A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System

Ângela F. Brochado (), Eugénio M. Rocha and Diogo Costa
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Ângela F. Brochado: Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal
Eugénio M. Rocha: Department of Mathematics (DMat) and Center for Research and Development in Mathematics and Applications (CIDMA), Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal
Diogo Costa: Department of Mechanical Engineering (DEM), Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal

Sustainability, 2024, vol. 16, issue 2, 1-22

Abstract: Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs.

Keywords: internet-of-things; data-driven system; synchromodal logistics; environmental factors; multi-criteria decision making; multimodal transportation; real-time scheduling and assignment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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