Decision Assessment Algorithms for Location and Capacity Optimization under Resource Shortages

Adrian Ramirez-Nafarrate, Ozgur M. Araz, John W. Fowler

Research output: Contribution to journalArticle

Abstract

Emergency response preparedness requires strategic and operational planning. As natural disasters, disease outbreaks, and bioterrorism continue to threaten public health and the global economic system, policy makers and businesses develop plans for improving their readiness. These emergencies present challenging problems that require evaluation of several trade-offs due to insufficient resources and capacity with limited response time pressure. Finding an optimal strategy to these problems can be computationally difficult, since they are formulated as large-scale service network design models with location-allocation decisions. The facility location problems in disaster management literature are typically known as NP -hard problems. Therefore, heuristic algorithms can help find practically feasible solutions, but decision makers may have to sacrifice one of their objectives while trying to satisfy multiple of them. This paper presents a flexible algorithm to evaluate trade-offs in resource scarce situations with a given response time. We formulate the location-allocation problem with capacity and time constraints, and the objective of minimizing the service time for individuals in an affected area. Due to the complexity to solve the problem for large-scale scenarios, the presented algorithm relaxes capacity and time constraints, simultaneously, and presents flexibility to assess trade-offs. A modified NSGA-II algorithm is used with a penalty function formulated to leverage resources. We analyze how values of the penalty parameters interact with a number of open sites in defining an efficient resource allocation strategy. Therefore, decision makers can expand their choices to design an emergency response network with their preferences taken into consideration.

Original languageEnglish (US)
JournalDecision Sciences
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Disasters
Bioterrorism
Public health
Heuristic algorithms
Resource allocation
Computational complexity
Planning
Economics
Resources
Trade-offs
Shortage
Industry
Decision maker
Time constraints
Capacity constraints
Emergency response
Response time
Location-allocation
Penalty function
Natural disasters

Keywords

  • Emergency Response
  • Heuristic Algorithms
  • Location
  • Resource Allocation
  • Service Network Design

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Strategy and Management
  • Information Systems and Management
  • Management of Technology and Innovation

Cite this

Decision Assessment Algorithms for Location and Capacity Optimization under Resource Shortages. / Ramirez-Nafarrate, Adrian; Araz, Ozgur M.; Fowler, John W.

In: Decision Sciences, 01.01.2019.

Research output: Contribution to journalArticle

@article{7b460518252343c6a3e2070095bc4585,
title = "Decision Assessment Algorithms for Location and Capacity Optimization under Resource Shortages",
abstract = "Emergency response preparedness requires strategic and operational planning. As natural disasters, disease outbreaks, and bioterrorism continue to threaten public health and the global economic system, policy makers and businesses develop plans for improving their readiness. These emergencies present challenging problems that require evaluation of several trade-offs due to insufficient resources and capacity with limited response time pressure. Finding an optimal strategy to these problems can be computationally difficult, since they are formulated as large-scale service network design models with location-allocation decisions. The facility location problems in disaster management literature are typically known as NP -hard problems. Therefore, heuristic algorithms can help find practically feasible solutions, but decision makers may have to sacrifice one of their objectives while trying to satisfy multiple of them. This paper presents a flexible algorithm to evaluate trade-offs in resource scarce situations with a given response time. We formulate the location-allocation problem with capacity and time constraints, and the objective of minimizing the service time for individuals in an affected area. Due to the complexity to solve the problem for large-scale scenarios, the presented algorithm relaxes capacity and time constraints, simultaneously, and presents flexibility to assess trade-offs. A modified NSGA-II algorithm is used with a penalty function formulated to leverage resources. We analyze how values of the penalty parameters interact with a number of open sites in defining an efficient resource allocation strategy. Therefore, decision makers can expand their choices to design an emergency response network with their preferences taken into consideration.",
keywords = "Emergency Response, Heuristic Algorithms, Location, Resource Allocation, Service Network Design",
author = "Adrian Ramirez-Nafarrate and Araz, {Ozgur M.} and Fowler, {John W.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1111/deci.12418",
language = "English (US)",
journal = "Decision Sciences",
issn = "0011-7315",
publisher = "Wiley-Blackwell",

}

TY - JOUR

T1 - Decision Assessment Algorithms for Location and Capacity Optimization under Resource Shortages

AU - Ramirez-Nafarrate, Adrian

AU - Araz, Ozgur M.

AU - Fowler, John W.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Emergency response preparedness requires strategic and operational planning. As natural disasters, disease outbreaks, and bioterrorism continue to threaten public health and the global economic system, policy makers and businesses develop plans for improving their readiness. These emergencies present challenging problems that require evaluation of several trade-offs due to insufficient resources and capacity with limited response time pressure. Finding an optimal strategy to these problems can be computationally difficult, since they are formulated as large-scale service network design models with location-allocation decisions. The facility location problems in disaster management literature are typically known as NP -hard problems. Therefore, heuristic algorithms can help find practically feasible solutions, but decision makers may have to sacrifice one of their objectives while trying to satisfy multiple of them. This paper presents a flexible algorithm to evaluate trade-offs in resource scarce situations with a given response time. We formulate the location-allocation problem with capacity and time constraints, and the objective of minimizing the service time for individuals in an affected area. Due to the complexity to solve the problem for large-scale scenarios, the presented algorithm relaxes capacity and time constraints, simultaneously, and presents flexibility to assess trade-offs. A modified NSGA-II algorithm is used with a penalty function formulated to leverage resources. We analyze how values of the penalty parameters interact with a number of open sites in defining an efficient resource allocation strategy. Therefore, decision makers can expand their choices to design an emergency response network with their preferences taken into consideration.

AB - Emergency response preparedness requires strategic and operational planning. As natural disasters, disease outbreaks, and bioterrorism continue to threaten public health and the global economic system, policy makers and businesses develop plans for improving their readiness. These emergencies present challenging problems that require evaluation of several trade-offs due to insufficient resources and capacity with limited response time pressure. Finding an optimal strategy to these problems can be computationally difficult, since they are formulated as large-scale service network design models with location-allocation decisions. The facility location problems in disaster management literature are typically known as NP -hard problems. Therefore, heuristic algorithms can help find practically feasible solutions, but decision makers may have to sacrifice one of their objectives while trying to satisfy multiple of them. This paper presents a flexible algorithm to evaluate trade-offs in resource scarce situations with a given response time. We formulate the location-allocation problem with capacity and time constraints, and the objective of minimizing the service time for individuals in an affected area. Due to the complexity to solve the problem for large-scale scenarios, the presented algorithm relaxes capacity and time constraints, simultaneously, and presents flexibility to assess trade-offs. A modified NSGA-II algorithm is used with a penalty function formulated to leverage resources. We analyze how values of the penalty parameters interact with a number of open sites in defining an efficient resource allocation strategy. Therefore, decision makers can expand their choices to design an emergency response network with their preferences taken into consideration.

KW - Emergency Response

KW - Heuristic Algorithms

KW - Location

KW - Resource Allocation

KW - Service Network Design

UR - http://www.scopus.com/inward/record.url?scp=85076166576&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85076166576&partnerID=8YFLogxK

U2 - 10.1111/deci.12418

DO - 10.1111/deci.12418

M3 - Article

AN - SCOPUS:85076166576

JO - Decision Sciences

JF - Decision Sciences

SN - 0011-7315

ER -