Optimization-Based Decision-Making Models in Rail Systems Engineering

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2425

Special Issue Editors

Department of Transportation Engineering, Faculty of Civil Engineering, University of Zagreb, Fra Andrije Kačića-Miošića 26, 10000 Zagreb, Croatia
Interests: railway engineering; urban transport; traffic systems; infrastructure monitoring

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Guest Editor
Department of Civil, Building and Environmental Engineering, Transport Area, University of Rome ‘‘La Sapienza’’, Via Eudossiana 18, 00184 Rome, Italy
Interests: transport systems engineering; railway and maritime transport
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Guest Editor
Department of Transport Infrastructure and Water Resources Engineering, Széchenyi István University, H-9026 Győr, Hungary
Interests: railroad tracks; railway; discrete element; deep learning; surface defect; digital image correlation; strain measurement; image analysis; additive manufacturing; 3D printing; fused filament fabrication; preventive maintenance; railway transport; mathematical model; numerical methods; topology optimization; sustainable development; internet of things; lithium-ion batteries; state of charge; battery; transport safety; driver distraction; image processing; cyber physical systems; virtual reality; digital twin; energy efficiency; regenerative braking; dynamic response; finite element method; ground penetrating radar; reinforced concrete; compressive strength; rubber crumb; ultimate tensile strength; energy storage; creep; unmanned aerial vehicle; drone; temperature distribution; box girder bridges; structural health monitoring; concrete sleeper; friction material; tribology; wear property; steel structure; strain; simulation model; material handling; discrete event simulation; reinforced plastic; energy absoration
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Special Issue Information

Dear Colleagues,

The daily work of railway engineers and managers consists of making a series of decisions, the quality of which is crucial to the efficiency and resiliency of the rail system. They face many challenges in their decision-making, including large and complex infrastructure asset networks; limited resources, time and information; a range of outcomes; conflicting goals; and uncertainties.

Planning, designing, and maintaining a rail system involves accounting for various multi-dimensional influential factors and conditions (including the topography, travel demand patterns, and land use and value) and numerous interconnected objectives, needs, and limitations that arise from the stakeholders involved (for example, the costs, operation safety, accessibility, punctuality, and travel time). Inadequate decisions often lead to longer travel times, inefficient space use, and overall resource wastage.

Therefore, this Special Issue will present and disseminate the most recent advances in optimization-based decision-making modelling, which is essential in developing and maintaining a reliable and cost-effective rail system. Topics of interest include, but are not limited to, the following:

  • Project evaluation models supporting decisions in developing new infrastructure and rehabilitating existing infrastructure;
  • Routing, alignment, and construction dynamics optimization models;
  • Track and vehicle interaction models describing influences on system design and performance;
  • Condition-based track maintenance models for proactive maintenance strategies;
  • Transport planning and scheduling models.

Dr. Maja Ahac
Prof. Dr. Stefano Ricci
Prof. Dr. Szabolcs Fischer
Guest Editors

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Keywords

  • optimization 
  • infrastructure evaluation 
  • vehicle dynamics 
  • track degradation 
  • predictive maintenance 
  • transport organization

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Published Papers (2 papers)

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Research

32 pages, 5130 KiB  
Article
Dynamic Optimization of Tunnel Construction Scheduling in a Reverse Construction Scenario
by Jianying Wei, Yuming Liu, Xiaochun Lu, Rong Zhao and Gan Wang
Systems 2025, 13(3), 168; https://doi.org/10.3390/systems13030168 - 27 Feb 2025
Viewed by 606
Abstract
Tunnel construction, a critical aspect of railway engineering, is a repetitive process with distinct linear characteristics. While the Linear Scheduling Method (LSM) is widely used for scheduling optimization in linear projects, it struggles to accommodate dynamic construction sequences, reverse construction, and flexible team [...] Read more.
Tunnel construction, a critical aspect of railway engineering, is a repetitive process with distinct linear characteristics. While the Linear Scheduling Method (LSM) is widely used for scheduling optimization in linear projects, it struggles to accommodate dynamic construction sequences, reverse construction, and flexible team allocation. Minimizing the project duration is a primary objective in tunnel construction scheduling optimization. To optimize tunnel construction, we propose a duration-shortening method using additional working surfaces, adaptable to multi-segment and multi-team scenarios. A dynamic optimization model is developed for tunnel construction scheduling, integrating LSM, soft logic, Work Breakdown Structure (WBS), and Resource Breakdown Structure (RBS) within a dynamic scheduling framework. This model analyzes logical relationships, work continuity, temporal and spatial constraints, and resource variation, focusing on reverse construction. The Mixed-Integer Programming (MIP) approach is used to build the mathematical model, solved with both exact algorithms and Genetic Algorithms (GA), and implemented in Python 3.12.7. Both algorithms perform well, with the GA excelling at handling complex constraints. Case studies confirm the method’s effectiveness in optimizing durations, devising flexible schedules, and improving efficiency and practicality. This research provides both theoretical insights and practical guidance for tunnel construction scheduling optimization in railway engineering. Full article
(This article belongs to the Special Issue Optimization-Based Decision-Making Models in Rail Systems Engineering)
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21 pages, 9737 KiB  
Article
Crowd Management at Turnstiles in Metro Stations: A Pilot Study Based on Observation and Microsimulation
by Sebastian Seriani, Vicente Aprigliano, Alvaro Peña, Alexis Garrido, Bernardo Arredondo, Vinicius Minatogawa, Claudio Falavigna and Taku Fujiyama
Systems 2025, 13(2), 95; https://doi.org/10.3390/systems13020095 - 1 Feb 2025
Viewed by 1280
Abstract
Crowd management at turnstiles in metro stations is a critical task for ensuring safety, efficiency, and comfort for passengers. A methodology based on observation and microsimulation provides an advanced understanding and optimization of crowd flow through these turnstiles. The aim is to optimize [...] Read more.
Crowd management at turnstiles in metro stations is a critical task for ensuring safety, efficiency, and comfort for passengers. A methodology based on observation and microsimulation provides an advanced understanding and optimization of crowd flow through these turnstiles. The aim is to optimize crowd management and prevent overcrowding and delays at metro turnstiles through innovative solutions. The methodology is based on simulating passenger movements through turnstiles to observe and optimize crowd behavior. The results show that passenger decisions (e.g., choosing which turnstile to use, adjusting pace) are based on perceived crowd density, level of service, and usage of space. For instance, the number of turnstiles, their location, and the layout are important variables to be considered in the decision-making sequence. These decisions can be influenced by parameters like turnstile availability, walking paths, and real-time data (e.g., density of passengers). The methodology can help metro operators decide where to place additional turnstiles or adjust operational schedules. By simulating crowd behavior, operators can make informed decisions to reduce congestion and improve the efficiency of turnstile usage. This methodology could be implemented in various metro systems to optimize operations during different crowd conditions and peak times, ensuring smooth, safe, and efficient passenger flow. Full article
(This article belongs to the Special Issue Optimization-Based Decision-Making Models in Rail Systems Engineering)
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