Minimization of Sustainable-Cost Using Tabu Search for Single Depot Heterogeneous Vehicle Routing Problem with Time Windows

Sustainable development is embraced by many groups for economic growth, environmental preservation, and social equality. This work presents a Tabu Search approach for minimising Sustainable-cost in the Single Depot Vehicle Routing Problem with Time Windows (VRPTW) with a heterogeneous fleet of vehicles. Tabu Search was given five greedy heuristic algorithms such as SAVING, SWEEP, NNH-1, NNH-2, and CIH, a random algorithm, and its sustainable variations as initial input. The algorithms were tested on the benchmark datasets of Solomon, Gehring, and Homberger, as well as a randomly generated dataset. Various computational analyses performed using Relative Percentage Deviation and Kruskal Wallis-H test indicates that the Tabu search algorithm tries to minimize the sustainable cost obtained from these initial solutions. TS-S-SAVING relatively and consistently outperforms in terms of the different performance measures considered in this study by attaining an efficiency of 96.55% for the benchmark dataset and 100% for the instance generated datasets.