EURO Journal on Transportation and Logistics

Special issue: Data analytics in logistics and transportation

Guest editors: Y. Adulyasak, J.-Fr. Cordeau and A. Lodi

  • Deadline to submit manuscripts: January 15, 2016
Over the past two decades, the increase in the size and complexity of logistics and transportation networks has been accompanied by a similar growth in the amount of data available to decision makers. This growth is largely fueled by developments in information technologies, in particular the widespread use of sensors, tracking devices, and mobile communication equipment. The abundance of up-to-date information not only improves visibility and traceability but also creates opportunities for the development of decision-support tools to improve the design, planning and control of logistics and transportation activities. This special issue aims at reporting on new models, algorithms and case studies related to the use of data analytics in the field of logistics and transportation. We are particularly interested in methods combining ideas from statistics, econometrics, optimization and simulation. Topics of interest include (but are not limited to):
  • Descriptive analytics and knowledge discovery: methods and algorithms to analyze large-scale datasets with the objective of identifying relationships, interactions and dependencies between different components in a logistics or transportation system (e.g., analysis of throughput and shipment data to make slotting and space allocation decisions in a warehouse);
  • Predictive analytics: statistical and data mining techniques to model and predict demand, travel times, lead times, trends, risks and other factors necessary for the planning and operation of logistics and transportation networks, both for freight and passengers;
  • Data-driven decision algorithms: optimization algorithms that make use of information extracted from large-scale datasets, algorithms that make decisions under uncertainty, and machine-learning algorithms applied to problems arising in the areas of production planning, inventory control, warehousing, and distribution management;
  • Data-driven business models: new optimization models that are centered on a new understanding of applications in logistics and transportation due to the availability and analysis of collected data;
  • Industrial applications of data analytics: innovative and successful applications of data analytics techniques to practical problems in logistics and transportation.

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