Abstract

Logistic Service Providers (LSPs) offering hinterland transportation face the trade-off between efficiently using the capacity of long-haul vehicles and minimizing the first and last-mile costs. To achieve the optimal trade-off, freights have to be consolidated considering the variation in the arrival of freight and their characteristics, the applicable transportation restrictions, and the interdependence of decisions over time. We propose the use of a Markov model and an Approximate Dynamic Programming (ADP) algorithm to consolidate the right freights in such transportation settings. Our model incorporates probabilistic knowledge of the arrival of freights and their characteristics, as well as generic definitions of transportation restrictions and costs. Using small test instances, we show that our ADP solution provides accurate approximations to the optimal solution of the Markov model. Using larger problem instances, we show that our modeling approach has significant benefits when compared to common-practice heuristic approaches. Document type: Part of book or chapter of book

Abstract

It has been predicted that electric vehicles will play a crucial role in incorporating a large renewable component in the energy sector. If electric vehicles are integrated in a naive way, they may exacerbate issues related to peak demand and transmission capacity limits while not reducing polluting emissions. Optimizing the charging of electric vehicles is paramount for their successful integration. This paper presents a model to describe the driving patterns of electric vehicles in order to provide primary input information to any mathematical programming model for optimal charging. Specifically, an inhomogeneous Markov model that captures the diurnal variation in the use of a vehicle is presented. The model is defined by the time-varying probabilities of starting and ending a trip, and is justified due to the uncertainty associated with the use of the vehicle. The model is fitted to data collected from the actual utilization of a vehicle. Inhomogeneous Markov models imply a large number of parameters. The number of parameters in the proposed model is reduced using B-splines. Document type: Book