Data‑Driven Industrial Engineering for Sustainable Transportation and Logistics

Call for Papers – Data‑Driven Industrial Engineering for Sustainable Transportation and Logistics

Journal: Transportation Research Part E: Logistics and Transportation Review
Publisher: Elsevier
Submission deadline: 31 December 2026

This special issue explores how industrial‑engineering methods and data‑driven technologies can redesign transportation and logistics systems to be more sustainable, resilient, and intelligent across freight, urban delivery, industrial, and humanitarian contexts. It explicitly links optimization, simulation, human‑centric design, and systems‑integration principles with AI, machine learning, digital twins, and IoT‑based analytics to support greener, safer, and more adaptive logistics operations.

Why this issue matters

• Transportation and logistics are major contributors to global emissions and resource use, yet they face rising pressures from decarbonization targets, supply‑chain disruptions, and digitalisation.

• Industrial engineering offers methodological tools (e.g., process modelling, layout and inventory optimization, human‑factor analysis) that can structure and implement data‑driven innovations in logistics and supply chains.

• This SI bridges industrial engineering and transportation research, highlighting how AI, digital twins, IoT, and predictive analytics can be embedded in sustainable, circular‑economy‑oriented logistics designs.

Key topic areas

Contributions may address, but are not limited to:

• AI, ML, and big‑data analytics for logistics

  • AI‑ and ML‑based optimization of freight and passenger flows, routing, and scheduling.

  • Real‑time decision support and dynamic adjustment of logistics plans under uncertainty.

• Digital‑twin‑based planning and simulation

  • Digital‑twin platforms for smart ports, warehouses, and last‑mile delivery systems.

  • Simulation‑driven design of sustainable logistics nodes and multimodal hubs.

• IoT‑driven asset management

  • Predictive maintenance and reliability modelling of vehicles, containers, and infrastructure.

  • Data‑driven forecasting of equipment failures and degradation patterns.

• Sustainable and circular logistics

  • Green transportation strategies and decarbonisation of logistics networks.

  • Reverse logistics, closed‑loop systems, and circular economy models for waste reduction and product reuse.

• Resilience and disruption management

  • Data‑driven disruption management, predictive risk modelling, and adaptive supply‑chain strategies.

  • Logistics antifragility and viability under climate, geopolitical, or pandemic‑type shocks.

• Human‑centric logistics and AI

  • Behavioral modelling of drivers, operators, and consumers in transportation and logistics.

  • Human‑centric AI and digitalisation designs that maintain worker safety, well‑being, and trust.

• ESG and sustainability performance

  • ESG measurement and sustainability indicators for transportation and logistics networks.

  • Industrial‑engineering methods to audit and improve environmental, social, and governance performance.

Submissions should be original, methodologically rigorous, and clearly demonstrate how industrial‑engineering approaches and data‑driven techniques jointly advance sustainable and resilient logistics practice.

Guest editors

• Masood Fathi, University of Skövde, Sweden / Uppsala University, Sweden

• Taha Arbaoui, Institut National des Sciences Appliquées (INSA), Lyon, France

• Daria Battini, University of Padua, Italy

• Alexandre Dolgui, IMT Atlantique, France

• Khaled Hadj‑Hamou, Institut National des Sciences Appliquées (INSA), Lyon, France

Submission information

• Submission portal:
  https://www.editorialmanager.com/tre

• When submitting, select article type:
  “VSI: CIE52”

• Submission deadline: 31 December 2026

• All manuscripts must follow the journal’s Guide for Authors:
  https://www.elsevier.com/journals/transportation-research-part-e-logistics-and-transportation-review/1366‑5545/guide‑for‑authors

Papers will be peer‑reviewed on originality, methodological quality, and practical relevance, with an emphasis on actionable insights for managers, policymakers, and industrial‑engineering practitioners in the logistics and transportation domain.

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