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Optimal crop planning using a chance constrained linear programming model

机译：使用机会约束线性规划模型的最佳作物计划

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摘要

A monthly time-stepped chance constrained linear programming (CCLP) model was developed to derive opti-nmal cropping patterns and optimal operational strategies for the Sri Ram Sagar Project, with stochastic inflows.nThe stochastic nature of the inflows was incorporated into the model in its equivalent deterministic form.nThese equivalent deterministic inflow values were estimated from the annual and monthly probability distributionnof observed inflows, and named the chance constrained linear programming model-annual and chance constrainednlinear programming model-monthly, respectively. The models were solved for nine different dependable inflownlevels, namely for 50, 55, 60, 65, 70, 75, 80, 85 and 90% in each CCLP. The results of the models were comparednwith respect to net benefit, irrigation intensity, total cropped area, optimal cropping pattern, optimal releases, evap-noration loss and initial storages. Based on the results obtained, it can be concluded that, in CCLP optimization, thenprobability distribution of ‘model time period’ (t-month in this case) should be considered rather than the prob-nability of ‘planning time period’ (year in this case).
机译：开发了月度时变机会受限线性规划（CCLP）模型，以得出随机流入的Sri Ram Sagar项目的最佳种植模式和最佳运营策略.n流入的随机性已纳入其模型中这些等价确定性流入值是根据观测到的流入量的年和月概率分布n估算的，分别命名为机会约束线性规划模型-年度和机会约束线性规划模型-每月。求解了9种不同的可靠流入水平的模型，即每个CCLP中的50％，55％，60％，65％，70％，75％，80％，85％和90％。比较了模型的结果，包括净收益，灌溉强度，总种植面积，最佳种植方式，最佳释放量，蒸发损失和初始储存量。根据获得的结果，可以得出结论，在CCLP优化中，应考虑“模型时间段”（在这种情况下为t月）的概率分布，而不是“计划时间段”（以年为单位）的概率分布。这个案例）。

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