Logistics Research

All over the world, road congestion is among the most prevalent transport challenges usually in urban environments which not only increases fuel consumption and emission of harmful gases, but also causes stress for the drivers. Intelligent Transportation System (ITS) enables a better use of the infrastructure by connecting vehicles to other vehicles as well as infrastructure and thus delivers a faster communication opportunity to ensure safe and secure driving. Machine-to-machine (M2M) communication is one of the latest information and communication technologies which offers ubiquitous connectivity among several smart devices. The use of mobile (cellular) M2M communications has emerged due to the wide range, high reliability, increased data rates, decreased costs as well as easy and short-term deployment opportunities. Since the radio spectrum is a scarce resource, M2M traffic can potentially degrade the performance of mobile networks due to the large number of devices sending small-sized data. This paper presents an efficient data multiplexing scheme by using Long-Term Evolution Advanced (LTE-Advanced) Relay Nodes, which aggregates M2M traffic to maximize radio resource utilization. Extensive system-level simulations are performed using an LTE-Advanced-based model developed in the RIVERBED modeler to evaluate the performance of the proposed data multiplexing scheme. Simulation results show that approximately 40 % more smart M2M devices used in ITS and logistics are served per LTE-Advanced cell under the present system settings.

Issues related to flows of goods and information are frequently discussed in the logistics and Supply Chain Management literature. But, only few contributions are exploring the financial flows associated with supply chains. This article reviews the state-of-the-art of research regarding financial flows in supply chains. In doing so, it becomes apparent that an explicit examination and optimisation of the cost of capital has been missing so far. In order to close this gap, a conceptual framework and a mathematical model of “Supply Chain Finance” is proposed.

Regarding the amount of processed information, there are two basic approaches to handle today’s increasing requirements for facility logistics systems. First, the collection of all achievable information about the system and its load in order to predict future system states accurately; theoretically, this could lead to optimal results. Second, the use of rather simple heuristics with less need for information but higher flexibility and robustness instead. This approach is for instance being realized with the Internet of Things in facility logistics. This paper analyses the dependency of throughput time and capacity utilization on available information for two limiting cases. While a ‘best possible’ case assumes that the material flow control has all relevant information about the system and loads, a ‘worst reasonable’ case considers the same for very limited information. The influence of layout, throughput, and element availabilities on this relationship is analyzed for both cases on the basis of a scalable generic test system and steady flow situations. In total, nine simulation studies are conducted and analyzed regarding throughput time and element utilization as performance indicators. Implications for the importance of information on performance depending on system complexity are discussed, and directions for further research are provided.