Abstract
Express parcel carriers offer a wide range of guaranteed delivery times in order to separate
customers who value quick delivery from those that are less time but more price sensitive. Such
segmentation, however, adds a whole new layer of complexity to the task of optimizing the logistics
operations. While many sophisticated models have been developed to assist network planners
in minimizing costs, few approaches account for the interplay between service pricing, customer
decisions and the associated restrictions in the distribution process. This paper attempts to fill
this research gap by introducing a heuristic solution approach that simultaneously determines the
ideal set of services, the associated pricing and the fulfillment plan in order to maximize profit. By
integrating revenue management techniques into vehicle routing and
eet planning, we derive a new
type of formulation called service selection, pricing and fulfillment problem (SSPFP). It combines
a multi-product pricing problem with a cycle-based service network design formulation. In order
derive good-quality solutions for realistically-sized instances we use an asynchronous parallel genetic
algorithm and follow the intuition that small changes to prices and customer assignments cause
minor changes in the distribution process. We thus base every new solution on the most similar
already evaluated fulfillment plan. This adapted initial solution is then iteratively improved by a
newly-developed route-pattern exchange heuristic. The performance of the developed algorithm is
demonstrated on a number of randomly created test instances and is compared to the solutions of
a commercial MIP-solver.
customers who value quick delivery from those that are less time but more price sensitive. Such
segmentation, however, adds a whole new layer of complexity to the task of optimizing the logistics
operations. While many sophisticated models have been developed to assist network planners
in minimizing costs, few approaches account for the interplay between service pricing, customer
decisions and the associated restrictions in the distribution process. This paper attempts to fill
this research gap by introducing a heuristic solution approach that simultaneously determines the
ideal set of services, the associated pricing and the fulfillment plan in order to maximize profit. By
integrating revenue management techniques into vehicle routing and
eet planning, we derive a new
type of formulation called service selection, pricing and fulfillment problem (SSPFP). It combines
a multi-product pricing problem with a cycle-based service network design formulation. In order
derive good-quality solutions for realistically-sized instances we use an asynchronous parallel genetic
algorithm and follow the intuition that small changes to prices and customer assignments cause
minor changes in the distribution process. We thus base every new solution on the most similar
already evaluated fulfillment plan. This adapted initial solution is then iteratively improved by a
newly-developed route-pattern exchange heuristic. The performance of the developed algorithm is
demonstrated on a number of randomly created test instances and is compared to the solutions of
a commercial MIP-solver.
| Originalsprache | Englisch |
|---|---|
| Erscheinungsort | Vienna |
| Herausgeber | WU Vienna University of Economics and Business |
| Publikationsstatus | Veröffentlicht - 2019 |
Publikationsreihe
| Reihe | Schriftenreihe des Instituts für Transportwirtschaft und Logistik - Supply Chain Management |
|---|---|
| Nummer | 01/2019 |
WU Working Paper Reihe
- Schriftenreihe des Instituts für Transportwirtschaft und Logistik - Supply Chain Management