  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.
