Sustainability evaluation for biomass supply chain synthesis: Novel principal component analysis (PCA) aided optimisation approach

摘要

The evaluation of sustainability performance of a biomass supply chain is often compounded of a complex series of variables. The redundancies in variables often make the results become hard to be analysed and diagnosed. Therefore, principal component analysis (PCA) is introduced to reduce the redundancy of data series by converting a series of correlated variables into a set of uncorrelated variables known as principal components (PCs), without losing too much information. However, the optimisation of PCs is relatively difficult as PCs encompass of convex combinations of original variables. In this paper, a novel PCA aided optimisation approach is introduced to solve the multi-echelon biomass supply chain problem (i.e., technology selection and transportation design), with the consideration of economic, environmental (including various impact categories and environmental footprints) and social (including health aspect, safety aspect and job creation) dimensions. On top of that, analytical hierarchy process (AHP) is applied to determine the priority scale assigned to each objective. The model is illustrated by using a case study in Johor, Malaysia. In this case study, the original 13 variables (indicators) had been successfully reduced to less than 3 PCs. Besides, the obtained results are benchmarked with two other conventional optimisation approaches, namely weighted-sum approach and max-min aggregation approach. The results show that PCA optimisation approach can provide reliable and comparable results (degree of satisfaction of the obtained results, lambda(SCM) are greater than 70%). In addition, sensitivity analysis is conducted to analyse the effect of relative priority of each objective on the technology selection and transportation design. This research is also expected to be useful for the big data (large volumes of extensively varied data that are generated and processed at high velocity) analysis in future supply chain management. (C) 2018 Elsevier Ltd. All rights reserved.