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Journal : Bulletin of Electrical Engineering and Informatics

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance Purba Daru Kusuma; Meta Kallista
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3223

Abstract

One problem in collaborative pickup delivery problem (PDP) was excessive outsourced jobs. It happened in many studies on the collaborative PDP. Besides, the revenue sharing in it was unclear although important. This work aimed to propose a novel collaborative PDP model which minimizes total travel distance while maintains low outsourced jobs. It proposed several contributions. First, it prioritized internal jobs first rather than full collaborative model. Second, it proposed new revenue sharing model. It adopted cluster-first route-second and mixed pickup and delivery. It was developed by combining the genetic algorithm and nearest distance algorithm where the genetic algorithm was used in the clustering process and the nearest distance was used in the routing process. The simulation result shows that the proposed model was better than the comparing models: (1) combined K-means and genetic algorithm model (KMGA) and (2) combined simulated annealing and last-in first-out (SNLIFO) model. When the number of orders was high (300 units), the total travel distance of the proposed model was 37 percent lower than the KMGA model and 30 percent lower than the SNLIFO model. In average, the outsourcing rate of the proposed model was 70 percent lower than the previous models.
Coordinated COVID-19 vaccination scheduling model by using nearest distance-single course timetabling method Purba Daru Kusuma; Ratna Astuti Nugrahaeni
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3384

Abstract

This work proposes a new coordinated vaccine scheduling model suitable for the city size COVID-19 vaccination program. It is different from the existing COVID-19 vaccination scheduling mechanism where there is no coordination among endpoint providers. On the other side, the vaccine stock in every provider is limited, so that this mismatch creates many unserved participants. Moreover, studies on the COVID-19 vaccination scheduling problem are hard to find. This work aims to solve this mismatch problem. It is developed by combining the nearest distance and the single course timetabling. It is then optimized by using a cloud theory based-simulated annealing algorithm. The simulation result shows that the proposed model outperforms both the uncoordinated and basic course timetabling models. It can minimize the number of unserved participants, total travel distance, and the number of participants with missed timeslot. It produces zero unserved participants if the total vaccine quantity is at least equal to the total number of participants. The proposed model creates lower total travel distance than the uncoordinated or basic course timetabling adopted model. It is also better than the basic course timetabling model in creating a low number of participants with missed timeslot.
Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration Kusuma, Purba Daru; Hasibuan, Faisal Candrasyah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6507

Abstract

This study offers a new swarm-based metaheuristic: random-guided optimizer (RGO). RGO has novel mechanics in shifting the random motion into a guided motion strategy during the iteration. In RGO, the iteration is divided into three equal size phases. In the first phase, the unit walks randomly inside the search space to tackle the local optimal problem earlier. In the second phase, each unit uses a unit selected randomly among the population as a reference in conducting the guided motion. In the third phase, each unit conducts guided motion toward or surpasses the best unit. Through simulation, RGO successfully finds the acceptable solution for 23 benchmark functions. Moreover, RGO successfully finds the global optimal solution for four functions: Branin, Goldstein-Price, Six Hump Camel, and Schwefel 2.22. RGO also outperforms slime mold algorithm (SMA), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO) in solving 12, 20, 12, and 1 function consecutively. In the future, improvement can be made by transforming RGO into solid multiple-phase strategy without losing its identity as a metaheuristic with multiple strategy in every iteration.
Co-Authors Abdi Hazman Abdul Rohim Achmad Rizal Aditya Enggar Adrian Sabagus Tanazri Afifah Shalihah Agita Chrisna Agita Fajar Prabowo Ahmad Fauzan Fauti Albert Kurniawan, Albert Alfiansyah, Alvan Anas Satria Anas Satria Andrew Brian Osmond Andri Liem Anggraini, Ratika Dwi ANGGUNMEKA LUHUR PRASASTI Anton Siswo Raharjo Anton Siswo Raharjo Ansori Anton Siswo Raharjo Ansori Anton Siswo Raharjo AnsoriI Arief Ilham Novandi Arief Ilham Novandi Ario Dewantoro Asep Mulyana Ashri Dini Maharawati Ashri Dinimaharawati Ashridini Maharawati Astrid Melati Aulia Wildan Axel David Bangkit Surya Praja Budhi Irawan Burhannuddin Dirgantoro Casi Setianingsih Cut Aisyah Ilmi Deyan Havith Dailamy Diantoro Arifian Dimas Anjar Saputro Dipo Suryantoro Dwi Putra, Sulistyo Emantoko Fachry Reiza Fadli Idris Fairuz Azmi Faisal Candrasyah Hasibuan Fajar Hari Andriana Farhansyah Iqbal Fadjrianto Farid Reza Sukma Fauzan, Nadhifi Qurrunul Bahratu Fawwaz Aboeruslan Muyadi Fikri Reksa Maulana Fiqri Ramadhan Friza Fahmi Hutama Gabriela, Melanie Gema Wahyu Saputra Gerin Sonia Yuki Lumban Tobing Grace Cyndiana Hafidz Kahamdany Hafizh Septian Pristanto Hanatar Adi Naluri Harahap, M Yusril Fauzan Hariwidjaja, Valrama Wardhana Herwin Yudha Setyawan Hikmawan, Fakhrity Ikhsan Hakiki Ilham Arisyandy Ilham Majid Rabbani Ilyas Hermawan Irfan Setiawan Irham Imami Harahap Irma Damayanti Iwan Iwut Tritoasmoro Jabal Rachmah Jenni Teresia Kemas Muhammad Rizky Abdillah Khairunnisa Br Ginting Lutfi Hadi Wicaksono Mahaasin, Habib Irfan Markus Lamserep Hutauruk Meta Kallista Mirza Ahmad Febrian Mohammad Ibrahim Al Mahi Mohammad Viko Mashar Muhammad Agung Laksono Muhammad Alif Fathiraihan Muhammad Fadhil Muhammad Hafidz Muhammad Insan Aulia Muhammad Junaid Musa Muhammad Ken Muhammad Pascal Aryan Muhammad Taufik Hidayat Muhammad Thariq Machaz Nabhan, Muhammad Sidqi Nadhifah Nadhifah Naema Simanjuntak Nasheeri, Al Ghifary Akmal Naufira Septriyanti Nugraha, Alvin Yoga Nurdin Panji Christoper Silalahi Prabowo Wahyu Basuki Prayoga, Ivan Fernanda Putti, Fasya Hanifah Rachman Fadly Krisdiantoro Raja Ilham Maulidani Gumelar Raka Putra Gustian Ramadhan, Achmad Rionov Faddillah Ramadhanti, Tassya Randy Erfa Saputra Rangkuti, Abdul Haris Ratna Astuti Ratna Astuti Nugrahaeni Reza Rendian Septiawan Rifqi Muhammad Fikri Rizka Shinta Wulandari Rizki Akbari Tamin Rizky Maulana Roswan Latuconsina Rumaini M Rumani M Ryan Adytia Ryan Gani Dharmawan Salimah, Hurin Salwa, Nabilah Samgusdian, Arfara Yema Saputra, Agung Aji Saputra, Dany Eka Septian Putra Manuel Simangunsong Septian Rizki Agrianto Silvia Latifah Putri Suaib, Fauzil Fahrezi Theodore Dian Arief Sianipar Tinton Aji Sadewo Tito Waluyo Purboyo Utama, Muhammad Aimar Rizki Whibi Waskita Wicaksono Wijaya, Anjas Rahmanta Cahya Wilda Satria Yohanes Yogas Herlambang Yoviandi Eka Prakoso Yulin Zurina Zaki Zamzami Zurratul Ikhsan