English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The well-known Vehicle Routing Problem with Time Windows (VRPTW) aims toreduce the cost of moving goods between several destinations whileaccommodating constraints like set time windows for certain locations andvehicle capacity. Applications of the VRPTW problem in the real world includeSupply Chain Management (SCM) and logistic dispatching, both of which arecrucial to the economy and are expanding quickly as work habits change.Therefore, to solve the VRPTW problem, metaheuristic algorithms i.e. ParticleSwarm Optimization (PSO) have been found to work effectively, however, they canexperience premature convergence. To lower the risk of PSO's prematureconvergence, the authors have solved VRPTW in this paper utilising a novel formof the PSO methodology that uses the Roulette Wheel Method (RWPSO). Computingexperiments using the Solomon VRPTW benchmark datasets on the RWPSO demonstratethat RWPSO is competitive with other state-of-the-art algorithms from theliterature. Also, comparisons with two cutting-edge algorithms from theliterature show how competitive the suggested algorithm is.

Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows