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Rule based optimization for a bulk handling port operations

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Abstract

In this paper a study on the operations of a bulk material port is carried out to develop a decision support model, which deals with the dynamics of port and aids in better decision making at different scenarios. Here, we describe various decisions taken by port authorities pertaining to the import of coal at the terminal. We optimize these decisions with the aim of minimizing the unloading time of ships at the port, congestion in the stockyard, and loading time of the rakes. A practice-oriented decision support model is proposed to assist port planners in making these decisions. Various operational rules are embedded within the model, for carrying out the modelling of various port operations. The utility of the developed model is demonstrated using a case study which helps in achieving efficient utilization of berths, stockyard and rake loading stations.

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References

  • Asperen, E. V., Dekker, R., & Polman, M. (2002). Allocation of ships in a port simulation. In Alexander Verbraeck, VH (Ed.) Proceedings 15th European simulation symposium, SCS European Council/SCS Europe BVBA, ISBN 3-936150-28-1 (book)/3-936150-29-X (CD) (2003).

  • Abdekhodaee, A., Dunstall, S., Ernst, A. T., & Lam, L. (2004). Integration of stockyard and rail network: A scheduling case study. In Proceedings of the fifth Asia Pacific industrial engineering and management systems conference.

  • Boland, N., Gulczynski, D., & Savelsbergh, M. (2012). A stockyard planning problem. European Journal on Transportation and Logistics, 1(3), 197–236.

    Article  Google Scholar 

  • Babu, S. A. K. I., Pratap, S., Lahoti, G., Fernandes, K. J., Tiwari, M. K., Mount, & Mand Xiong, Y. (2014). Minimizing delay of ships in bulk terminals by simultaneous ship scheduling, stockyard planning and train scheduling. Maritimes Economics and Logistic. doi:10.1057/mel.2014.20.

  • Gökdağ, H., & Yildiz, A. R. (2012). Structural damage detection using modal parameters and particle swarm optimization. Materials Testing, 54(6), 416–420.

    Article  Google Scholar 

  • Hani, Y., Amodeo, L., Yalaoui, F., & Chen, H. (2008). Simulation based optimization of a train maintenance facility. Journal of Intelligent Manufacturing, 19, 293–300.

    Article  Google Scholar 

  • Hosseini, S., & Al Khaled, A. (2014). A survey on the imperialist competitive algorithm metaheuristic: Implementation in engineering domain and directions for future research. Applied Soft Computing, 24, 1078–1094.

  • Imai, A., Zhang, J. T., Nishimura, E., & Papadimitriou, S. (2007). The berth allocation problem with service time and delay time objectives. Maritime Economics & Logistics, 9, 269–290.

    Article  Google Scholar 

  • Liang, C., Hwang, H., & Gen, M. (2012). A berth allocation planning problem with direct transhipment consideration. Journal of Intelligent Manufacturing, 23, 2207–2214.

    Article  Google Scholar 

  • Liang, C., Guo, J., & Yang, Y. (2011). Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning. Journal of Intelligent Manufacturing, 22, 471–479.

    Article  Google Scholar 

  • Liang, C., Li, M. M., Lu, B., Gu, T., Jo, J., & Ding, Y. (2015). Dynamic configuration of QC allocating problem based on multi-objective genetic algorithm. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1035-7.

  • Lee, Y., & Chen, C. (2009). An optimization heuristic for the berth scheduling problem. European Journal of Operational Research, 196(2), 500–508.

    Article  Google Scholar 

  • Park, J. H., Kim, H. J., & Lee, C. (2009). Ubiquitous software controller to prevent deadlocks for automated guided vehicle systems in a container port terminal environment. Journal of Intelligent Manufacturing, 20, 321–325.

    Article  Google Scholar 

  • Robenek, T., Umang, N., & Bierlaire, M., (2012). A branch-and-price algorithm to solve the integrated berth allocation and yard assignment problem in bulk ports. Report TRANSP-OR 120617, Transport and Mobility Laboratory, École Polytechnique Fédérale de Lausanne.

  • Singh, G., David, S., Ernst, A. T., Gavriliouk, O., Oyston, R., Giles, T., et al. (2012). A mixed integer programming model for long term capacity expansion planning: A case study from The Hunter Valley Coal Chain. European Journal of Operational Research, 220(1), 210–224.

    Article  Google Scholar 

  • Won, S. H., Zhang, X., & Kim, K. H. (2012). Workload-based yard-planning system in container terminals. Journal of Intelligent Manufacturing, 23, 2193–2206.

    Article  Google Scholar 

  • Wang, Y., & Kim, K. H. (2011). A quay crane scheduling algorithm considering the workload of yard cranes in a container yard. Journal of Intelligent Manufacturing, 22, 459–470.

    Article  Google Scholar 

  • Yildiz, A. R., & Saitou, K. (2011). Topology synthesis of multicomponent structural assemblies in continuum domains. Journal of Mechanical Design, 133(1), 011008.

    Article  Google Scholar 

  • Yildiz, A. R. (2012a). A comparative study of population-based optimization algorithms for turning operations. Information Sciences, 210, 81–88.

    Article  Google Scholar 

  • Yildiz, A. R. (2012b). A new hybrid particle swarm optimization approach for structural design optimization in the automotive industry. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 226(10), 1340–1351.

    Google Scholar 

  • Yildiz, A. R., & Solanki, K. N. (2012). Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach. The International Journal of Advanced Manufacturing Technology, 59(1–4), 367–376.

    Article  Google Scholar 

  • Yildiz, A. R. (2013a). Comparison of evolutionary-based optimization algorithms for structural design optimization. Engineering Applications of Artificial Intelligence, 26(1), 327–333.

    Article  Google Scholar 

  • Yildiz, A. R. (2013b). Optimization of multi-pass turning operations using hybrid teaching learning-based approach. The International Journal of Advanced Manufacturing Technology, 66(9–12), 1319–1326.

    Article  Google Scholar 

  • Zhen, L. (2014). Container yard template planning under uncertain maritime market. Transportation Research Part E, 69, 199–217.

    Article  Google Scholar 

  • Zhen, L., Chew, E. P., & Lee, L. H. (2011). An integrated model for berth template and yard template planning in transshipment hubs. Transportation Science, 45(4), 483–504.

    Article  Google Scholar 

  • Zhen, L., Lee, L. H., & Chew, E. P. (2012). A decision model for berth allocation under uncertainty. European Journal of Operational Research, 212(1), 54–68.

    Article  Google Scholar 

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Correspondence to M. K. Tiwari.

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Pratap, S., Daultani, Y., Tiwari, M.K. et al. Rule based optimization for a bulk handling port operations. J Intell Manuf 29, 287–311 (2018). https://doi.org/10.1007/s10845-015-1108-7

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  • DOI: https://doi.org/10.1007/s10845-015-1108-7

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