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arXiv · 2004.13836

Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization

Heerok Banerjee·V. Ganapathy·V. M. Shenbagaraman
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Abstract

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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Heerok Banerjee, V. Ganapathy, V. M. Shenbagaraman. 2020-04-24. Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization. https://arxiv.org/abs/2004.13836

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