Multi-objective optimization of sustainable biomass supply chain network design

•The optimal design and planning of the biomass supply chain network is considered.•Three-stage sequential solution methodology is proposed.•A novel multi-objective mixed integer linear programming model is developed.•A case study is solved with real-world instances. This paper addresses the optimal...

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Veröffentlicht in:Applied energy 2020-08, Vol.272, p.115259, Article 115259
Hauptverfasser: Gital Durmaz, Yeşim, Bilgen, Bilge
Format: Artikel
Sprache:eng
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Zusammenfassung:•The optimal design and planning of the biomass supply chain network is considered.•Three-stage sequential solution methodology is proposed.•A novel multi-objective mixed integer linear programming model is developed.•A case study is solved with real-world instances. This paper addresses the optimal design and planning of the biomass supply chain network that encompasses flow from poultry farms to biogas facilities. A novel multi-stage solution methodology is developed to solve the sustainable biomass supply chain network design problem. Geographical Information Systems, and Analytic Hierarchy Process Techniques are used to determine the candidate location of biogas facilities. The proposed multi-objective mixed integer linear programming model is capable of making strategic decisions (optimal biogas facility locations with capacities) along with the tactical decisions (transportation network flows). The model incorporates the two objective function of maximization of the profit, and minimization of total distance between poultry farms and biogas facilities. The aim is to determine the optimal number, location, and size of the biogas facilities, as well as the network flow, and electricity generated. The applicability of the model and solution methodology is demonstrated through a case study for a poultry supply chain network in Turkey. Additionally, sensitivity analysis is conducted to account for the impact of different parameters on the model. Sensitivity analysis show that both maximum distance parameter, and purchasing prices have major impact on decisions, and financial yield.
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2020.115259