The HSABA for Emergency Location-Routing Problem

Siliang Luan,Zhongtai Jiang,Wei Wang,Qingfang Yang,Huxing Zhou,Ruijuan Chu,Zhuorui Wang

doi:10.1155/2019/5391687

Abstract

This article presents a Location-Routing Problem (LRP) model to assist decision makers in emergency logistics. The model attempts to consider the relationship between the location of warehouses and the delivery routes in order to maximize the rescue efficiency. The objective function of the minimization of time and cost is established in the single-stage LRP model considering different scenarios. The hybrid self-adaptive bat algorithm (HSABA) is an improved nature-inspired algorithm for solving this LRP model, hard optimization problem. The HSABA with self-adaptation mechanism and hybridization mechanism effectively improves the defect of the original BA, that is, trapping into the local optima easily. An example is provided to prove the effectiveness of our model. The studied example shows that the single-stage LRP model can effectively select supply locations and plan rescue routes faced with different disasters and the HSABA outperforms the basic BA.

Highlights

The 21st century is a period of the rapid development of human information technology
Location-Routing Problem (LRP) plays a key role in the decision support system for preemptive prevention, allocation of emergency resources in the event, selection of the best rescue route, and post-event improvement in all aspects of the emergency
There are 20 demand points, 4 supply locations randomly selected from Solomon test data

Summary

Introduction

The 21st century is a period of the rapid development of human information technology. G. Barbaroso et al proposed a two-stage stochastic programming model for the complexity of emergency rescue supplies to the disaster area after analyzing the system uncertainty and information asymmetry caused by the vulnerability of the transportation system [13]. Hong et al forecasted the motion of a floating platform by a support vector regression model with a hybrid kernel function and proposed chaotic efficient bat algorithm based on the chaotic, niche search, and evolution mechanisms to improve the reliability and effectiveness of the basic BA [24]. (5) We ignore the disaster areas that require the rescue of aircraft, railway, and water transportation, and we only consider the road network around disaster areas

Problem Formulation

Solution Approach

Case Study

Conclusion