Seminar by Prof. Melvyn Sim, National University of Singapore
Event details
| Date | 15.08.2019 |
| Hour | 15:00 › 16:30 |
| Speaker | Prof. Melvyn Sim, National University of Singapore |
| Location | |
| Category | Conferences - Seminars |
Robust Data-Driven Vehicle Routing with Time Windows
Abstract
Optimal routing solutions in deterministic models usually fail to deliver promised on-time services in the real world of uncertainty, causing potential loss of customers and revenue. In this study, we propose a new formulation for the data-driven Vehicle Routing Problem with Time Windows (vrptw) under uncertain travel times that is compatible with the paradigm of distributionally robust optimization. To mitigate the lateness as much as possible, our model minimizes an innovative decision criterion on the delays, termed the Service Fulfillment Risk Index (sri), while limiting the travel cost within a budget. The sri accounts for both the late arrival probability and its magnitude, captures the risk and the Wasserstein ambiguity in travel times, and is efficiently evaluable in closed form. In particular, the closed-form solution reduces the vrptw under the Wasserstein ambiguity of interest to the problem under the empirical distribution with advanced deadlines. To solve the problem, we develop a Benders decomposition algorithm and a variable neighborhood search heuristic, and explore their speedup strategies. We demonstrate their effectiveness through extensive computational studies. In particular, our solution greatly improves on-time arrival per- formance with slightly increased expenditure than the deterministic solution. Our sri also outperforms the canonical decision criteria, lateness probability and expected lateness duration, in out-of-sample simulations. This is a joint work with with Yu Zhang, Zhenzhen Zhang and Andrew Lim.
Abstract
Optimal routing solutions in deterministic models usually fail to deliver promised on-time services in the real world of uncertainty, causing potential loss of customers and revenue. In this study, we propose a new formulation for the data-driven Vehicle Routing Problem with Time Windows (vrptw) under uncertain travel times that is compatible with the paradigm of distributionally robust optimization. To mitigate the lateness as much as possible, our model minimizes an innovative decision criterion on the delays, termed the Service Fulfillment Risk Index (sri), while limiting the travel cost within a budget. The sri accounts for both the late arrival probability and its magnitude, captures the risk and the Wasserstein ambiguity in travel times, and is efficiently evaluable in closed form. In particular, the closed-form solution reduces the vrptw under the Wasserstein ambiguity of interest to the problem under the empirical distribution with advanced deadlines. To solve the problem, we develop a Benders decomposition algorithm and a variable neighborhood search heuristic, and explore their speedup strategies. We demonstrate their effectiveness through extensive computational studies. In particular, our solution greatly improves on-time arrival per- formance with slightly increased expenditure than the deterministic solution. Our sri also outperforms the canonical decision criteria, lateness probability and expected lateness duration, in out-of-sample simulations. This is a joint work with with Yu Zhang, Zhenzhen Zhang and Andrew Lim.
Practical information
- General public
- Free