Optimizing cane supply decisions within a sugar mill region.

*(English)*Zbl 0956.90017Summary: A sugar mill region contains large differences in sugar yield due to harvest date, harvest age of crop, geographical location and crop class. While it is desirable to harvest all cane when the likely sugar yields are at the reason’s peak, this is not possible due to limited capacities of harvesting, mill crushing and transport. The harvesting of cane is therefore carried out over several months.

This paper focuses on the development of a large-scale integer programming model to optimize the decisions of harvest date, crop cycle length, and whether to fallow for all paddocks within a mill region. Net revenue for the mull region is maximized over a planning horizon of several years. The model, which is an extension of the generalized assignment problem with over 500.000 integer variables, is subject to the constraints of total assigned land, equity, mill crushing and geographical transport capacity. This large model is solved heuristically using a new form of local search that incorporates some of the ideas of tabu search (but there is no tabu list). It applies oscillation through feasible versus infeasible space, that is controlled by the success of the search throughout the solution process.

Application to five large Australian mill regions through strong industry participation resulted in up to a 7 per cent increase in net revenue.

This paper focuses on the development of a large-scale integer programming model to optimize the decisions of harvest date, crop cycle length, and whether to fallow for all paddocks within a mill region. Net revenue for the mull region is maximized over a planning horizon of several years. The model, which is an extension of the generalized assignment problem with over 500.000 integer variables, is subject to the constraints of total assigned land, equity, mill crushing and geographical transport capacity. This large model is solved heuristically using a new form of local search that incorporates some of the ideas of tabu search (but there is no tabu list). It applies oscillation through feasible versus infeasible space, that is controlled by the success of the search throughout the solution process.

Application to five large Australian mill regions through strong industry participation resulted in up to a 7 per cent increase in net revenue.

##### MSC:

90B90 | Case-oriented studies in operations research |

90C10 | Integer programming |

90B35 | Deterministic scheduling theory in operations research |

90C59 | Approximation methods and heuristics in mathematical programming |

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##### References:

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