic provides reliable solutions and saves considerable computational time.
Using the same CBM measure, equity is incorporated into the MEXSLP model. We propose two different approaches to estimate CBM and develop two modified MEXSLP models improving the quality of service provided to outlying and lowly populated areas. The two MEXSLP models modified with CBM are linear, and the decision maker has the leverage to chose the level of equity in the solution using the parameter ß. The same Portland dataset is used for testing the MEXSLP models. Integration of CBM into the MEXSLP has improved the service in Portland suburbs by locating few EMS units closer to them.
Rights
© 2021 Rohan Sirupa
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Persistent Identifier
https://archives.pdx.edu/ds/psu/36048
Recommended Citation
Sirupa, Rohan, "Incorporating Conditional ß-Mean based Equity Metric in Coverage based Facility Location Problems" (2021). Dissertations and Theses. Paper 5714.
https://doi.org/10.15760/etd.7587
Comments
This research is a product of NSF grants awarded to Prof. Unnikrishnan (Grant numbers: 1562109 and 1826337).