Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization
This addresses uncertainty modelling for risk-averse supply chain systems, but it appears incremental as it builds on existing optimization methods.
The paper tackled the problem of building robust supply chain models against irregular variations by introducing Pareto Optimization to handle uncertainties and bound their entropy, with results showing it can elude non-deterministic errors and produce robust models.
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.