Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 18 January 2020 / Approved: 20 January 2020 / Online: 20 January 2020 (10:21:00 CET)

How to cite: Banerjee, H. Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization. Preprints 2020, 2020010226 (doi: 10.20944/preprints202001.0226.v1). Banerjee, H. Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization. Preprints 2020, 2020010226 (doi: 10.20944/preprints202001.0226.v1).

Abstract

Risk modelling along with multi-objective optimization problems have been at theepicenter of attention for supply chain managers. In this paper, we introduce a datasetfor risk modelling in sophisticated supply chain networks based on formal mathematical models. We have discussed the methodology and simulation tools used to synthesize the dataset. Additionally, the underlying mathematical models are discussed in granular details along with providing directions to conducting statistical analyses or neural machine learning models. The simulation is performed using MATLAB ™Simulink and the models are illustrated as well.

Supplementary and Associated Material

Subject Areas

Supply Chain Management (SCM); Supply Chain Risk Management (SCRM); risk modelling; time-series analysis; machine learning

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