2327. Dynamic Compensation Optimization for Occasional Driver Delivery
Invited abstract in session TD-11: Applications, stream Pricing and Revenue Management.
Thursday, 14:30-16:00Room: U2-200
Authors (first author is the speaker)
| 1. | Kai Winheller
|
| Universität Duisburg-Essen | |
| 2. | Rouven Schur
|
| University of Duisburg-Essen | |
| 3. | Matthias Soppert
|
| Chair of Business Analytics & Management Science, University of the Bundeswehr Munich |
Abstract
Many brick-and-mortar retailers now complement their stores with home-delivery services, yet the last mile remains the most expensive link in the supply chain. A promising solution is to engage in-store customers as occasional drivers (ODs), who deliver online orders along their homeward routes. Previous research typically assumes that retailers deterministically know each customer's post-purchase destination and that ODs are always willing to execute a delivery once assigned—assumptions rarely met in practice.
We dispense with both assumptions. Instead, we model the destinations of ODs as stochastic, reflecting real-world uncertainty. Retailers can directly influence ODs' willingness to participate and their selection of delivery tasks by dynamically adjusting monetary incentives. We represent this decision environment as a finite-horizon Markov decision process. For a special one-dimensional (1D) case, we derive a closed-form optimal policy. Building upon this insight, we develop a decomposition-based algorithm that leverages the exact solution of the 1D case to heuristically address the more complex two-dimensional (2D) scenario.
Keywords
- Dynamical Systems
- Revenue Management and Pricing
- Transportation
Status: accepted
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