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Title: Regression based scenario generation: Applications for performance management
Authors: Mitra, Sovan 
Lim, Sungmook 
Karathanasopoulos, A. 
Issue Date: 2018
Journal: Operations Research Perspectives 
Abstract: Regression analysis is a common tool in performance management and measurement in industry. Many firms wish to optimise their performance using Stochastic Programming but to the best of our knowledge there exists no scenario generation method for regression models. In this paper we propose a new scenario generation method for linear regression used in performance management. Our scenario generation method is able to produce more representative scenarios by utilising the data driven properties of linear regression models and cluster based resampling. Secondly, our scenario generation method is more robust to model ‘overfitting’ by utilising a multiple of linear regression functions, hence our scenarios are more reliable. Finally, our scenario generation method enables parsimonious incorporation of decision analysis, such as worst case scenarios, hence our scenario generation facilitates decision making. This paper will also be of interest to industry professionals
DOI: 10.1016/j.orp.2018.100095
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