Bi-criteria optimization of cleaner biofuel supply chain model by novel fuzzy goal programming technique

Bi-criteria optimization of cleaner biofuel supply chain model by novel fuzzy goal programming technique

WoS Article

In engineering, the biofuel supply chain (BSC) is a significant supply chain optimization challenge. Here, the multi-objective optimization problem for biofuel distribution is based on the life cycle energy consumption (LCEC) and the emission CO2 of involve in supply network of a biofuel. It includes numerous feedstocks, means of transportation, and locations for biofuel plant construction, as well as multiple technologies, the number of feedstocks in the agricultural system, transportation capabilities, crop yields, and market needs. To provide a modelling framework for optimization issues with multi-fold uncertainty, different reduction approaches to turn a Type-2 Fuzzy variable into a Type-1 Fuzzy variable through the expected value method are described. The deterministic BSC model is then solved utilizing Goal Programming and Weighted Sum Method using Generalized Reduced Gradient Technique. The goal BSC models are (i) Minimization of life cycle energy consumption (ii) total emission of CO2 in uncertain environment. The numerical experiments demonstrated with different optimistic labels by which managers can control their objects by choosing optimistic values. Here, we have developed a systematic optimization approaches for the advancement of biofuel transportation, supply and process chain analysis, sustainability, and industrial optimization. Finally, a real-world case study is used to demonstrate the applicability of the suggested approach. The findings show that the proposed solutions are successful in reducing logistical expenses.

In engineering, the biofuel supply chain (BSC) is a significant supply chain optimization challenge. Here, the multi-objective optimization problem for biofuel distribution is based on the life cycle energy consumption (LCEC) and the emission CO2 of involve in supply network of a biofuel. It includes numerous feedstocks, means of transportation, and locations for biofuel plant construction, as well as multiple technologies, the number of feedstocks in the agricultural system, transportation capabilities, crop yields, and market needs. To provide a modelling framework for optimization issues with multi-fold uncertainty, different reduction approaches to turn a Type-2 Fuzzy variable into a Type-1 Fuzzy variable through the expected value method are described. The deterministic BSC model is then solved utilizing Goal Programming and Weighted Sum Method using Generalized Reduced Gradient Technique. The goal BSC models are (i) Minimization of life cycle energy consumption (ii) total emission of CO2 in uncertain environment. The numerical experiments demonstrated with different optimistic labels by which managers can control their objects by choosing optimistic values. Here, we have developed a systematic optimization approaches for the advancement of biofuel transportation, supply and process chain analysis, sustainability, and industrial optimization. Finally, a real-world case study is used to demonstrate the applicability of the suggested approach. The findings show that the proposed solutions are successful in reducing logistical expenses.

Biofuel supply chain (BSC); Goal Programming Method (GPM); Fuzzy Type-2 Trapezoidal Variables; Expected value

Biofuel supply chain (BSC); Goal Programming Method (GPM); Fuzzy Type-2 Trapezoidal Variables; Expected value

JANA, D.; BHATTACHARJEE, S.; DOSTÁL, P.; JANKOVÁ, Z.; BARNALI, B.

2022

03.04.2022

Elsevier

2772-3909

Cleaner Logistics and Supply Chain

4

2022

United Kingdom of Great Britain and Northern Ireland

1

12

12

https://www.sciencedirect.com/science/article/pii/S2772390922000178?via%3Dihub