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A novel data-driven engine for identifying trailer capacity sharing opportunities during shipment planning stages is developed for providing a practical solution for collaborative freight logistics. The engine is based on a novel trailer capacity graph (TCG) that describes the real-time trailer capacity status of all collaborating partners. Compared with previous methods that replying on OD of shipments and trailer trips, the application of a TCG enables this engine to match shipments with both the OD and the route of trailer trips. Moreover, special treatments of a TCG, namely trailer route approximation and route shape simplification, dramatically reduce the computational cost, which makes this engine extendable for serving more than two collaborating partners in real-time. Experiments using this engine based on real-world data provided by two companies first illustrate that users of this engine should be aware that the configuration of the treatments of TCG is a trade-off between computational performance and effectiveness in identifying opportunities. Secondly, experiment results suggest that this two-partners collaboration implies an imbalance favourable towards the smaller operator. However, this unfavourable imbalance for the larger operator is likely to reduce as the number of operators joining the collaboration increases.

Most design optimisation studies for district heating systems have focused on the optimal sizing of network assets and on the location of production units. However, the strategic value of the flexibility in phasing of the inherently modular heat networks, which is an important aspect in many feasibility studies for district heating schemes in the UK, is almost always neglected in the scientific literature. This paper considers the sequential problem faced by a decision-maker in the phasing of long-term investments into district heating networks and their expansions. The problem is formulated as a multi-stage stochastic programme to determine the annual capital expenditure that maximises the expected net present value of the project. The optimisation approach is illustrated by applying it to the hypothetical case of the UK’s Marston Vale eco town. It was found that the approach is capable of simulating the optimal growth of a network, from both a single heat source or separate islands of growth, as well as the optimal marginal expansion of an existing district heating network. The proposed approach can be used by decision makers as a framework to determine both the optimal phasing and extension of district heating networks and can be adapted simply to various, more complex real-life situations by introducing additional constraints and parameters. The versatility of the base formulation also makes it a powerful approach regardless of the size of the network and also potentially applicable to cooling networks., International Journal of Sustainable Energy Planning and Management, Vol 9 (2016)

This paper summarises the lessons learned from using electric freight vehicles (EFVs) for urban freight transport in three dimensions. First we look at the technical and operational suitability of using EFVs, including energy efficiency, range and its seasonal variations, and charging and local grid capacity issues. Then we examine all the elements which affect the business case from a carrier’s perspective, including changes of business model by the size of the vehicle, changes in value network and the elements required to enable a transition towards a wider-scale electrification. Finally, we present the environmental impacts from running EFVs at three levels, including direct impact analysis from the project demonstration activities, impact analysis at a wider uptake level by using traffic models and impact monetisation. Data used for this analysis is collected under the European FP7 project FREVUE, where around 80 electric-powered vehicles were deployed in the cities of Amsterdam, Lisbon, London, Madrid, Milan, Oslo, Rotterdam, and Stockholm.