On Wednesday, September 16, 2026, from 12:00 to 13:30 CEST, the EU-funded HOLOGISTICS project (Holistically Optimised Logistics Operations and Green Innovative Solutions for Transport Cargo Sustainably, Grant Agreement No. 101202556) hosted an online workshop titled “Simulation Modelling as a Decision-Support Tool”. Co-moderated by Julian Stephens (MJC2) and Prof. Irina Yatskiv (Jackiva) (Transport and Telecommunication Institute – TSI), the session brought together 42 participants, including 35 connected industry/academic attendees.

The event provided a comprehensive platform to demonstrate how data-driven simulation environments, real-time analytics, and cloud-native digital twin architectures can de-risk green infrastructure investments and accelerate the transition toward zero-emission freight transport.

Agenda 

• Welcome and introduction – Julian Stephens (MJC2) and Prof. Irina Yatskiv (Jackiva) (TSI)  

• Simulation Modelling for Transport and Logistics Decisions – Prof. Irina Yatskiv (Jackiva) and Prof. Mihails Savrasovs (TSI)  

• Case Study 1: Applying Simulation to Compare Electric and Diesel Truck Fleets. Experience from the ePIcenter Project – Prof. Irina Yatskiv (Jackiva) and Prof. Jurijs Tolujevs (TSI)  

• Case Study 2: A Configurable Simulation Model for Flexible Rural Last-Mile Postal Delivery: A Latvijas Pasts Case Study – Prof. Irina Yatskiv (Jackiva), Prof. Mihails Savrasovs, and Prof. Jurijs Tolujevs (TSI)  

• Simulation-as-a-Service Paradigm for Transportation Systems – Prof. Mihails Savrasovs and Prof. Irina Yatskiv (Jackiva) (TSI)  

• Discussion: From Simulation Models to Decision Support – Julian Stephens (MJC2) and Prof. Irina Yatskiv (Jackiva) (TSI).

HOLOGISTICS Framework & Decision-Making Architecture

The workshop opened with a welcome and overview of the session structure by Julian Stephens and Prof. Irina Yatskiv. Julian Stephens presented the foundational framework of the HOLOGISTICS initiative—a 42-month Horizon Europe Innovation Action coordinated by the University of Modena and Reggio Emilia (UNIMORE), uniting 25 international partners with a total budget of €13.12 million (€9.88 million EU contribution). The project targets cargo decarbonization, multimodal efficiency, and synchromodality aligned with Physical Internet principles.

Following the opening, Prof. Irina Yatskiv and Prof. Mihails Savrasovs (TSI) introduced the theoretical foundations of data-driven decision-making and practical evaluation frameworks. They detailed a structured 7-step decision-making process: identifying the problem, gathering information, identifying alternatives, weighting evidence, choosing among alternatives, taking action, and reviewing the decision. They emphasized that simulation modeling provides an essential sandbox for testing operational alternatives before capital deployment, directly feeding Digital Twins and supporting evidence-based investments.

To illustrate long-term spatial planning, TSI presented a multi-phase simulation (2019–2036) evaluating the transport network capacity around Riga Airport (RIX) as it evolves into an Airport City and Aerotropolis. The model confirmed that planned road infrastructure and the integration of the new RailBaltic railway station will successfully absorb a projected 33% increase in traffic demand by 2036 without causing network degradation or adverse traffic impacts.

Empirical Case Studies: Heavy Fleets and Rural Last-Mile Logistics

The core technical agenda showcased two concrete case studies demonstrating the practical application of simulation tools across different transport domains:

Case Study 1: Comparing Electric and Diesel Truck Fleets

Presented by Prof. Irina Yatskiv and Prof. Jurijs Tolujevs (TSI), this study evaluated heavy freight operations using the TraPodSim simulation system (developed within the ePIcenter Horizon project using AnyLogic). The simulation modeled a 491.6 km regional freight loop connecting 8 cities in North Rhine-Westphalia, Germany, testing 5 charging station deployment strategies across 6 distinct fleet scenarios:

• Scenario 1 (Electric – 2 × eActros, 8t payload, L/U charging): Spent 1,004.99 kWh of energy, generated 348.73 kg of $\text{CO}_2$, with operating costs of €793.94.
• Scenario 2 (Electric – 1 × eEconic, 16t payload, L/U charging): Spent 562.54 kWh of energy, generated 195.20 kg of $\text{CO}_2$, with operating costs of €444.41.
• Scenario 3 (Electric – 2 × eActros, 8t payload, dedicated stations): Spent 1,062.36 kWh of energy, generated 368.64 kg of $\text{CO}_2$, with operating costs of €839.27.
• Scenario 4 (Electric – 1 × eEconic, 16t payload, dedicated stations): Spent 560.54 kWh of energy, generated 194.51 kg of $\text{CO}_2$, with operating costs of €442.83.
• Scenario 5 (Diesel Baseline – 2 × Actros 1835, 8t payload): Spent 234.04 L of fuel, generated 615.53 kg of $\text{CO}_2$, with operating costs of €432.98.
• Scenario 6 (Diesel Baseline – 1 × Actros 2543, 16t payload): Spent 135.72 L of fuel, generated 356.94 kg of $\text{CO}_2$, with operating costs of €251.08.

The results demonstrated that electric trucks yield a 40–45% reduction in $\text{CO}_2$ emissions compared to diesel fleets, though they currently face higher energy operational costs and charging downtime constraints.

Case Study 2: Flexible Rural Last-Mile Postal Delivery

Presented by Prof. Irina Yatskiv, Prof. Jurijs Tolujevs, and Prof. Mihails Savrasovs, this study addressed low-density rural logistics in the Saldus region, Latvia (in collaboration with Latvijas Pasts). The team modeled four flexible delivery schemes combining self-service, public transport integration, and volunteer networks. Over a 2-day scenario serving 22 customers, shifting from traditional diesel car trips (which generated 41.50 kg $\text{CO}_2$ over 259.35 km) to integrated public transit, motorcycle, and bicycle legs reduced consumer-side emissions down to 2.84 kg $\text{CO}_2$—a reduction of over 93%.

Simulation-as-a-Service (SIMaaS) & Interactive Discussion

Moving into future technological paradigms, Prof. Mihails Savrasovs and Prof. Irina Yatskiv presented the Simulation-as-a-Service (SIMaaS) framework. Inspired by NATO Modelling and Simulation as a Service (MSaaS) standards, SIMaaS transitions logistics modeling from monolithic applications to cloud-native, service-oriented environments. The architecture decouples functionality into dedicated layers:

• Data Layer: Handles ingestion, ETL, and time synchronization via Apache Kafka, InfluxDB, and PostgreSQL.
• Model Layer: Integrates transport engines such as AnyLogic, SUMO, MATSim, and ExtendSim.
• Service & Orchestration Layer: Utilizes REST APIs, gRPC, MQTT, OpenAPI, and FMI managed through Docker and Kubernetes for AI-driven scenario generation.
• Execution & Digital Twin Layer: Deploys high-performance computing (HPC) clusters and cloud/edge infrastructure integrated with Prometheus, Grafana, and FIWARE Orion Context Brokers for real-time recalibration.

Interactive Discussion & Closing

The workshop concluded with an interactive discussion session moderated by Julian Stephens and Prof. Irina Yatskiv. Panelists addressed audience questions regarding the inclusion of weather variables and travel time delays in multi-scenario parameterization, as well as the potential for training AI and machine learning algorithms on existing simulation models to enhance orchestration and accuracy.

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