A Simple Tabu Search with Crowdsourcing Data using Google Services API for Green Logistic: A case study

Green logistics is increasingly critical in mitigating the environmental impact of transportation. In this study, we address the Symmetric Green Volume Capacitated Vehicle Routing Problem with Time Windows (SG-VCVRP-TW) by developing a robust mixed-integer nonlinear programming (MINLP) model paired with a Tabu Search (TS) algorithm. Our innovative approach uniquely integrates real-world data from the Google Distance Matrix API to account for dynamic vehicle speeds and travel times, thereby enhancing the accuracy of fuel consumption and CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions predictions. The case study of a pharmaceutical distribution company in Algeria illustrates the practical benefits of our method, achieving a reduction in the daily fleet size from 16 to 12 vehicles (a 28.13% decrease, with peaks up to 48%). Moreover, our analysis reveals that incorporating variable speed profiles leads to significant improvements in model precision—with deviations over 20% under fixed-speed assumptions—and shows that longer distances do not necessarily result in higher fuel consumption. These results underscore the substantial potential of our approach to driving more sustainable and efficient transportation solutions in green logistics.