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Comparing an expected value with a multistage stochastic optimization approach for the case of wine grape harvesting operations with quality degradation
International Transactions in Operational Research ( IF 3.1 ) Pub Date : 2021-04-23 , DOI: 10.1111/itor.12982
Elbio L. Avanzini 1 , Alejandro F. Mac Cawley 1 , Jorge R. Vera 1 , Sergio Maturana 1
Affiliation  

Operations planning is an important step in any activity as it aligns resources to achieve economic production value. In agriculture operations where uncertainty is present, planners must deal with biological and environmental factors, among others, which add variability and complexity to the production planning process. In this work, we consider operations planning to harvest grapes for wine production where uncertainty in weather conditions will affect the quality of grapes and, consequently, the economic value of the product. In this setting, planners make decisions on labor allocation and harvesting schedules, considering uncertainty of future rain. Weather uncertainty is modeled following a Markov Chain approach, in which rain affects the quality of grapes and labor productivity. We compare an expected value with a multi-stage stochastic optimization approach using standard metrics such as Value of Stochastic Solution and Expected Value of Perfect Information. We analyze the impact of grape quality over time, if they are not harvested on the optimal ripeness day, and also consider differences in ability between workers, which accounts for the impact of rain in their productivity. Results are presented for a small grape harvest instance and we compare the performance of both models under different scenarios of uncertainty, manpower ability, and product qualities. Results indicate that the multi-stage approach produces better results than the expected value approach, especially under high uncertainty and high grape quality scenarios. Worker ability is also a mechanism for dealing with uncertainty, and both models take advantage of this variable.

中文翻译:

将预期值与多阶段随机优化方法进行质量退化的酿酒葡萄收获作业的比较

运营计划是任何活动中的重要一步,因为它调整资源以实现经济生产价值。在存在不确定性的农业经营中,计划者必须处理生物和环境因素等,这些因素增加了生产计划过程的可变性和复杂性。在这项工作中,我们考虑了为葡萄酒生产收获葡萄的运营计划,其中天气条件的不确定性会影响葡萄的质量,从而影响产品的经济价值。在这种情况下,规划人员在考虑未来降雨的不确定性的情况下就劳动力分配和收获时间表做出决定。天气不确定性是按照马尔可夫链方法建模的,其中降雨会影响葡萄的质量和劳动生产率。我们使用标准指标(例如随机解的价值和完美信息的期望值)将期望值与多阶段随机优化方法进行比较。我们分析了葡萄质量随时间的影响,如果它们没有在最佳成熟日收获,并且还考虑了工人之间的能力差异,这说明了雨水对他们生产力的影响。给出了一个小葡萄收获实例的结果,我们比较了两种模型在不确定性、人力能力和产品质量的不同情况下的性能。结果表明,多阶段方法比预期值方法产生更好的结果,尤其是在高不确定性和高葡萄质量的情况下。工人能力也是一种处理不确定性的机制,
更新日期:2021-04-23
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