An RFID-based inventory management framework for emergency relief operations

Eren Erman Ozguven, Kaan Ozbay

Research output: Contribution to journalArticle

Abstract

In the aftermath of super storm Sandy, a large region from North Carolina to Maine endured food shortages, power outages, and long lines at gas stations forced to ration fuel due to low supply and high demand. These issues were largely the result of the affected transportation network's inability to effectively cope with random and highly dynamic changes, and a lack of available resources and suppliers who were capable of enacting adequate emergency response measures. These problems experienced during super storm Sandy further underscored the need for a robust emergency inventory management system, where planning policies can be integrated with real-time on-line inventory management strategies to keep track of fluctuations of vital commodities such as food, water, medicine, fuel and power supplies. Motivated by this important problem, this paper investigates a comprehensive feedback-based emergency management framework for disasters such as super storm Sandy that provides integration with an emerging intelligent transportation systems technology, namely Radio Frequency Identification Devices (RFID). Within this framework, the offline-planning problem is solved by the stochastic humanitarian inventory management approach; and the online modeling strategy includes the application of a continuous time model predictive control technique. After introducing the mathematical background, the proposed framework is discussed using case studies built based on super storm Sandy in order to understand the efficiency and practicality of this RFID-based methodology. Results suggest that the methodology can properly account for and react to the rapidly changing needs for vital supplies that occur during the emergency relief operations. Based on this approach, planners and decision makers can be aware of the time delay that can happen due to disaster-related disruptions and thus maintain a safe level of buffer for vital supplies.

Original languageEnglish (US)
Pages (from-to)166-187
Number of pages22
JournalTransportation Research Part C: Emerging Technologies
Volume57
DOIs
StatePublished - Aug 1 2015

Fingerprint

Radio frequency identification (RFID)
radio
management
Disasters
disaster
food
Planning
planning
predictive model
Model predictive control
methodology
transportation system
Outages
supplier
fluctuation
Medicine
shortage
commodity
decision maker
Time delay

Keywords

  • Disaster planning and logistics
  • Emergency management
  • Model predictive control
  • RFID technologies
  • Stochastic inventory control

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research
  • Automotive Engineering
  • Transportation

Cite this

An RFID-based inventory management framework for emergency relief operations. / Ozguven, Eren Erman; Ozbay, Kaan.

In: Transportation Research Part C: Emerging Technologies, Vol. 57, 01.08.2015, p. 166-187.

Research output: Contribution to journalArticle

@article{0b1b686411ef4936a2d16f40f97dec6c,
title = "An RFID-based inventory management framework for emergency relief operations",
abstract = "In the aftermath of super storm Sandy, a large region from North Carolina to Maine endured food shortages, power outages, and long lines at gas stations forced to ration fuel due to low supply and high demand. These issues were largely the result of the affected transportation network's inability to effectively cope with random and highly dynamic changes, and a lack of available resources and suppliers who were capable of enacting adequate emergency response measures. These problems experienced during super storm Sandy further underscored the need for a robust emergency inventory management system, where planning policies can be integrated with real-time on-line inventory management strategies to keep track of fluctuations of vital commodities such as food, water, medicine, fuel and power supplies. Motivated by this important problem, this paper investigates a comprehensive feedback-based emergency management framework for disasters such as super storm Sandy that provides integration with an emerging intelligent transportation systems technology, namely Radio Frequency Identification Devices (RFID). Within this framework, the offline-planning problem is solved by the stochastic humanitarian inventory management approach; and the online modeling strategy includes the application of a continuous time model predictive control technique. After introducing the mathematical background, the proposed framework is discussed using case studies built based on super storm Sandy in order to understand the efficiency and practicality of this RFID-based methodology. Results suggest that the methodology can properly account for and react to the rapidly changing needs for vital supplies that occur during the emergency relief operations. Based on this approach, planners and decision makers can be aware of the time delay that can happen due to disaster-related disruptions and thus maintain a safe level of buffer for vital supplies.",
keywords = "Disaster planning and logistics, Emergency management, Model predictive control, RFID technologies, Stochastic inventory control",
author = "Ozguven, {Eren Erman} and Kaan Ozbay",
year = "2015",
month = "8",
day = "1",
doi = "10.1016/j.trc.2015.06.021",
language = "English (US)",
volume = "57",
pages = "166--187",
journal = "Transportation Research Part C: Emerging Technologies",
issn = "0968-090X",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - An RFID-based inventory management framework for emergency relief operations

AU - Ozguven, Eren Erman

AU - Ozbay, Kaan

PY - 2015/8/1

Y1 - 2015/8/1

N2 - In the aftermath of super storm Sandy, a large region from North Carolina to Maine endured food shortages, power outages, and long lines at gas stations forced to ration fuel due to low supply and high demand. These issues were largely the result of the affected transportation network's inability to effectively cope with random and highly dynamic changes, and a lack of available resources and suppliers who were capable of enacting adequate emergency response measures. These problems experienced during super storm Sandy further underscored the need for a robust emergency inventory management system, where planning policies can be integrated with real-time on-line inventory management strategies to keep track of fluctuations of vital commodities such as food, water, medicine, fuel and power supplies. Motivated by this important problem, this paper investigates a comprehensive feedback-based emergency management framework for disasters such as super storm Sandy that provides integration with an emerging intelligent transportation systems technology, namely Radio Frequency Identification Devices (RFID). Within this framework, the offline-planning problem is solved by the stochastic humanitarian inventory management approach; and the online modeling strategy includes the application of a continuous time model predictive control technique. After introducing the mathematical background, the proposed framework is discussed using case studies built based on super storm Sandy in order to understand the efficiency and practicality of this RFID-based methodology. Results suggest that the methodology can properly account for and react to the rapidly changing needs for vital supplies that occur during the emergency relief operations. Based on this approach, planners and decision makers can be aware of the time delay that can happen due to disaster-related disruptions and thus maintain a safe level of buffer for vital supplies.

AB - In the aftermath of super storm Sandy, a large region from North Carolina to Maine endured food shortages, power outages, and long lines at gas stations forced to ration fuel due to low supply and high demand. These issues were largely the result of the affected transportation network's inability to effectively cope with random and highly dynamic changes, and a lack of available resources and suppliers who were capable of enacting adequate emergency response measures. These problems experienced during super storm Sandy further underscored the need for a robust emergency inventory management system, where planning policies can be integrated with real-time on-line inventory management strategies to keep track of fluctuations of vital commodities such as food, water, medicine, fuel and power supplies. Motivated by this important problem, this paper investigates a comprehensive feedback-based emergency management framework for disasters such as super storm Sandy that provides integration with an emerging intelligent transportation systems technology, namely Radio Frequency Identification Devices (RFID). Within this framework, the offline-planning problem is solved by the stochastic humanitarian inventory management approach; and the online modeling strategy includes the application of a continuous time model predictive control technique. After introducing the mathematical background, the proposed framework is discussed using case studies built based on super storm Sandy in order to understand the efficiency and practicality of this RFID-based methodology. Results suggest that the methodology can properly account for and react to the rapidly changing needs for vital supplies that occur during the emergency relief operations. Based on this approach, planners and decision makers can be aware of the time delay that can happen due to disaster-related disruptions and thus maintain a safe level of buffer for vital supplies.

KW - Disaster planning and logistics

KW - Emergency management

KW - Model predictive control

KW - RFID technologies

KW - Stochastic inventory control

UR - http://www.scopus.com/inward/record.url?scp=84934759857&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84934759857&partnerID=8YFLogxK

U2 - 10.1016/j.trc.2015.06.021

DO - 10.1016/j.trc.2015.06.021

M3 - Article

VL - 57

SP - 166

EP - 187

JO - Transportation Research Part C: Emerging Technologies

JF - Transportation Research Part C: Emerging Technologies

SN - 0968-090X

ER -