Optimized Vehicle Routing Problem for The Last Mile E-Commerce Parcels Delivery Using E-Cargo Bikes
DOI:
https://doi.org/10.59973/emjsr.114Keywords:
Capacitated Vehicle Routing Problem, Last Mile Delivery, Evolutionary Solver, Genetic Algorithm, Electric Cargo Bikes, Logistics Operational Cost, Completion Time, Carbon Footprint, Battery Energy Consumption, Central London Case StudyAbstract
This research addresses the Optimized Vehicle Routing Problem (VRP) for last-mile e-commerce parcel delivery using e-cargo bikes in a Central London case study setting. The primary objective is to enhance delivery routing efficiency, and reduce operational costs, competition time, and carbon footprints through the application of the Evolutionary Algorithm using the Excel Solver. With the rise of e-commerce, efficient last-mile delivery has become increasingly important to ensure customer satisfaction and sustainability. As an alternative, electric cargo bikes offer promising benefits, capable of navigating congested areas and providing eco-friendly delivery solutions since traditional delivery methods face challenges such as traffic congestion, high fuel costs, and significant carbon emissions. The research begins with a comprehensive literature review, highlighting the various VRP variants and their applications in urban logistics. Existing studies have primarily focused on optimization techniques for conventional vehicles, with limited attention to e-cargo bikes. This study fills that gap by developing the Mixed-integer linear programming (MILP) Mathematical Model specifically for e-cargo bike routes, considering factors such as vehicle capacity, delivery time windows, and distance minimization. Four different scenarios were tested to evaluate the effectiveness of the proposed algorithm. Data for this research was meticulously collected from e-commerce delivery records, urban traffic conditions, and geographical information pertinent to central London's delivery routes. The evolutionary algorithm was implemented to generate and iteratively improve upon feasible routing solutions by setting different parameters of population sizes and mutation rates which led to minimizing the total distance traveled and associated costs. Consequently, the best-optimized routes resulted a substantial reduction in daily logistics costs, from £141 to £112 which reduced the traveling distances from 65.176 km to 29.810 km which underscores the economic viability of e-cargo bikes for last-mile delivery. The research also highlights the substantial potential of e-cargo bikes to lower CO2 emissions, contributing to more sustainable urban logistics. The findings support the integration of e-cargo bikes as a viable alternative to traditional delivery vehicles, promoting both environmental benefits and daily operational efficiency, particularly in metropolitan areas like Central London. This study provides a foundational understanding of the benefits and challenges associated with e-cargo bikes in last-mile delivery, paving the way for further advancements in sustainable urban logistics.
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