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Multi-objective models and techniques for analysing the absolute capacity of railway networks

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  • Burdett, Robert L.

Abstract

Railway capacity determination and expansion are very important topics. In prior research, the competition between different entities such as train services and train types, on different network corridors however have been ignored, poorly modelled, or else assumed to be static. In response, a comprehensive set of multi-objective models have been formulated in this article to perform a trade-off analysis. These models determine the total absolute capacity of railway networks as the most equitable solution according to a clearly defined set of competing objectives. The models also perform a sensitivity analysis of capacity with respect to those competing objectives. The models have been extensively tested on a case study and their significant worth is shown. The models were solved using a variety of techniques however an adaptive E constraint method was shown to be most superior. In order to identify only the best solution, a Simulated Annealing meta-heuristic was implemented and tested. However a linearization technique based upon separable programming was also developed and shown to be superior in terms of solution quality but far less in terms of computational time.

Suggested Citation

  • Burdett, Robert L., 2015. "Multi-objective models and techniques for analysing the absolute capacity of railway networks," European Journal of Operational Research, Elsevier, vol. 245(2), pages 489-505.
  • Handle: RePEc:eee:ejores:v:245:y:2015:i:2:p:489-505
    DOI: 10.1016/j.ejor.2015.03.020
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    Cited by:

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    2. Line Blander Reinhardt & David Pisinger & Richard Lusby, 2018. "Railway capacity and expansion analysis using time discretized paths," Flexible Services and Manufacturing Journal, Springer, vol. 30(4), pages 712-739, December.
    3. Li, Feng & Gao, Ziyou & Wang, David Z.W. & Liu, Ronghui & Tang, Tao & Wu, Jianjun & Yang, Lixing, 2017. "A subjective capacity evaluation model for single-track railway system with δ-balanced traffic and λ-tolerance level," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 43-66.
    4. Jovanović, Predrag & Pavlović, Norbert & Belošević, Ivan & Milinković, Sanjin, 2020. "Graph coloring-based approach for railway station design analysis and capacity determination," European Journal of Operational Research, Elsevier, vol. 287(1), pages 348-360.
    5. Burdett, Robert & Kozan, Erhan, 2016. "A multi-criteria approach for hospital capacity analysis," European Journal of Operational Research, Elsevier, vol. 255(2), pages 505-521.
    6. Bevrani, Bayan & Burdett, Robert L. & Bhaskar, Ashish & Yarlagadda, Prasad K.D.V., 2017. "A capacity assessment approach for multi-modal transportation systems," European Journal of Operational Research, Elsevier, vol. 263(3), pages 864-878.
    7. Yu, Shiwei & Zheng, Shuhong & Gao, Shiwei & Yang, Juan, 2017. "A multi-objective decision model for investment in energy savings and emission reductions in coal mining," European Journal of Operational Research, Elsevier, vol. 260(1), pages 335-347.
    8. Burdett, RL, 2016. "Optimisation models for expanding a railway's theoretical capacity," European Journal of Operational Research, Elsevier, vol. 251(3), pages 783-797.
    9. Yan, Fei & Bešinović, Nikola & Goverde, Rob M.P., 2019. "Multi-objective periodic railway timetabling on dense heterogeneous railway corridors," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 52-75.
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