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https://gestaoeproducao.com/article/doi/10.1590/0104-530x-2557-19
Gestão & Produção
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Modelo matemático para planejamento da distribuição de locomotivas para atendimento à demanda de formação de trens

Mathematical model for planning the distribution of locomotives to meet the demand for making up trains

Fabiano Cézar Gomes Nascimento; Rodrigo de Alvarenga Rosa

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Resumo

Resumo O custo operacional para distribuir locomotivas ao longo dos pátios ferroviários com o objetivo de atender às demandas de formação de trens é muito alto. assim, este artigo propõe um modelo matemático para planejamento da distribuição de locomotivas que visa atender às requisições dos pátios, Locomotive Scheduling Problem (LSP), com o intuito de minimizar os custos de distribuição. o modelo proposto apresenta uma nova formulação para o LSP With Multiple Locomotives e considera o desbalanceamento entre oferta e demanda de locomotivas para atender às requisições de um pátio, o que ainda não havia sido tratado na literatura. testes em instâncias com base em dados reais da estrada de ferro vitória a minas (EFVM) foram resolvidos de forma ótima utilizando o solver CPLEX 12.6. o modelo se mostrou bastante aderente ao planejamento da distribuição, e diversos parâmetros que afetam os custos da distribuição foram analisados. os resultados mostraram ganhos em relação ao planejamento manual atualmente realizado.

Palavras-chave

Planejamento da distribuição de locomotivas, Locomotive scheduling problem, Locomotive assignment problem, Transporte ferroviário

Abstract

Abstract The cost for locomotive distribution over the rail yards to meet the locomotive demand for train formation is very high. Thus, this paper proposes a mathematical model based on the Locomotive Scheduling Problem for locomotive distribution planning to meet the demand of the rail yards seeking to minimize the distribution costs. The proposed model presents a new formulation for the LSP with Multiple Locomotives and considers the imbalance between offer and demand of locomotives, this situation was not addressed in the literature yet. Tests on instances based on real data from the Vitória a Minas Railroad (EFVM) were solved optimally using CPLEX 12.6. The model proved to be a good tool to analyze the locomotive distribution planning. When compared with the manual planning currently held by the railroad, the results showed several gains.

Keywords

Locomotive distribution planning, Locomotive scheduling problem, Locomotive assignment problem, Railroad transport

Referências

Acompanhamento das concessões ferroviárias: relatório anual. 2013.

Evolução do transporte ferroviário de cargas. 2015.

Ahuja R. K., Liu J., Orlin J. B., Sharma D., Shughart L. A. Solving real-life locomotive-scheduling problems. Transportation Science. 2005;39(4):503-17.

Ahuja R. K., Shughart L. A., Liu J. An optimization-based approach for locomotive planning. 2006.

Bacelar A., Garcia A. S. An optimization approach to the locomotive scheduling problem in brazilian railways.. 2006:493-500.

Bouzaiene-Ayari B., Cheng C., Das S., Fiorillo R., Powell W. B. From single commodity to multiattribute models for locomotive optimization: a comparison of optimal integer programming and approximate dynamic programming. Transportation Science. 2016;50(2):366-89.

Transporte e economia: o sistema ferroviário brasileiro. 2013.

Cordeau J. F., Toth P., Vigo D. A survey of optimization models for train routing and scheduling. Transportation Science. 1998;32(4):380-404.

Florian M., Bushell G., Ferland J., Guerin G., Nastansky L. The engine scheduling problem in a railway network. Infor. 1976;14(2):121-38.

Gohring K. W., Mcbrayer R. N., Mcgaughey R. S. Planning locomotive and caboose distribution. Rail International. 1973;3:151-8.

Holt J. N. Locomotive scheduling by computer bashpeak. Rail International. 1973;4:1053-8.

Maposa D., Swene S. D. Locomotive scheduling in freight transport at Mpopoma Train Station in Bulawayo for the Southern Region, Zimbabwe. Internacional Journal of Economics and Management Sciences. 2012;1(12):104-16.

Noble D. H., Al‐Amin M., Mills R. G. J. Production of locomotive rosters for a multi‐class multi‐locomotive problem. The Journal of the Operational Research Society. 2001;52(11):1191-200.

Noori S., Ghannadpour S. F. Locomotive assignment problem with train precedence using genetic algorithm. Journal of Industrial Engineering International. 2012;8(1):1-13.

Piu F. A mixed integer programming approach to the locomotive assignment problem.. 2011:1-42.

Piu F., Speranza M. G. The locomotive assignment problem: a survey on optimization models. International Transactions in Operational Research. 2014;21(3):327-52.

Scholz V. Knowledge‐based locomotive planning for the Swedish Railway. 2000.

Vaidyanathan B., Ahuja R. K., Orlin J. B. The locomotive routing problem. Transportation Science. 2008;42(4):492-507.

Vaidyanathan B., Ahuja R. K., Liu J., Shughart L. A. Real-life locomotive planning: new formulations and computational results. Transportation Research Part B: Methodological. 2008;42(2):147-68.

Ziarati K., Chizari H., Nezhad A. M. Locomotive optimization using artificial intelligence approach. Indian Journal of Science and Technology. 2005;29:93-105.

Ziarati K., Soumis F., Desrosiers J., Solomon M. M. A branch-first, cut-second approach for locomotive assignment. Management Science. 1999;45(8):1156-68.

Ziarati K., Soumis F., Desrosiers J., Gélinas S., Saintonge A. Locomotive assignment with heterogeneous consists at CN North America. European Journal of Operational Research. 1997;97(2):281-92.

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