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All Journal Jurnal Sistem dan Manajemen Industri Jurnal Teknik Industri JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JARES (Journal of Academic Research and Sciences) Enrichment : Journal of Management
Anak Agung Ngurah Perwira Redi
Bina Nusantara University
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Social Sciences Transportation

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Journal : Jurnal Teknik Industri

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Genetic Algorithm with Cluster-first Route-second to Solve the Capacitated Vehicle Routing Problem with Time Windows : A Case Study Karina Aginta Putri; Nur Layli Rachmawati; Mirna Lusiani; Anak Agung Ngurah Perwira Redi
Jurnal Teknik Industri Vol. 23 No. 1 (2021): June 2021
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.23.1.75-82

Abstract

In a distribution problem, designing the right distribution route can minimize the total transportation costs. Therefore, this research aims to design a distribution route that produces a minimal distribution distance by clustering the demand points first. We generated the clustering method to cluster the demand points by considering the proximity among the demand points and the total vehicle capacity. In solving this problem, we are using p-median to determine the cluster and a genetic algorithm to determine the distribution route with the characteristics of the CVRPTW problem. CVRPTW or capacitated vehicle routing problem with time windows is a type of VRP problem where there is a limitation of the vehicle capacity and service time range of its demand point. This research concludes that clustering the demand points provides a better result in terms of total distribution costs by up to 16.26% compared to the existing delivery schedule. The performance of the genetic algorithm shows an average difference of 1.73%, compared to the exact or optimal method. The genetic algorithm is 89.68% faster than the exact method in the computational time.
Co-Authors Adesatya Lentera Nagari Adji Candra Kurniawan Adji Candra Kurniawan (Universitas Pertamina) Ahmad Arif Santosa Aisyahna Nurul Mauliddina Athiyyah Zhafarina (Universitas Pertamina) Deasy Lastya Sari Faris Ahmad Saifuddin Fergyanto E. Gunawan Helen Storyna Karina Aginta Putri Mirna Lusiani Mirna Lusiani (Universitas Pertamina) Muhammad Asrol Nanda Ruswandi Nanda Ruswandi (Universitas Pertamina) Nur Layli Rachmawati Raihan Ahmad Fadhil (Universitas Pertamina) Redioka, Anak Agung Ngurah Agung Ribka Intan Putri Sinaga Silvester Adi Surya Herjuna