Bilevel Fractional/Quadratic Green Transshipment Problem by Implementing AI traffic control system with Multi Choice Parameters Under Fuzzy Environment

Abstract

Objectives: To investigate the effectiveness of AI based traffic control system on reducing the economic and environmental cost in the context of the transshipment problem.
Methods: The mathematical model of bilevel fractional/quadratic green transshipment problem by implementing AI traffic control system is formulated and numerical example is provided to emphasize the nature of this model. Due to inherent uncertainty, fermatean fuzzy parameters are incorporated in this model. Also, Supply and demand are considered as fermatean fuzzy multi choice. Existing fermatean fuzzy programming is used to find the solutions for proposed transshipment model.
Findings: Comparative study has been made for bilevel fractional/quadratic transshipment problem with and without implementation of AI traffic control system. Optimum Solutions obtained for the proposed model by using prescribed method reveals that the bilevel fractional/quadratic green transshipment problem gives the minimum transportation cost, deterioration cost, carbon emission cost than the transshipment problem with traditional traffic control system. Obtained solutions for bilevel fractional/quadratic green transshipment problem with implementation of AI traffic control system shows a reduction of 7.8% in transportation cost, 4% in cost of carbon emission than the traditional transshipment problem. Meanwhile, obtained solutions for bilevel fractional/quadratic green transshipment problem shows a reduction of 14% in cost and 14.4% in time than bilevel fractional/quadratic green transportation problem.
Novelty: The efficiency of bilevel fractional/quadratic green transshipment problem by implementing AI traffic control system with multi choice parameters under Fermatean fuzzy environment is not yet investigated in literature.