The last-mile in the courier express parcel (CEP) sector is the most challenging part of the overall transport chain. This is, among other reasons, because many recipients are not at home when deliveries take place. On the other hand, it is for many recipients inconvenient that they have to collect their parcels at different pickup shops varying from logistics service provider (LSP) to LSP. One solution is to employ (open) parcel lockers which are conveniently located for recipients and which allow successful (first) deliveries for LSPs. In this paper, we investigate the impact of parcel lockers with respect to traveled distances as well as CO2 emissions. We show that under certain situations, parcel lockers positively contribute to both aforementioned performance indexes. Based on our observations, we formulate recommendations how to support the implementation of parcel lockers.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this article is to propose and evaluate a novel system architecture for Smart City applications which uses ontology reasoning and a distributed stream processing framework on the cloud. In the domain of Smart City, often methodologies of semantic modeling and automated inference are applied. However, semantic models often face performance problems when applied in large scale. Design/methodology/approach -The problem domain is addressed by using methods from Big Data processing in combination with semantic models. The architecture is designed in a way that for the Smart City model still traditional semantic models and rule engines can be used. However, sensor data occurring at such Smart Cities are pre-processed by a Big Data streaming platform to lower the workload to be processed by the rule engine. Findings -By creating a real-world implementation of the proposed architecture and running simulations of Smart Cities of different sizes, on top of this implementation, the authors found that the combination of Big Data streaming platforms with semantic reasoning is a valid approach to the problem. Research limitations/implications -In this article, real-world sensor data from only two buildings were extrapolated for the simulations. Obviously, real-world scenarios will have a more complex set of sensor input values, which needs to be addressed in future work. Originality/value -The simulations show that merely using a streaming platform as a buffer for sensor input values already increases the sensor data throughput and that by applying intelligent filtering in the streaming platform, the actual number of rule executions can be limited to a minimum.

This paper highlights how the domain of Smart Cities is often modeled by ontologies to create applications and services that are highly flexible, (re)configurable, and inter-operable. However, ontology repositories and their accompanying reasoning and rule languages face the disadvantage of bad runtime behavior, especially if the models grow large in size. We propose an architecture that uses tools and methods from the domain of Big Data processing in conjunction with an ontology repository and a rule engine to overcome potential performance bottlenecks that will occur in this scenario.

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