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1719. From approximation errors to optimality gap - exploiting structural knowledge of opportunity cost in integrated demand management and vehicle routing problems
Invited abstract in session TD-59: Pricing and applications, stream Pricing and Revenue Management.
Tuesday, 14:30-16:00Room: S08 (building: 101)
Authors (first author is the speaker)
| 1. | Vienna Klein
|
| Chair of Analytics & Optimization, University of Augsburg | |
| 2. | David Fleckenstein
|
| Chair of Analytics & Optimization, University of Augsburg | |
| 3. | Robert Klein
|
| Chair of Analytics & Optimization, University of Augsburg | |
| 4. | Claudius Steinhardt
|
| Chair of Business Analytics & Management Science, University of the Bundeswehr Munich (UniBw) |
Abstract
The widespread adoption of digital distribution channels both enables and forces more and more logistical service providers to manage booking processes actively to maintain competitiveness. As a result, their operational planning is no longer limited to solving vehicle routing problems. Instead, demand management and subsequent vehicle routing problems are integrated to steer the booking process with the aim of optimizing the downstream fulfillment operations. The resulting integrated demand management and vehicle routing problems (i-DMVRPs) can be modeled as Markov decision process and, theoretically, solved via the well-known Bellman equation. Unfortunately, the Bellman equation is intractable for industry-sized instances. Thus, in the literature, i-DMVRPs are often addressed via opportunity cost approximation approaches. However, the overall performance of the respective approaches largely varies between different instance structures. Furthermore, to the best of our knowledge, there is neither a structured procedure to analyze the corresponding root causes nor general guidelines on when to apply which class of approximation approach. In this work, we address this gap by proposing a structured method to analyze, explain and compare the performance impact of different opportunity cost approximation-based solution approaches for i-DMVRPs. Further, we identify common patterns in approximation errors and derive general guidelines for an informed algorithm development process.
Keywords
- Revenue Management and Pricing
- E-Commerce
- Vehicle Routing
Status: accepted
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