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

• URL: http://arxiv.org/abs/2004.13836v1
• Date: Fri, 24 Apr 2020 21:04:25 GMT
• Title: Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization
• Authors: Heerok Banerjee, V. Ganapathy and V. M. Shenbagaraman
• Abstract summary: One of the arduous tasks in supply chain modelling is to build robust models against irregular variations. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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