论文标题
使用多目标帕累托优化在规避风险的供应链系统中进行不确定性建模
Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization
论文作者
论文摘要
供应链建模中艰巨的任务之一是针对不规则变化构建强大的模型。在时间序列分析和机器学习模型的扩散过程中，提出了几种修改，例如加速了经典的Levenberg-Marquardt算法，重量衰减和归一化，这引入了解决此问题的算法优化方法。在本文中，我们引入了一种新颖的方法，即帕累托优化，以通过在某些APRIORII假设下对它们进行显式建模来处理不确定性并限制此类不确定性的熵。我们已经使用遗传方法实施了帕累托优化，并将结果与经典的遗传算法和混合构成线性编程（MILP）模型进行了比较。我们的结果产生了经验证据，表明帕累托优化可以避开这种非决错误，并且是产生强大和反应性供应链模型的正式方法。
One of the arduous tasks in supply chain modelling is to build robust models against irregular variations. During the proliferation of time-series analyses and machine learning models, several modifications were proposed such as acceleration of the classical levenberg-marquardt algorithm, weight decaying and normalization, which introduced an algorithmic optimization approach to this problem. In this paper, we have introduced a novel methodology namely, Pareto Optimization to handle uncertainties and bound the entropy of such uncertainties by explicitly modelling them under some apriori assumptions. We have implemented Pareto Optimization using a genetic approach and compared the results with classical genetic algorithms and Mixed-Integer Linear Programming (MILP) models. Our results yields empirical evidence suggesting that Pareto Optimization can elude such non-deterministic errors and is a formal approach towards producing robust and reactive supply chain models.
