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Learning Value functions for Same-Day Delivery Problems in the Tardiness Regime 19thth

Learning Value Functions for Same-Day Delivery Problems in...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Frohner, Nikolaus Raidl, Günther R. Institute of Logic and Computation Vienna TU Wien Austria

ISBN: (纸本)9783031829512

Same-day delivery problems are a class of stochastic decision making problems concerned with delivering orders placed dynamically by stochastic customers on the same day given a fleet of vehicles. We consider a variant where all orders have to be served with the objective to minimize a tardiness penalty function and where their spatiotemporal distribution is known. A well-known baseline approach to increase performance compared to myopic optimization is by sampling and optimizing scenarios in the short-horizon and deriving a consensus solution from the resulting plans. Its drawback is the computational effort required, which may not make it suitable for near real-time decision making. Extending recent methodology from the literature, we replace this online sampling by an offline training of a short-horizon value function using a neural network, which is then used in the online point-in-time optimization, combining current reward plus estimated future value of a solution candidate. In a first computational study on a single-vehicle instance class with unavoidable tardiness, we show that this leads to comparable performance as the sampling approach, while greatly reducing the online decision time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic Vehicle Routing with Stochastic Customers Same-Day Delivery surrogate function optimization Value function Approximation

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Learning surrogate functions for the Short-Horizon Planning in Same-Day Delivery Problems 1

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18th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Bracher, Adrian Frohner, Nikolaus Raidl, Gunther R. TU Wien Inst Log & Computat Favoritenstr 9-11-192-01 A-1040 Vienna Austria

ISBN: (数字)9783030782306

ISBN: (纸本)9783030782306;9783030782290

Same-day delivery problems are challenging stochastic vehicle routing problems, where dynamically arriving orders have to be delivered to customers within a short time while minimizing costs. In this work, we consider the short-horizon planning of a problem variant where every order has to be delivered with the goal to minimize delivery tardiness, travel times, and labor costs of the drivers involved. Stochastic information as spatial and temporal order distributions is available upfront. Since timely routing decisions have to be made over the planning horizon of a day, the well-known sampling approach from the literature for considering expected future orders is not suitable due to its high runtimes. To mitigate this, we suggest to use a surrogate function for route durations that predicts the future delivery duration of the orders belonging to a route at its planned starting time. This surrogate function is directly used in the online optimization replacing the myopic current route duration. The function is trained offline by data obtained from running full day-simulations, sampling and solving a number of scenarios for each route at each decision point in time. We consider three different models for the surrogate function and compare with a sampling approach on challenging real-world inspired artificial instances. Results indicate that the new approach can outperform the sampling approach by orders of magnitude regarding runtime while significantly reducing travel costs in most cases.

关键词： Same-day delivery Dynamic and stochastic vehicle routing Sampling surrogate function optimization Supervised learning

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