基于多目标Pareto优化的风险规避供应链系统不确定性建模
Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization
摘要：
供应链建模中的一个艰巨任务是建立鲁棒的模型来抵抗不规则的变化。随着时间序列分析和机器学习模型的发展，对经典的levenberg-marquardt算法进行了加速、权值衰减和归一化等改进，引入了一种算法优化方法来解决这一问题。在本文中，我们介绍了一种新的方法，即帕累托优化处理不确定性和约束的熵，这些不确定性明确建模下的一些先验假设。我们已经实现了帕累托优化使用遗传方法，并与经典的遗传算法和混合线性规划（MILP）模型的结果进行了比较。我们的研究结果产生的经验证据表明，帕累托优化可以避免这种不确定性的错误，是一种正式的方法，对生产强大的和反应式的供应链模型。
英文摘要：
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.
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