  The well-known Vehicle Routing Problem with Time Windows (VRPTW) aims to
reduce the cost of moving goods between several destinations while
accommodating constraints like set time windows for certain locations and
vehicle capacity. Applications of the VRPTW problem in the real world include
Supply Chain Management (SCM) and logistic dispatching, both of which are
crucial to the economy and are expanding quickly as work habits change.
Therefore, to solve the VRPTW problem, metaheuristic algorithms i.e. Particle
Swarm Optimization (PSO) have been found to work effectively, however, they can
experience premature convergence. To lower the risk of PSO's premature
convergence, the authors have solved VRPTW in this paper utilising a novel form
of the PSO methodology that uses the Roulette Wheel Method (RWPSO). Computing
experiments using the Solomon VRPTW benchmark datasets on the RWPSO demonstrate
that RWPSO is competitive with other state-of-the-art algorithms from the
literature. Also, comparisons with two cutting-edge algorithms from the
literature show how competitive the suggested algorithm is.
