Skip to main content

Transportation Research Part E: Logistics and Transportation Review

  • Volume 12Issue 12

  • ISSN: 1366-5545
  • 5 Year impact factor: 9.2
  • Impact factor: 8.3

Transportation Research Part E: Logistics and Transportation Review (TR-E) is differentiated from its sister journals (TR-A, TR-B, TR-C, TR-D, and TR-F). As reflected in the… Read more

Subscription options

Institutional subscription on ScienceDirect

Request a sales quote

Transportation Research Part E: Logistics and Transportation Review (TR-E) is differentiated from its sister journals (TR-A, TR-B, TR-C, TR-D, and TR-F). As reflected in their title, the commonality between these journals is the focus on ‘Transportation,’ but TR-E is differentiated by specializing in ‘Logistics.’ Of course, it is widely accepted that transportation is undoubtedly one of the most critical components of logistics. TR-E publishes informative and high-quality articles drawn from across the spectrum of logistics components. The related research studies are multi-disciplinary and include (i) hard/ classic logistics research, such as transportation, material handling, packaging, warehousing, inventory, and handling, and so on (ii) soft logistics research by adding Operations Management (OM) and Supply Chain Management (SCM) concepts, tools, and philosophies to the classic logistics, such as sustainability, risk and disruption, circular economy, and artificial intelligence.

There are no limitations to the research methods utilized. Therefore, various research methods can be used, such as analytical (e.g., operations research techniques including game theory, queuing theory, dynamic programming, linear, integer, and nonlinear programming), quantitative and qualitative empirical research (e.g., time series, regression, microeconomics), simulation, mixed research methods (e.g., combining surveys and case studies with quantitative data analysis), experimental research (e.g., controlled experiments, lab experiments, and field experiments), case studies (e.g., in-depth analysis), machine learning, artificial intelligence and network analysis (e.g., graph theoretic concept).