Abstract / Description of output
Extreme events such as disasters cause partial or total disruption of basic services such as water,energy, communication and transportation. In particular, roads can be damaged or blocked by debris, thereby obstructing access to certain affected areas. Thus, restoration of the damaged roads is necessary to evacuate victims and distribute emergency commodities to relief centers or affected areas. The Crew Scheduling and Routing Problem (CSRP) addresses decisions in postdisaster situations with the aim of minimizing the time that affected areas remain inaccessible.The integration of crew scheduling and routing decisions makes this problem too complicated to be effectively solved for practical instances using mixed integer programming (MIP) formulations recently proposed in the literature. Therefore, we propose a branch-and-Benders-cut (BBC)algorithm that decomposes the integrated problem into a master problem (MP) with scheduling decisions and subproblems with routing decisions. Computational tests based on instances from the literature show that the proposed exact method improves the results of MIP formulations and other exact and metaheuristic methods proposed in literature. The BBC algorithm provides feasible solutions and optimality gaps for instances that thus far have not been possible to solve by exact methods in the literature.
Original language | English |
---|---|
Pages (from-to) | 16-34 |
Journal | European Journal of Operational Research |
Volume | 257 |
Issue number | 1 |
Early online date | 8 Nov 2018 |
DOIs | |
Publication status | Published - 16 May 2019 |
Keywords / Materials (for Non-textual outputs)
- combinatorial optimization
- benders decomposition
- branch-and-cut
- crew scheduling and routing
- road restoration
Fingerprint
Dive into the research topics of 'A branch-and-benders-cut algorithm for the crew scheduling and routing problem in road restoration'. Together they form a unique fingerprint.Profiles
-
Douglas Alem
- Business School - Senior Lecturer in Business Analytics
- Management Science and Business Economics
- Management Science
Person: Academic: Research Active