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Variable Neighborhood Search for Multistage Train Classification at Strategic Planning Level
Author(s) -
Belošević Ivan,
Ivić Miloš
Publication year - 2018
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12304
Subject(s) - marshalling , schedule , computer science , train , sorting , context (archaeology) , set (abstract data type) , integer programming , mathematical optimization , variable neighborhood search , heuristic , variable (mathematics) , operations research , machine learning , artificial intelligence , algorithm , engineering , mathematics , metaheuristic , operating system , paleontology , mathematical analysis , cartography , biology , programming language , geography
Abstract Considerations on influences and interrelations between structural design and functional planning are imperative for raising the cost and operational effectiveness of marshaling yards in the context of managing wagonload services. This article focuses on multistage train classification, which rearranges a set of wagons from inbound trains to create a set of multigroup outbound trains in a proper form. Previous papers on the subject of multistage train classification were limited to considerations on the optimal classification schedule addressing the number of sorting steps as a primary objective. In contrast, this article proposes a model for overall optimization of the classification schedule and sidings layout, simultaneously addressing the number of sorting steps and the total number of wagon movements in the objective function. The model is created on a binary integer programming formulation. A Variable Neighborhood Search (VNS) heuristic is developed due to the computational complexity of the problem. Numerical experiments validate the formulated model and demonstrate the efficiency of the VNS meta‐heuristic approach.