PENGEMBANGAN MODEL GREEN CAPACITATED VEHICLE ROUTING PROBLEM (GCVRP) PADA OPTIMASI DISTRIBUSI DENGAN MENGGUNAKAN METODE MIXED INTEGER LINEAR PROGRAM (MILP) (STUDI KASUS : PDAM TIRTA BINANGUN KULON PROGO)

Dias Dzaky K, NIM.: 19106060015 (2023) PENGEMBANGAN MODEL GREEN CAPACITATED VEHICLE ROUTING PROBLEM (GCVRP) PADA OPTIMASI DISTRIBUSI DENGAN MENGGUNAKAN METODE MIXED INTEGER LINEAR PROGRAM (MILP) (STUDI KASUS : PDAM TIRTA BINANGUN KULON PROGO). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

In Indonesia, the transportation sector is the second largest contributor to carbon dioxide (CO2) emissions, accounting for 24.64% of total emissions in 2019. One effort to achieve emission reduction targets in the transportation sector is by selecting the appropriate shipping routes. This study developed a mathematical model called Mixed Integer Linear Program (MILP) for the routing problem with the aim of minimizing CO2 gas emissions, while considering vehicle capacity, which is commonly known as the Green Capacitated Vehicle Routing Problem (GCVRP). The developed model uses an emission matrix that considers the difference in load for each transport and acknowledges that heavier vehicles tend to produce higher emission factors than lighter vehicles. The software used for data processing is CPLEX with Excel integration as input and output data. The model also utilizes the assistance of the Google Maps Application Programming Interface (API) in automatically calculating distances. The program output has a user-friendly display that is easy for users to read and can even display a Google Maps link to the proposed route. This study used test data on the distribution route of Perusahaan Daerah Air Minum (PDAM) Tirta Binangun Kulon Progo for 12 days from February 13-28, 2023. The results showed a significant reduction in CO2 emissions by 25.471% and a distance savings of 24.929% for the vehicles. By using the MILP mathematical model that considers factors such as vehicle capacity and load differences for each vehicle, as well as utilizing Google Maps API technology, this program is able to generate significant CO2 emission reductions and more efficient distances traveled.

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