Crew scheduling and routing in network repair under uncertainty
| 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 - Operational Research |
| 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) | |