Improving harvesting and transport planning within a sugar value chain

Recent economic pressures have forced the Australian sugar industry to achieve better integration and optimization of the cane harvesting and transport sectors of the value chain. The logistical and economic complexity of the harvesting and transport system was captured through the development of a modelling framework that effectively links several component models that describe the parts of the system. Through engaging in participatory research with representatives of a sugar region located at the north-east coast of Australia, we use this modelling framework to address some key industry issues in rationalizing rail track infrastructure and re-organizing harvesting. These issues were addressed by building component models for the modelling framework in the field of location science, namely the capacitated P-Median problem and spatial clustering. By applying the modelling framework and its component models to the case-study region, we explored a range of scenarios with a net cost reduction of up to AU$2 260 000 per year.

[1]  James W. Jones,et al.  Systems approaches for the design of sustainable agro-ecosystems , 2001 .

[2]  Gerhard W. Dueck,et al.  Threshold accepting: a general purpose optimization algorithm appearing superior to simulated anneal , 1990 .

[3]  W. Jordan.  A New Paradigm , 1991, Restoration & Management Notes.

[4]  D. Pannell,et al.  Social and economic challenges in the development of complex farming systems , 1999, Agroforestry Systems.

[5]  R. Ruben,et al.  Integrating agricultural research and policy analysis: analytical framework and policy applications for bio-economic modelling , 1998 .

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

[7]  Andrew J. Higgins.  Australian Sugar Mills Optimize Harvester Rosters to Improve Production , 2002, Interfaces.

[8]  Eligius M. T. Hendrix,et al.  On optimisation of agri chains by dynamic programming , 2002, Eur. J. Oper. Res..

[9]  Mauricio G. C. Resende,et al.  A Greedy Randomized Adaptive Search Procedure for Maximum Independent Set , 1994, Oper. Res..

[10]  Paul van Beek,et al.  An application of mixed-integer linear programming models on the redesign of the supply network of Nutricia Dairy & Drinks Group in Hungary , 2002, OR Spectr..

[11]  Andrew Higgins,et al.  Assessing the potential benefits of alternative cane supply arrangements in the Australian sugar industry , 2003 .

[12]  Belén Melián-Batista,et al.  The Parallel Variable Neighborhood Search for the p-Median Problem , 2002, J. Heuristics.

[13]  Richard L. Church,et al.  Geographical information systems and location science , 2002, Comput. Oper. Res..

[14]  Ian McGill,et al.  Action learning : a guide for professional, management, and educational development , 1995 .

[15]  D. M. Hogarth,et al.  INCREASED SUGAR INDUSTRY PROFITABILITY THROUGH HARVESTING BEST PRACTICE , 2002 .

[16]  Nicos Christofides,et al.  Capacitated clustering problems by hybrid simulated annealing and tabu search , 1994 .

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

[18]  D. M. Hogarth,et al.  HARVEST HAUL MODEL—THE COST OF HARVESTING PADDOCKS OF SUGARCANE ACROSS A SUGAR MILLING REGION , 2004 .

[19]  T. Bernet,et al.  Tailoring agricultural extension to different production contexts: a user-friendly farm-household model to improve decision-making for participatory research , 2001 .

[20]  Per J. Agrell,et al.  Interactive multiobjective agro-ecological land use planning: The Bungoma region in Kenya , 2004, Eur. J. Oper. Res..

[21]  J. Current,et al.  An efficient tabu search procedure for the p-Median Problem , 1997 .

[22]  Andrew Higgins,et al.  A simulation model for capacity planning in sugarcane transport , 2005 .

[23]  Steve Russ,et al.  A new paradigm for computer-based decision support , 2002, Decis. Support Syst..

[24]  Christina H. Gladwin,et al.  The Quality of Science in Participatory Research: A Case Study from Eastern Zambia , 2002 .

[25]  Julian Park,et al.  Learning selection: an evolutionary model for understanding, implementing and evaluating participatory technology development , 2002 .

[26]  Roberto Baldacci,et al.  A new method for solving capacitated location problems based on a set partitioning approach , 2002, Comput. Oper. Res..

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