EURO 2024 Copenhagen
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1226. Robust Policies for a Multi-Stage Fleet Sizing Problem Under Demand Uncertainty

Invited abstract in session MD-64: Vehicle Routing Under Uncertainty 1, stream VeRoLog - Vehicle Routing and Logistics.

Monday, 14:30-16:00
Room: S16 (building: 101)

Authors (first author is the speaker)

1. Alice Raffaele
Department of Mechanical, Energy, and Management Engineering, University of Calabria
2. Demetrio Laganà
Department of Mechanical, Energy and Management Engineering, University of Calabria
3. Roberto Musmanno
Department of Mechanical, Energy, and Management Engineering, University of Calabria
4. Roberto Roberti
Information Engineering, University of Padova

Abstract

In a stochastic dynamic setting over a planning period of days, we consider a set of customers, each placing exactly one order on a known day. Each order is associated with an uncertain demand and can be either served or outsourced by paying a penalty. Customers have day windows that, if some flexibility is allowed, can be extended by anticipating or delaying the service by paying a penalty. At the end of each day, we decide which pending orders to serve on the following day by exploiting a heterogeneous fleet of vehicles. The goal is to minimize the total costs due to fleet sizing and customer inconvenience for anticipations, delays, and outsourcing. We formulate the problem as a Markov decision process and solve it with Approximate Dynamic Programming by integrating robust-optimization techniques in Direct Lookahead Approximation policies. Our study is motivated by an on-demand waste collection service. Nevertheless, the problem has applications in many other contexts, such as reverse logistics and technician routing [1, 2].

References
[1] S. Mishra and S. P. Singh, Designing dynamic reverse logistics network for post-sale., title, Annals of Operations Research, pages 1–30, 2022.
[2] M. Nowak and P. Szufel, Technician routing and scheduling for the sharing economy, European Journal of Operational Research, 2023.

Keywords

Status: accepted

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