Paper Info
Title
A Novel Hybrid Ant Colony Optimization Algorithm for Emergency Transportation Problems During Post-Disaster Scenarios.
Abstract
The increasing impacts of natural disasters have led to concerns regarding predisaster plans and post-disaster responses. During post-disaster responses, emergency transportation is the most important part of disaster relief supply chain operations, and its optimal planning differs from traditional transportation problems in the objective function and complex constraints. In disaster scenarios, fairness and effectiveness are two important aspects. This paper investigates emergency transportation in real-life disasters scenarios and formulates the problem as an integer linear programming model (called cum-MDVRP), which combines cumulative vehicle routing problem and multidepot vehicle routing problem. The cum-MDVRP is NP-hard. To solve it, a novel hybrid ant colony optimization-based algorithm is proposed by combining both saving algorithms and a simple two-step 2-opt algorithm. The proposed algorithm allows ants to go in and out the depots for multiple rounds, so we abbreviate it as ACOMR. Moreover, we present a smart design of the ants' tabus, which helps to simplify the solution constructing process. The ACOMR could yield good solutions quickly, then the decision makers for emergency responses could do expert planning at the earliest time. Computational results on standard benchmarking data sets show that the proposed cum-MDVRP model performs well, and the ACOMR algorithm is more effective and stable than the existing algorithms.
Year
DOI
Venue
2018
10.1109/TSMC.2016.2606440
IEEE Trans. Systems, Man, and Cybernetics: Systems
Keywords
Field
DocType
Linear programming,Algorithm design and analysis,Vehicle routing,Optimization,Vehicles,Genetic algorithms
Ant colony optimization algorithms,Mathematical optimization,Vehicle routing problem,Algorithm design,Computer science,Emergency management,Algorithm,Supply chain,Linear programming,Benchmarking,Genetic algorithm
Journal
Volume
Issue
ISSN
48
4
2168-2216
Citations 
PageRank 
References 
5
0.41
29
Authors
4
Name
Order
Citations
PageRank
Zhigang Yu171.80
Tsan-Ming Choi2104075.03
Haikuo Liu3181.65
Xiaohang Yue49812.03