Profit maximization via capacity control for distribution logistics problems. (September 2022)

• Record Type: Journal Article
• Authors: Giallombardo, Giovanni; Guerriero, Francesca; Miglionico, Giovanna
• Is Part Of: Computers & industrial engineering. Volume 171(2022)
• Publication Date: 2022-09
• Subjects: Revenue management -- Dynamic programming -- Capacity-control policies -- Logistics -- Vehicle-routing; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108466
• Languages: English
• ISSNs: 0360-8352

Abstract

Abstract: We consider a distribution logistics scenario where a shipping operator, managing a limited amount of resources, receives a stream of service requests, issued by a set of customers along a booking time-horizon, that are referred to a future operational period. The shipping operator must then decide about accepting or rejecting each incoming request at the time it is issued, accounting for revenues, but also considering resource consumptions. In this context, the decision process is based on dynamically finding the best trade-off between the immediate return of accepting the request and the convenience of preserving capacity to possibly exploit more valuable future requests. We give a dynamic formulation of the problem aimed at maximizing the operator revenues, accounting also for the operational distribution costs. Due to the "curse of dimensionality", the dynamic program cannot be solved optimally. For this reason, we propose a mixed-integer linear programming approximation, whose exact or approximate solutions provide the relevant information to apply some commonplace revenue management policies in the real-time decision-making. Adopting a capacitated vehicle routing problem as an underlying distribution application, we analyze the computational behavior of the proposed techniques on a set of academic test problems. Highlights: We study revenue management capacity-control problems in the distribution logistics field. The decisions depend on the revenue of the current request, the future revenues and operational costs. A dynamic formulation of the problem aimed at maximizing the profit is developed. Revenue management policies are defined and evaluated empirically.