Abstract

The purpose of this paper is to provide a methodologically sound instrument to assess the long-term penetration of innovative propulsion systems vis á vis the technologies that currently dominate the market. Towards this end, it simulates the diffusion process of four propulsion technologies (gasoline ICE, LPG ICE, EV, and fuel cells) by taking endogenously into account the effect of learning-by- doing at the manufacturing level. The analysis is extended to the EU zone, it encompasses a time horizon of 20 years and it is repeated for three different policy scenarios, namely: a business-as-usual scenario, an incentive scenario (where financial aids are envisaged for the purchase of non-polluting vehicles) and a production quota scenario (where vehicle manufacturer produce a minimum quota of zero-emission vehicles). The results suggest that - without any specific intervention - the EU urban transportation market will continue to be significantly dominated by ICE vehicles. However, they also indicate that this trend can be reverted by sound policies aimed at supporting the development of new transportation technologies. In particular it is argued that policies that leverage the learning curve of the new technologies during the very early stage of their life cycle are more effective than those that envisage a generalized support dispersed over a longer time horizon.

Abstract

International audience; Transportation is an important component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routes is a crucial management problem. In this paper, a vehicle routing problem with dynamic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture travel times. The queueing approach is compared with other approaches and its potential benefits are described and quantified. Moreover, the optimization of the starting times of a route at the distribution center is evaluated. Finally, the trade-off between solution quality and calculation time is discussed. Numerous test instances are used, both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment, which is usually the case on European road networks.

Abstract

gning and scheduling vehicle routes in a dynamic environment is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic and dynamic nature of travel times. In this paper, a vehicle routing problem with time-dependent travel times due to potential traffic congestion is considered. The approach developed introduces the traffic congestion component based on queueing theory. This is an innovative modelling scheme to capture the stochastic behavior of travel times as it generates an analytical expression for the expected travel times as well as for the variance of the travel times. Routing solutions that perform well in the face of the extra complications due to congestion are developed. These more realistic solutions have the potential to reduce real operating costs for a broad range of industries which daily face routing problems. A number of datasets are used to illustrate the appropriateness of the novel approach. Moreover it is shown that static (or time-independent) solutions are often infeasible within a congested traffic environment which is generally the case on European road networks. Finally, the effect of travel time variability (obtained via the queueing approach) is quantified for the different datasets.