A Novel Approach for Vehicle Fleet Sizing and Allocation under Uncertain Demand

PRESSER, DEMIAN J.; CAFARO, VANINA G.; CAFARO, DIEGO C.

Computer Aided Chemical Engineering

Elsevier

Lugar: Amsterdam; Año: 2022; p. 949 - 954

In this work, we propose a novel optimization model based on mixed integer linear programming (MILP) whose goal is to optimize the size and allocation of vehicle fleets under uncertain demand patterns. In contrast to previous contributions, the probability of meeting demand is a model decision and the approach is able to handle any probability distribution to characterize the number of trips required in the network. Furthermore, fleet procurement and contracting decisions for alternative types of vehicles are integrated into the model. The formulation allows to considerably reduce costs by avoiding oversizing and can be adapted to diverse demand characterizations and contracting terms from different industries. The proposed approach is able to tackle problems of real dimensions in very low computational times in comparison to previous works. A real-world case study involving maintenance operations in geographically spread assets is addressed to highlight the model capabilities and draw conclusions.