Integrated Palm Biomass Supply Chain toward Sustainable Management

Abstract

Abstract Sustainable biomass supply chain management (SBSCM) problem is a multi-objective optimisation (MOO) problem since the objectives of each sustainability dimension and (or) each components under a same sustainability dimension can be conflicting. It is unlikely to have a single solution that could simultaneously satisfy all objectives. Therefore, achieving optimum for one objective requires compromise of other objectives. Several approaches have been developed to solve MOO problems. The simplest way reported from the academicians is to transform MOO problem into single objective optimisation (SOO) problem by converting all objectives into a similar form of objectives. However, converting environmental impacts into economic form might produce sub-optimal solution as the optimal result is highly dependent on the magnitude of the penalty cost. This paper presents a systematic approach which integrates both economic and environmental concerns in the supply chain by using the concept of analytic hierarchy process (AHP). Instead of merely focusing on carbon emission, this paper incorporates other environmental indicators as well, i. e., global warming potential (GWP), ozone depletion potential (ODP), acidification potential (AP), photochemical ozone creation potential (POCP), nutrification potential (NP), aquatic toxicity potential (ATP), terrestrial toxicity potential (TTP), abiotic depletion potential (ADP), water footprint and land footprint. On top of that, this paper also proposes a graphical illustration method to present the sustainability performance of the results. Both economic sustainability and environmental sustainability are expressed in a vector form. With the aid of this graphical representation, the tendency of the process towards each sustainability dimension can be clearly seen. A case study in Johor state is used to demonstrate the proposed method and a palm biomass supply chain with high sustainability performance ( λ S C M ${\lambda ^{SCM}}$ = 0.8437) is synthesised in this paper.