Decarbonisation of urban logistics is a pressing issue. About one third of the freight-related CO 2 emissions in the Netherlands relates to urban logistics, consisting of both vans and trucks. Although electrification is a feasible solution, delivery models that not only focus on reducing the carbon footprint, but also the spatial footprint are important. A one-to-one replacement of diesel vehicles with electric vehicles does not reduce urban logistics' spatial footprint in densifying cities nor the delivery vans' perceived nuisance. This paper examines the impact of alternative delivery models in the parcel- and home delivery segment in four future scenarios on CO 2 emissions, vehicle kilometres and number and type of vehicles used (2030). Analyses are based on data from three companies in a large metropolitan region in the Netherlands. The results show the impact of vehicles fleets electrification, transhipment in consolidation points and a network of pickup points. This study illustrates that developing alternative last mile networks can result in a decrease in vehicle (van) movements, and with that a serious decrease in emissions. The implications of the results on the carbon footprint, urban space usage and costs for companies are discussed.
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Assistive technology supports maintenance or improvement of an individual’s functioning and independence, though for people in need the access to assistive products is not always guaranteed. This paper presents a generic quality framework for assistive technology service delivery that can be used independent of the setting, context, legislative framework, or type of technology. Based on available literature and a series of discussions among the authors, a framework was developed. It consists of 7 general quality criteria and four indicators for each of these criteria. The criteria are: accessibility; competence; coordination; efficiency; flexibility; user centeredness, and infrastructure. This framework can be used at a micro level (processes around individual users), meso level (the service delivery scheme or programme) or at a macro level (the whole country). It aims to help identify in an easy way the main strengths and weaknesses of a system or process, and thus guide possible improvements. As a next step in the development of this quality framework the authors propose to organise a global consultancy process to obtain responses from stakeholders across the world and to plan a number of case studies in which the framework is applied to different service delivery systems and processes in different countries.
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Consumers expect product availability as well as product quality and safety in retail outlets. When designing or re-designing fruit and vegetables supply chain networks one has to take these demands into consideration next to traditional efficiency and responsiveness requirements. In food science literature, much attention has been paid to the development of Time-Temperature Indicators to monitor individually the temperature conditions of food products throughout distribution as well as quality decay models that are able to predict product quality based upon this information. This chapter discusses opportunities to improve the design and management of fruit and vegetables supply chain networks. If product quality in each step of the supply chain can be predicted in advance, good flows can be controlled in a pro-active manner and better chain designs can be established resulting in higher product availability, higher product quality, and less product losses in retail. This chapter works towards a preliminary diagnostic instrument, which can be used to assess supply chain networks on QCL (Quality Controlled Logistics). Findings of two exploratory case studies, one on the tomato chain and one on the mango chain, are presented to illustrate the value of this concept. Results show the opportunities and bottlenecks for quality controlled logistics depend on product—(e.g. variability in quality), process—(e.g. ability to use containers and sort on quality), network- (e.g. current level of cooperation), and market characteristics (e.g. higher prices for better products).
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DISCO aims at fast-tracking upscaling to new generation of urban logistics and smart planning unblocking the transition to decarbonised and digital cities, delivering innovative frameworks and tools, Physical Internet (PI) inspired. To this scope, DISCO will deploy and demonstrate innovative and inclusive urban logistics and planning solutions for dynamic space re-allocation integrating urban freight at local level, within efficiently operated network-of-networks (PI) where the nodes and infrastructure are fixed and mobile based on throughput demands. Solutions are co-designed with the urban logistics community – e.g., cities, logistics service providers, retailers, real estate/public and private infrastructure owners, fleet owners, transport operators, research community, civil society - all together moving a paradigm change from sprawl to data driven, zero-emission and nearby-delivery-based models.
The project aim is to improve collusion resistance of real-world content delivery systems. The research will address the following topics: • Dynamic tracing. Improve the Laarhoven et al. dynamic tracing constructions [1,2] [A11,A19]. Modify the tally based decoder [A1,A3] to make use of dynamic side information. • Defense against multi-channel attacks. Colluders can easily spread the usage of their content access keys over multiple channels, thus making tracing more difficult. These attack scenarios have hardly been studied. Our aim is to reach the same level of understanding as in the single-channel case, i.e. to know the location of the saddlepoint and to derive good accusation scores. Preferably we want to tackle multi-channel dynamic tracing. • Watermarking layer. The watermarking layer (how to embed secret information into content) and the coding layer (what symbols to embed) are mostly treated independently. By using soft decoding techniques and exploiting the “nuts and bolts” of the embedding technique as an extra engineering degree of freedom, one should be able to improve collusion resistance. • Machine Learning. Finding a score function against unknown attacks is difficult. For non-binary decisions there exists no optimal procedure like Neyman-Pearson scoring. We want to investigate if machine learning can yield a reliable way to classify users as attacker or innocent. • Attacker cost/benefit analysis. For the various use cases (static versus dynamic, single-channel versus multi-channel) we will devise economic models and use these to determine the range of operational parameters where the attackers have a financial benefit. For the first three topics we have a fairly accurate idea how they can be achieved, based on work done in the CREST project, which was headed by the main applicant. Neural Networks (NNs) have enjoyed great success in recognizing patterns, particularly Convolutional NNs in image recognition. Recurrent NNs ("LSTM networks") are successfully applied in translation tasks. We plan to combine these two approaches, inspired by traditional score functions, to study whether they can lead to improved tracing. An often-overlooked reality is that large-scale piracy runs as a for-profit business. Thus countermeasures need not be perfect, as long as they increase the attack cost enough to make piracy unattractive. In the field of collusion resistance, this cost analysis has never been performed yet; even a simple model will be valuable to understand which countermeasures are effective.
