Track: Data Analytics

Abstract

Integration across a supply chain decision levels is key on improving investment returns. Integration of different time scales leads to large scale problems usually computationally intractable. Different approaches have been proposed to tackle the problem in terms of modeling and solution methods. However, most of them are problem specific or applicable only to short time horizons. Clustering has the potential to handle such problems by grouping similar input parameters together and considerably reducing the model size while not compromising solution accuracy. This work presents a new class of clustering algorithms to support the integration of planning applications of different time scales. The clustering algorithms were formulated using integer programming with integral absolute error as similarity measure. Two different clustering algorithms were developed: normal and sequence. The models were developed in the GAMS software. Two case studies are presented to assess the algorithms outputs and computational performance using utility demand data. It was found that the algorithm is capable of finding good quality solutions; and even succeed at finding optimal solutions with a small computational effort while providing clusters with high intra-cluster similarity and low inter-cluster similarity.