Scheduling of road vehicles in sugarcane transport: A case study at an Australian sugar mill

Pressure to remain internationally competitive has forced Australian sugar mills to reduce capital and operational costs. Improved scheduling of road transport vehicles provides one such opportunity, as it would reduce vehicle queue and mill idle times and hence the number of vehicles needed. It is difficult for mill traffic officers to produce good transport schedules manually due to the need to service a large number of harvesters in different locations. To address this issue, research was undertaken participatively with a sugar milling company in Australia to produce and implement a mixed integer programming model that represents the road transport operations. Two meta-heuristics were applied to find a solution to the model, leading to potential cost savings of AU$240,000 per year versus schedules produced manually by the mill traffic officer. The model was also applied to explore regional planning options for a more integrated harvesting and transport system.

[1]  M. Robertson,et al.  The FARMSCAPE approach to decision support: farmers', advisers', researchers' monitoring, simulation, communication and performance evaluation , 2002 .

[2]  John Sherington,et al.  Participatory research methods—Implementation, effectiveness and institutional context , 1997 .

[3]  Mohamadreza Banihashemi,et al.  HEURISTIC APPROACHES FOR SOLVING LARGE-SCALE BUS TRANSIT VEHICLE SCHEDULING PROBLEM WITH ROUTE TIME CONSTRAINTS , 2002 .

[4]  Pierre Hansen,et al.  Variable Neighbourhood Search , 2003 .

[5]  Barrie M. Baker,et al.  A genetic algorithm for the vehicle routing problem , 2003, Comput. Oper. Res..

[6]  I. Grossmann,et al.  MILP model for scheduling and design of a special class of multipurpose batch plants , 1996 .

[7]  Pedro M. Mateo,et al.  Optimization with simulation and multiobjective analysis in industrial decision-making: A case study , 2002, Eur. J. Oper. Res..

[8]  Hans-Otto Günther,et al.  Supply optimization for the production of raw sugar , 2007 .

[9]  Andreas Löbel,et al.  Solving Large-Scale Multiple-Depot Vehicle Scheduling Problems , 1999 .

[10]  Éric D. Taillard,et al.  Solving real-life vehicle routing problems efficiently using tabu search , 1993, Ann. Oper. Res..

[11]  Mohamadreza Banihashemi,et al.  A comparative analysis of bus transit vehicle scheduling models , 2003 .

[12]  Gilbert Laporte,et al.  The vehicle routing problem: An overview of exact and approximate algorithms , 1992 .

[13]  P. G. Everitt,et al.  Towards an integrated cane transport scheduling system , 1997 .

[14]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[15]  Michael Forbes,et al.  An exact algorithm for multiple depot bus scheduling , 1994 .

[16]  Fred W. Glover,et al.  A user's guide to tabu search , 1993, Ann. Oper. Res..

[17]  D. J. Abel,et al.  A Routing and Scheduling Problem for a Rail System: A Case Study , 1981 .

[18]  Iiro Harjunkoski,et al.  Solving a large-scale industrial scheduling problem using MILP combined with a heuristic procedure , 2002, Eur. J. Oper. Res..

[19]  David Kendrick,et al.  GAMS, a user's guide , 1988, SGNM.