Abstract

Disaster management generally includes the post-disaster stage, which consists of the actions taken in response to the disaster damages. These actions include the employment of emergency plans and assigned resources to (i) rescue affected people immediately, (ii) deliver personnel, medical care and equipment to the disaster area, and (iii) aid to prevent the infrastructural and environmental losses. In the response phase, humanitarian logistics directly influence the efficiency of the relief operation. Ambulances routing problem is defined as employing the optimisation tools to manage the flow of ambulances for finding the best ambulance tours to transport the injured to hospitals. Researchers pointed out the importance of equity and fairness in humanitarian relief services: managing the operations of ambulances in the immediate aftermath of a disaster must be done impartially and efficiently to rescue affected people with different priority in accordance with the restrictions. Our research aim is to find the best ambulance tours to transport the patients during a disaster in relief operations while considering fairness and equity to deliver services to patients in balance. The problem is formulated as a new variant of the team orienteering problem with hierarchical objectives to address also the efficiency issue. Due to the limitation of solving the proposed model using a general-purpose solver, we propose a new hybrid algorithm based on a machine learning and neighbourhood search. Based on a new set of realistic benchmark instances, our quantitative analysis proves that our algorithm is capable to largely reduce the solution running time especially when the complexity of the problem increases. Further, a comparison between the fair solution and the system optimum solution is also provided.

Highlights

  • The destructive impacts of disasters have threatened societies for years and inevitable consequences entail financial and human costs for the victims

  • Many researchers concerning humanitarian relief services pointed out the importance of equity, the few articles focused on fairness as an objective in disaster optimisation problems

  • Many researchers concerning humanitarian relief services highlight the importance of equity, the few articles focused on fairness as an objective in disaster optimisation problems as highlighted in our literature review

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Summary

Introduction

The destructive impacts of disasters have threatened societies for years and inevitable consequences entail financial and human costs for the victims. Pre-disaster stage predicts the potential human and property losses and develops the preparedness plans to reduce the impact of disasters by improvement in emergency services and humanitarian logistics while the post-disaster stage consists of the actions taken in response to the disaster damages (Shiripour and Mahdavi-Amiri 2019). These two phases have been classified into four action categories in more detail: (i) mitigation refers to the actions needed to prevent the occurrence of a disaster and to decrease the disastrous impacts; (ii) preparedness involves planning procedures in a community for a timely response to damages; (iii) response includes the employment of emergency plans and assigned resources to (a) rescue affected people immediately, (b) deliver personnel, medical care and equipment to the disaster area, and (c) aid to prevent the infrastructural and environmental losses; (iv) recovery is the final action category in which actions followed to return the situation to normalcy (Boonmee et al 2017; Altay and Green 2006).

Literature review
Improving the solution using ad hoc neighbourhoods
The generation of realistic instances
Computational analysis
Conclusions
Full Text
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