Open AccessOptimizing the Heterogeneous Fleet Vehicle Routing Problem with Time Window on Urban Last Mile Delivery
Author(s) -
Talitha Ayu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/830/1/012100
Subject(s) - vehicle routing problem , fleet management , last mile (transportation) , computer science , transport engineering , routing (electronic design automation) , operations research , process (computing) , trips architecture , total cost , plan (archaeology) , mile , engineering , business , computer network , physics , astronomy , operating system , accounting , archaeology , history
The growth of the e-commerce business in Indonesia during the COVID-19 pandemic has increased the demand for last mile delivery (LMD) services. LMD activities with a large frequency of trips can increase logistics operational costs if operational handling is not carried out optimally. Optimization of LMD distribution routes is applied as an operational handling and also as a solution to the vehicle routing problem (VRP). VRP is an important problem to consider in transportation modeling where an optimum route plan is desired to obtain the minimum cost. The purpose of this paper is to optimize the last mile delivery route using the Heterogeneous Fleet Vehicle Routing Problem with Time Window (HFVRP-TW) model in urban areas. The route optimization process is carried out by developing and applying the HFVRP-TW model using data from one of the express delivery companies in Jakarta, Indonesia, and then simulating and forming several operational scenarios. The results of the analysis show that the application of route optimization with variations in vehicle types reduced operating costs by an average of 58.46% - 65.98% compared to the existing conditions. Scenario evaluation is carried out to obtain the scenario with the lowest operational cost. The evaluation results show that the best scenario is a scenario that has the closest characteristics to existing conditions, where there is no change in the type of vehicle used and optimization is only applied to operational routes. However, several other scenarios using a variety of different vehicle types still resulted in a reduction in operating costs of more than 50%.