This thesis addresses the Vehicle Routing Problem with Interdiction (VRPI), an extension of the classic Vehicle Routing Problem (VRP) that incorporates the risk of route interdiction due to events such as natural disasters, armed conflicts, and infrastructural failures, among others. These interdictions introduce uncertainty and complexity into logistics planning, requiring innovative approaches to the routing process. This research employs both exact methods, using the CPLEX solver, and heuristic methods, particularly using the Greedy Randomized Adaptive Search Procedure (GRASP), to solve VRPI with different instance sizes.
This research’s key contributions include successfully implementing the GRASP algorithm on large-scale benchmark instances, representing a significant advancement over prior implementations that focused on smaller, randomly generated instances. A flexible framework was also developed to adapt the GRASP methodology for different VRP variants, including the Capacitated Vehicle Routing Problem (CVRP) and Split Delivery Vehicle Routing Problem (SDVRP), with and without interdiction.
A feasibility analysis for small instances was developed using CPLEX, highlighting the sensitivity of VRPI solutions to interdiction probabilities, particularly in scenarios with tight capacity constraints. The findings of this analysis are extended to large instances.
Additionally, a 3-fold logic was incorporated in the GRASP implementation—focused on minimizing cost, minimizing interdiction, and minimizing demand—proved crit- ical in facing the VRPI challenges, and provided high-quality solutions with reduced computational effort. Including the minimum demand logic in GRASP was instrumen- tal during the implementation and numerical experimentation for large benchmark in- stances.
The implications of this thesis are significant for operational research (OR), particularly in high-risk environments where route interdictions can occur. Future research directions include generating more diverse benchmark instances for VRPI, exploring the impact of variability in interdiction probabilities on solution quality and computational time, and applying exact methods like dynamic programming to solve large VRP instances. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/30390 |
| Date | 11 1900 |
| Creators | Hinrichsen Picand, Carlos |
| Contributors | Huang, Kai, Computational Engineering and Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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