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Formulations and solution methods for the road restoration crew scheduling and routing problem

Grant number: 17/22094-8
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): August 01, 2018
Effective date (End): July 31, 2019
Field of knowledge:Engineering - Production Engineering
Principal Investigator:Pedro Augusto Munari Junior
Grantee:Alfredo Daniel Moreno Arteaga
Supervisor abroad: Michel Gendreau
Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Local de pesquisa : Centre Interuniversitaire de Recherche sur les Réseaux d'Entreprise, la Logistique et le Transport (CIRRELT), Canada  
Associated to the scholarship:16/15966-6 - Crew scheduling and routing in network repair under uncertainty, BP.DR

Abstract

Disasters can cause partial or total disruption of basic services such as water, energy, communication and transportation. In particular, recovering the transportation infrastructure composed by roads, bridges and tunnels is of ultimate importance in post-disaster situations, to enable the evacuation of victims and the distribution of supplies to affected areas. Road restoration, one of the main activities in this context, is a complex problem due to its inherent decisions that must be taken quickly and under uncertainty. Even though the road restoration problem has attracted the attention of researchers in the last few years, there is lack of research on the incorporation of uncertainty and on the development of efficient solution methods. This project thus focuses on the Road Restoration Crew Scheduling and Routing Problem (RRCSRP) under uncertainty, which integrates different decisions, namely, the allocation of resources and the scheduling and routing of the crew that performs the restoration activities. The aim is to propose robust optimization models to incorporate travel and repair times uncertainties. Hybrid solution methods that combine Benders decomposition and meta-heuristic shall be developed to solve the corresponding optimization problems. Computational experiments using real-life instances will be considered to validate the approaches. (AU)

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