Lóránt Tavasszy

% VERIFIED; verified 2026-10-11
@article{orbit-0ff17dc05ca7a7fc,
  title = {Trade-offs in multi-channel delivery network design with perishable and non-perishable goods},
  author = {Cao, Luyang and Tavasszy, Lóránt and Stokkink, Patrick},
  journal = {Journal of Supply Chain Management Science},
  year = {2025},
  volume = {6},
  number = {3-4},
  doi = {10.59490/jscms.2025.8229},
  url = {https://doi.org/10.59490/jscms.2025.8229},
  issn = {2451-9901},
  publisher = {TU Delft OPEN Publishing},
  abstract = {This study focuses on optimizing last-mile delivery in e-commerce by balancing cost efficiency and customer preferences, particularly for mixed perishable and non-perishable goods distribution. As online grocery shopping grows, ensuring the timely and efficient delivery of perishable products while maintaining quality remains a critical challenge. The problem is modelled as a variant of the multi-objective Vehicle Routing Problem (VRP), where customer utility and operational costs are incorporated as two objectives. Customer utility is computed with parameters estimated using the Best-Worst Method (BWM). The multi-objective model is solved by linearizing the non-linear objectives and using a weighted sum method. The model evaluates home delivery, attended pickup points, and lockers, revealing that cost-driven strategies shift deliveries toward self-pickup, with perishable items primarily assigned to attended pickup points due to temperature control. The findings provide insights for improving delivery network design, enhancing service quality, and optimizing the distribution of both perishable and non-perishable products.},
  keywords = {Last mile delivery, Perishable goods logistics, Vehicle routing problem, Mixed integer linear programming, Best-World Method},
}

% VERIFIED; verified 2026-10-11
@article{orbit-ee16586e0e1c3d14,
  title = {A data-to-value framework for freight ITS: Insights from a living lab},
  author = {Vanga, Ratnaji and Thiyagarajan, Nagasubramanian and Gelper, Sarah and Maknoon, Yousef and Duinkerken, Mark B. and Tavasszy, Lóránt A.},
  journal = {Research in Transportation Business \& Management},
  year = {2024},
  volume = {57},
  pages = {101214},
  eid = {101214},
  doi = {10.1016/j.rtbm.2024.101214},
  url = {https://doi.org/10.1016/j.rtbm.2024.101214},
  issn = {2210-5395, 2210-5409},
  publisher = {Elsevier BV},
  abstract = {The emergence of Intelligent Transportation Systems (ITS) for freight transport in recent times has created interest among practitioners and researchers to extend freight ITS to support broader logistics processes, including dynamic tour scheduling, loading and unloading, warehousing, and even production. However, connecting transport data, ITS and logistics information systems require collaboration between different organizations and new business models to create business value for logistics actors. It is critical for these stakeholders to consider how their business models connect to create meaningful new data-to-information value chains. This study develops a conceptual framework to identify opportunities for logistics value creation with freight transport data. Building on the literature we construct a framework that reconciles multi-firm and firm-level business modelling. The main component is a generalized framework for Data-to-Value (DtV) chains for applications in information and communications technology. In order to support its business validity, we extend this framework with Business Model Canvases (BMCs) of the actors in the value chain. Three real-life use cases from a freight ITS community in the Netherlands are used to evaluate and illustrate the framework.},
  keywords = {Business ecosystems, Business models, Co-design, Data-to-value chains, Freight transportation, Intelligent transport systems},
}

% VERIFIED; verified 2026-10-11
@article{orbit-e3881cc9067f2ae2,
  title = {A Predictive–Proactive Approach for Slot Management of a Loading Facility With Truck ETA Information},
  author = {Prakoso, Emanuel Febrianto and Maknoon, Yousef and Pel, Adam and Tavasszy, Lóránt A. and Vanga, Ratnaji},
  journal = {Frontiers in Future Transportation},
  year = {2022},
  volume = {3},
  eid = {815267},
  doi = {10.3389/ffutr.2022.815267},
  url = {https://doi.org/10.3389/ffutr.2022.815267},
  issn = {2673-5210},
  publisher = {Frontiers},
  abstract = {Due to the uncertain and dynamic environment around scheduling systems, timely revisions or reschedules of the master plans are essential for achieving optimal utilization. With the recent development of Industry 4.0 technologies, many researchers perceive the creation of cyber-physical systems as a solution for managing systems under uncertainty. This article focuses on a loading facility under uncertain truck arrivals due to road congestion and proposes utilizing real-time truck location information to improve performance. We do this by developing an integrated system consisting of a predictive model using machine learning (MC) classifiers and a mathematical model for real-time slot rescheduling. The ML classifier is used to predict the presence probabilities of all the incoming trucks at a particular slot based on the historical traffic data and the real-time truck location. Subsequently, a Mixed-Integer Quadratic Programming (MIQP) model is developed to solve a Probabilistic Slot Rescheduling Problem (P-SRP), which uses the estimated truck presence probabilities and minimizes the total expected cost of rescheduling. We implemented this by first testing multiple ML classifiers and selected the ANN classifier for prediction as it outperformed others. Our limited experiments showed that the proposed method reduced the total rescheduling cost by 42 \% . Furthermore, our sensitivity analysis with different congestion levels, complexity, and rescheduling strategy also showed the practicality of the proposed approach.},
  keywords = {Real-time information, Proactive rescheduling, Truck visibility, Probabilistic optimization, machine learning, Uncertain arrivals},
}

% VERIFIED; verified 2026-10-11
@article{orbit-c94bf03ae9e90cd7,
  title = {Hinterland freight transportation replanning model under the framework of synchromodality},
  author = {Qu, Wenhua and Rezaei, Jafar and Maknoon, Yousef and Tavasszy, Lóránt},
  journal = {Transportation Research Part E: Logistics and Transportation Review},
  year = {2019},
  volume = {131},
  pages = {308--328},
  doi = {10.1016/j.tre.2019.09.014},
  url = {https://doi.org/10.1016/j.tre.2019.09.014},
  issn = {1366-5545, 1878-5794},
  publisher = {Elsevier BV},
  abstract = {Hinterland freight transportation is managed according to a pre-designed schedule. In daily operations, unexpected uncertainties cause deviation from the original plan. Thus replanning is needed to deal with the perturbations and complete the transportation tasks. This paper proposes a mixed-integer programming model to re-plan hinterland freight transportation, based on the framework of synchromodality. It is a holistic resolution of shipment flow rerouting, consequence transshipment organization in the intermediate terminals, and corresponding service rescheduling. The replanning benefits from a high operational flexibility and coordination via a split of shipment and aligning the departure time of service flows with the shipment flows.},
  keywords = {Freight transportation replanning, Shipment split, Synchromodality, Synchronization of shipment and services, Uncertainty management},
}