Sustainable multi-objective solid TSP to reduce carbon emission impact by metaphor-free aspiration level based MOQO Jaya algorithm

This work delves into implementing two models of uncertainty theory, namely the Expected Value Model (EVM) and the Optimistic Value Model (OVM), to address the complexities of the Multi-Objective Travelling Salesman Problem (MOTSP) and the Multi-Objective Solid Travelling Salesman Problem (MOSTSP). The study considers the impact of carbon emissions in these models. It employs a newly developed Aspiration Level-based Multi-Objective Quasi Oppositional Jaya (AL-based MOQO Jaya) algorithm for solving problems based on real-world data from the city of Surat for 10 and 50 nodes. The significant advantage of using the AL-based MOQO Jaya algorithm is it does not require any algorithm-specific parameters. Hence, it can solve different optimization problems irrespective of parameter setting. The average run time of AL-based MOQO Jaya for the EVM model is 1.1952 s, and for the OVM model is 1.0554s with ten nodes. For 50 nodes, the average run time for the EVM model is 1.5595 s, and for OVM, it is 1.4764 s, which is relatively lesser compared to CPLEX, FPT, HGA, and Rao algorithms for both MOTSP and MOSTSP. Further, the AL-based MOQO Jaya algorithm dominates the solutions obtained by other established techniques. The study concludes solutions obtained through the AL-based MOQO Jaya algorithm are more efficient and provide a viable alternative for decision-makers.