Optimisation problems usually take the form of having a single or multiple objectives with a set of constraints. The model itself concerns a single problem for which the best possible solution is sought. Problems are usually static in the sense that they do not consider changes over time in a cumulative manner. Dynamic optimisation problems to incorporate changes. However, these are memoryless in that the problem description changes and a new problem is solved – but with little reference to any previous information. In this paper, a temporally augmented version of a water management problem which allows farmers to plan over long time horizons is introduced. A climate change projection model is used to predict both rainfall and temperature for the Murrumbidgee Irrigation Area in Australia for up to 50 years into the future. Three representative decades are extracted from the climate change model to create the temporal data sets. The results confirm the utility of the temporal approach and show, for the case study area, that crops that can feasibly and sustainably be grown will be a lot fewer than the present day in the challenging water-reduced conditions of the future.

优化问题通常采用具有一组约束的单个或多个目标的形式。该模型本身涉及单个问题，因此寻求最佳解决方案。问题通常是静态的，因为它们不会以累积的方式考虑随时间的变化。包含更改的动态优化问题。但是，这些方法无记忆，因为问题描述会发生变化，并且可以解决新问题-但很少参考任何以前的信息。在本文中，介绍了水资源管理问题的暂时扩展版本，该版本允许农民进行长期规划。气候变化预测模型可用于预测澳大利亚Murrumbidgee灌溉区未来50年的降雨量和温度。从气候变化模型中提取了三个有代表性的十年，以创建时间数据集。结果证实了时间方法的实用性，并表明，在案例研究区域中，在未来面临严峻的节水条件下，能够可持续，可持续地种植的农作物将比今天少得多。