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A Scheduling-Based Approach to Railway Capacity Estimation for Industrial Freight Junctions

Authors Luca Di Gaspero , Alessia Grosso , Giovanni Longo

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Subject Classification

ACM Subject Classification
  • Applied computing → Transportation
  • Theory of computation → Constraint and logic programming
  • Computing methodologies → Temporal reasoning
Keywords
  • Freight Train Scheduling
  • Infrastructure Capacity Assessment
  • Optional Activities
  • OptalCP

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Abstract

This work proposes a Constraint Programming (CP)-based methodology for estimating the maximum freight capacity of railway junctions serving industrial areas with multiple terminals, where minimum per-destination service frequencies must be guaranteed as hard constraints to preserve commercial competitiveness among terminal operators. The problem is formulated as a scheduling problem with optional interval variables: the solver decides which candidate services to execute and when, maximising completed round-trips within a 24-hour horizon while satisfying track-occupancy exclusivity, turnout conflicts, buffer-capacity limits, a mandatory maintenance window, and a fixed passenger timetable. The approach is implemented in OptalCP and evaluated on five scenarios covering the current and a planned upgraded infrastructure of a maritime port in the northern Adriatic Sea. Satisfiability queries are answered in under 13 seconds; maximisation queries establish that the as-is network sustains at least 35 freight services per day against the 11 currently operated, rising to at least 38 after the planned upgrade, with both authority service targets confirmed feasible on their respective infrastructures.

Cite As Get BibTex

Luca Di Gaspero, Alessia Grosso, and Giovanni Longo. A Scheduling-Based Approach to Railway Capacity Estimation for Industrial Freight Junctions. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 19:1-19:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.19

Author Details

Luca Di Gaspero
  • DPIA – Università degli Studi di Udine, Italy
Alessia Grosso
  • DEAMS – Università degli Studi di Trieste, Italy
Giovanni Longo
  • DEAMS – Università degli Studi di Trieste, Italy

Supplementary Materials

References

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