JTTs  Vol.6 No.2 , February 2016
A New Multi-Objective Model to Optimise Rail Transport Scheduler
Abstract: The sugarcane transport system plays a critical role in the overall performance of Australia’s sugarcane industry. An inefficient sugarcane transport system interrupts the raw sugarcane harvesting process, delays the delivery of sugarcane to the mill, deteriorates the sugar quality, increases the usage of empty bins, and leads to the additional sugarcane production costs. Due to these negative effects, there is an urgent need for an efficient sugarcane transport schedule that should be developed by the rail schedulers. In this study, a multi-objective model using mixed integer programming (MIP) is developed to produce an industry-oriented scheduling optimiser for sugarcane rail transport system. The exact MIP solver (IBM ILOG-CPLEX) is applied to minimise the makespan and the total operating time as multi-objective functions. Moreover, the so-called Siding neighbourhood search (SNS) algorithm is developed and integrated with Sidings Satisfaction Priorities (SSP) and Rail Conflict Elimination (RCE) algorithms to solve the problem in a more efficient way. In implementation, the sugarcane transport system of Kalamia Sugar Mill that is a coastal locality about 1050 km northwest of Brisbane city is investigated as a real case study. Computational experiments indicate that high-quality solutions are obtainable in industry-scale applications.
Cite this paper: Masoud, M. , Kent, G. , Kozan, E. and Liu, S. (2016) A New Multi-Objective Model to Optimise Rail Transport Scheduler. Journal of Transportation Technologies, 6, 86-98. doi: 10.4236/jtts.2016.62008.

[1]   Australian Sugar Milling Council. (2014).

[2]   Abel, D.J., Stark, K.P., Murry, C.R. and Demoulin, Y.M. (1981) A Routing and Scheduling Problem for a Rail System: A Case Study. Journal of the Operational Research Society, 32, 767-774.

[3]   Pinkney, A.J. andEveritt, P.G. (1997) Towards an Integrated Sugarcane Transport Scheduling System. Proceedings of the Australian Society of Sugar Cane Technologists, 19, 420-425.

[4]   Pinkney, A.J. and Kent, G.A. (2014) Real Time Harvest and Transport System. Syndicated Report No. 6/14, Sugar Research Institute, Australia.

[5]   Masoud, M., Kozan, E. and Kent, G.A. (2010) Scheduling Techniques to Optimise Sugarcane Rail Systems. ASOR Bulletin, 29, 25-34.

[6]   Masoud, M., Kozan, E. and Kent, G.A. (2010) A Constraint Programming Approach to Optimise Sugarcane Rail Operations. Proceedings of the 11th Asia Pacific Industrial Engineering and Management Systems Conference, Malaysia, 7-10 December 2010, Vol. 147, 1-7.

[7]   Masoud, M., Kozan, E. and Kent, G.A. (2010) A Comprehensive Approach for Scheduling Single Track Railways. The Annual Conference on Statistics, Computer Sciences and Operations Research, Egypt, Cairo, 45, 19-30.

[8]   Masoud, M. (2012) Scheduling Techniques to Optimise Rail Operations. PhD Thesis, Queensland University of Technology, Brisbane.

[9]   Masoud, M., Kozan, E. and Kent, G.A. (2011) A Job-Shop Scheduling Approach for Optimising Sugarcane Rail Operations. Flexible Services and Manufacturing Journal, 23, 181-196.

[10]   Hu, H., Li, K. and Xu, X. (2013) A Multi-Objective Train-Scheduling Optimization Model Considering Locomotive Assignment and Segment Emission Constraints for Energy Saving. Journal of Modern Transportation, 21, 9-16.

[11]   Li, X., Wang, D., Li, K. and Gao, Z. (2013) A Green Train Scheduling Model and Fuzzy Multi-Objective Optimization Algorithm. Applied Mathematical Modelling, 37, 2063-2073.

[12]   Li, X., Chien, C.F., Yang, L.X. and Gao, Z. (2014) The Train Fuelling Cost Minimization Problem with Fuzzy Fuel Prices. Flexible Service and Manufacturing Journal, 26, 249-267.

[13]   Masoud, M., Kozan, E. and Kent, G.A. (2015) Hybrid Metaheuristic Techniques for Optimising Sugarcane Rail Operations. International Journal of Production Research, 53, 2569-2589.

[14]   Burdett, R.L. (2015) Multi Objective Models and Techniques for Analysing the Absolute Capacity of Railway Networks. European Journal of Operational Research, 245, 489-505.

[15]   Kozan, K. and Liu, S.Q. (2012) A Demand-Responsive Decision Support System for Coal Transportation. Decision Support Systems, 54, 665-680.

[16]   Liu, S.Q. and Kozan, E. (2009) Scheduling Trains as a Blocking Parallel-Machine Job Shop Scheduling Problem. Computers and Operations Research, 36, 2840-2852.

[17]   Liu, S.Q. and Kozan, E. (2011) Optimising a Coal Rail Network under Capacity Constraints. Flexible Services and Manufacturing Journal, 23, 90-110.

[18]   Liu, S.Q. and Kozan, E. (2011) Scheduling Trains with Priorities: A No-Wait Blocking Parallel-Machine Job-Shop Scheduling Model. Transportation Science, 45, 175-198.

[19]   Kent, G.A. and Kozan, E. (2012) Reducing Transport Costs through the Automation of Schedule Generation. Syndicated Report 1/12, Sugar Research Institute, Australia.

[20]   Kent, G.A. and Pinkney, A.J. (2015) Real Time Harvest and Transport System. Report for Milestone 3, SRA Project Code 2014/037, Sugar Research Australia.

[21]   Liu, D., Lechner, B. and Freudenstein, S. (2016) Evaluation of High-Speed Track Quality Using Dynamic Simulation of Vehicle-Track Interaction. Journal of Transportation Technologies, 6, 9-14.

[22]   Zhai, W., Wang, K. and Cai, C. (2009) Fundamentals of Vehicle-Track Coupled Dynamics. Vehicle System Dynamics, 47, 1349-1376.

[23]   Grunow, M. and Gu, H. (2007) Supply Optimization for the Production of Raw Sugar. International Journal of Production Economics, 110, 224-239.