A two-stage stochastic mixed-integer program for the capacitated logistics fortification planning under accidental disruptions

Authors:
Xuwei Qin;X. Liu;Lixin Tang
Affiliations:
School of Business Administration, Northeastern University, PR China;Department of Production and Supply Chain Management, Technical University of Munich, Germany;Liaoning Key Laboratory of Manufacturing System and Logistics, The Logistics Institute, Northeastern University, PR China
Venue:
Computers and Industrial Engineering
Year:
2013

Citing 13
Cited 0

A modification of Benders' decomposition algorithm for discrete subproblems: An approach for stochastic programs with integer recourse

Journal of Global Optimization
The C3 Theorem and a D2 Algorithm for Large Scale Stochastic Mixed-Integer Programming: Set Convexification

Mathematical Programming: Series A and B
The Million-Variable "March" for Stochastic Combinatorial Optimization

Journal of Global Optimization
Decomposition with branch-and-cut approaches for two-stage stochastic mixed-integer programming

Mathematical Programming: Series A and B
Reliability Models for Facility Location: The Expected Failure Cost Case

Transportation Science
A bilevel mixed-integer program for critical infrastructure protection planning

Computers and Operations Research
A comparative study of decomposition algorithms for stochastic combinatorial optimization

Computational Optimization and Applications
A two-stage stochastic programming model for transportation network protection

Computers and Operations Research
Facility Reliability Issues in Network p-Median Problems: Strategic Centralization and Co-Location Effects

Operations Research
Enhanced Cut Generation Methods for Decomposition-Based Branch and Cut for Two-Stage Stochastic Mixed-Integer Programs

INFORMS Journal on Computing
Reliable Facility Location Design Under the Risk of Disruptions

Operations Research
Analysis of facility protection strategies against an uncertain number of attacks: The stochastic R-interdiction median problem with fortification

Computers and Operations Research
Optimal Allocation of Protective Resources in Shortest-Path Networks

Transportation Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

Vulnerability to service disruptions caused by accidents is one of the major threats in existing logistics systems. This paper presents a fortification planning model for capacitated logistics systems in a two-stage stochastic mixed-integer programming framework. Considering limited protection investment budget, the model can deal with locating fortified facilities, pre-positioning emergency inventory and assigning emergency transportation under scenario-based random parameters. The risk mitigation combination of facility protection and emergency inventory pre-positioning policies is proposed to hedge well against accidental disruptions in the capacitated logistics systems. The revised disjunctive decomposition-based branch-and-cut (D2-BAC) algorithm for the model is developed by integrating with two types of valid cuts and dynamical 'truncation' strategy of the branch-and-bound tree. Extensive computational results confirm the computational performance of the proposed method and indicate that this model can provide a powerful tool for identifying best possible fortification strategies. It is also demonstrated that the risk mitigation combination can significantly increase the reliability of capacitated logistics systems.