Optimalitas Rute pada Pengiriman Multiperjalanan dengan Armada Kendaraan Listrik Heterogen

Salsabilla, Kamilia; Bakhtiar, Toni; Hanum, Farida · 2024 · Crossref

DOI: 10.37905/jjom.v6i1.23993

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Summary

This study addresses the Electric Vehicle Routing Problem with Multiple Trips (EVRPMT) using a heterogeneous fleet, aiming to minimize operational costs in sustainable logistics. The research is motivated by the growing adoption of electric vehicles (EVs) and the specific challenges they pose, such as limited battery range, charging time constraints, and the need for battery swapping infrastructure. Unlike conventional vehicles, EVs require careful route planning to account for battery capacity limits and the availability of Battery Swapping Stations (BSS). The paper specifically explores how allowing vehicles to perform multiple trips and utilizing a fleet with varying loading and battery capacities affects route optimality. The authors formulate the problem as a Mixed-Integer Linear Programming (MILP) model. The objective function minimizes total operational costs, comprising travel costs and battery swapping costs. Key constraints include ensuring each customer is visited exactly once, maintaining closed routes starting and ending at the depot, respecting vehicle load and battery capacity limits, and eliminating subtours using the Miller-Tucker-Zemlin formulation. The model assumes that vehicles leave the depot and BSS with fully charged batteries and that power consumption is constant per unit distance. The study validates the model through two illustrative examples solved using the branch-and-bound method via Lingo 18.0. Example 1 involves a heterogeneous fleet serving 8 customers, while Example 2 uses a homogeneous fleet for 10 customers; both scenarios include one depot and two BSS. Simulation results demonstrate that the model effectively identifies global optimal solutions. In Example 1, the heterogeneous fleet achieved a minimum operational cost of 827,900 IDR, solved in 7.85 seconds. The analysis reveals that battery capacity and the presence of BSS significantly influence route selection. For instance, in the heterogeneous case, one vehicle with lower battery capacity was forced to visit a BSS immediately after leaving the depot to swap batteries, whereas the other vehicle completed its routes without swapping. The study confirms that allowing multiple trips enables the completion of distribution tasks when total customer demand exceeds the fleet's single-trip capacity. The significance of this work lies in providing an exact optimization framework for EVRPMT with heterogeneous fleets, a variant that is less explored in existing literature compared to heuristic approaches. The findings highlight the critical role of battery infrastructure and vehicle specifications in determining efficient routes. By demonstrating that exact methods can solve these complex routing problems efficiently for small to medium-sized instances, the paper contributes to the development of green logistics strategies that reduce emissions and operational costs. The results underscore the importance of integrating technical EV constraints, such as battery swapping, into logistical planning to achieve true cost-efficiency and sustainability.

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• last mile delivery