A chance-constrained two-stage stochastic programming model for humanitarian relief network design

•Introduce a new risk-averse stochastic pre-disaster relief network design problem.•Develop a novel chance-constrained two-stage stochastic programming model.•Hybrid stochastic approach with qualitative and quantitative aspects of risk.•A flexible approach to model preferences based on multiple stoc...

Full description

Saved in:
Bibliographic Details
Published in:Transportation research. Part B: methodological Vol. 108; pp. 55 - 83
Main Authors: Elçi, Özgün, Noyan, Nilay
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.02.2018
Elsevier Science Ltd
Subjects:
Algorithms
Benders decomposition
Computation
Computer applications
Conditional value-at-risk
Disaster relief
Disasters
Humanitarian aid
Humanitarian logistics
Hurricanes
Logistics
Network design
Probabilistic constraint
Risk aversion
Risk levels
Risk-averse
Stochastic models
Stochastic programming
Studies
Traffic accidents & safety
Two-stage stochastic programming
United States > US
Humanitarian logistics
Conditional value-at-risk
Risk-averse
Probabilistic constraint
Benders decomposition
Two-stage stochastic programming
ISSN:0191-2615, 1879-2367
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Introduce a new risk-averse stochastic pre-disaster relief network design problem.•Develop a novel chance-constrained two-stage stochastic programming model.•Hybrid stochastic approach with qualitative and quantitative aspects of risk.•A flexible approach to model preferences based on multiple stochastic criteria.•Develop an effective exact Benders decomposition-based branch-and-cut algorithm. We consider a stochastic pre-disaster relief network design problem, which mainly determines the capacities and locations of the response facilities and their inventory levels of the relief supplies in the presence of uncertainty in post-disaster demands and transportation network conditions. In contrast to the traditional humanitarian logistics literature, we develop a chance-constrained two-stage mean-risk stochastic programming model. This risk-averse model features a mean-risk objective, where the conditional value-at-risk (CVaR) is specified as the risk measure, and enforces a joint probabilistic constraint on the feasibility of the second-stage problem concerned with distributing the relief supplies to the affected areas in case of a disaster. To solve this computationally challenging stochastic optimization model, we employ an exact Benders decomposition-based branch-and-cut algorithm. We develop three variants of the proposed algorithm by using alternative representations of CVaR. We illustrate the application of our model and solution methods on a case study concerning the threat of hurricanes in the Southeastern part of the United States. An extensive computational study provides practical insights about the proposed modeling approach and demonstrates the computational effectiveness of the solution framework.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0191-2615
1879-2367
DOI:10.1016/j.trb.2017.12.002