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Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm

Journal of Information Systems Engineering and Business Intelligence · Vol. 5 No. 2 (2019) · pp. 120–130

DOI
10.20473/jisebi.5.2.120-130
ISSN
2443-2555, 2598-6333
Published
2019-10-24
Publisher
Universitas Airlangga
Language
eng

Abstract

Background: The applications of constrained optimization have been developed in many problems. One of them is production planning. Production planning is the important part for controlling the cost spent by the company.Objective: This research identifies about production planning optimization and algorithm to solve it in approaching. Production planning model is linear programming model with constraints: production, worker, and inventory.Methods: In this paper, we use heurisitic Particle Swarm Optimization-Genetic Algorithm (PSOGA) for solving production planning optimization. PSOGA is the algorithm combining Particle Swarm Optimization (PSO) and mutation operator of Genetic Algorithm (GA) to improve optimal solution resulted by PSO. Three simulations using three different mutation probabilies: 0, 0.01 and 0.7 are applied to PSOGA. Futhermore, some mutation probabilities in PSOGA will be simulated and percent of improvement will be computed.Results: From the simulations, PSOGA can improve optimal solution of PSO and the position of improvement is also determined by mutation probability. The small mutation probability gives smaller chance to the particle to explore and form new solution so that the position of improvement of small mutation probability is in middle of iteration. The large mutation probability gives larger chance to the particle to explore and form new solution so that the position of improvement of large mutation probability is in early of iteration.Conclusion: Overall, the simulations show that PSOGA can improve optimal solution resulted by PSO and therefore it can give optimal cost spent by the company for the planning.Keywords: Constrained Optimization, Genetic Algorithm, Linear Programming, Particle Swarm Optimization, Production Planning

Keywords

Collaboration network of the authors

40 authors · 133 co-authorship links · 91 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Sepuluh Nopember
  • Universitas Nahdlatul Ulama Surabaya
  • Institut Teknologi Adhi Tama Surabaya
  • Darul Ulum Islamic University
  • Universitas Airlangga
  • Binus University
  • Universitas Muhammadiyah Malang
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Hendro NurhadiInstitut Teknologi Sepuluh Nopember9
Berny Pebo TomasouwUniversitas Pattimura7
Andy SuryowinotoInstitut Teknologi Adhi Tama Surabaya6
Fajar Annas SusantoUniversitas Nahdlatul Ulama Surabaya5
Dieky AdzkiyaInstitut Teknologi Sepuluh Nopember4
Hamzah ArofUniversity of Malaya3
Firman YudiantoUniversitas Nahdlatul Ulama Surabaya2
Katherin IndriawatiInstitut Teknologi Sepuluh Nopember2
Reza Maliki AkbarInstitut Teknologi Sepuluh Nopember2
Aji Akbar FirdausUniversitas Airlangga2
Vaizal Asy’ariUniversity of Pembangunan Nasional Veteran Jawa Timur2
Abdul MuhithDarul Ulum Islamic University2
Dian AdriantoPusat Hidro-Oseanografi Angkatan Laut2
Mohamad Yusak AnshoriUniversitas Nahdlatul Ulama Surabaya2
Teay ShawyunAffiliation unknown2
Dennis Villasor MadrigalUniversity of Negros Occidental – Recoletos2
Erna AprilianiInstitut Teknologi Sepuluh Nopember1
Mayga KikiInstitut Teknologi Sepuluh Nopember1
Subchan SubchanInstitut Teknologi Sepuluh Nopember1
Moch ZakariaInstitut Teknologi Adhi Tama Surabaya1
Muhammad Sawal BaitalDiponegoro University1
Angga Adhitya WidjanarkoInstitut Teknologi Adhi Tama Surabaya1
Yuliyanto Agung PrabowoInstitut Teknologi Adhi Tama Surabaya1
Nyoman JuniartaUniversité de Lorraine1
Ragil Puji RahayuUniversitas Nahdlatul Ulama Surabaya1
Dina NovitaUniversitas Muhammadiyah Surabaya1
Arasy AlimudinUniversitas Narotama1
Akas Yekti Pulih AsihUniversitas Nahdlatul Ulama Surabaya1
Puguh TriwinantoNational Research and Innovation Agency1
Rachman Sinatriya MarjiantoUniversitas Airlangga1
Hendro NurhadiInstitut Teknologi Sepuluh Nopember1
Andi Mohammad Nurdin HalidState University of Makassar1
Agung SudjatmokoBinus University1
Adi Teguh SupraptoBinus University1
Muhaimin MuhaiminUniversitas Jambi1
Sena Sukmananda SupraptoAffiliation unknown1
Vicky Andria KusumaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm</dc:title>
	<dc:creator>Rahmalia, Dinita</dc:creator>
	<dc:creator>Herlambang, Teguh</dc:creator>
	<dc:creator>Saputro, Thomy Eko</dc:creator>
	<dc:subject xml:lang="en-US">Linear Programming</dc:subject>
	<dc:subject xml:lang="en-US">Constrained Optimization</dc:subject>
	<dc:subject xml:lang="en-US">Production Planning</dc:subject>
	<dc:subject xml:lang="en-US">Particle Swarm Optimization</dc:subject>
	<dc:subject xml:lang="en-US">Genetic Algorithm</dc:subject>
	<dc:description xml:lang="en-US">Background: The applications of constrained optimization have been developed in many problems. One of them is production planning. Production planning is the important part for controlling the cost spent by the company.Objective: This research identifies about production planning optimization and algorithm to solve it in approaching. Production planning model is linear programming model with constraints : production, worker, and inventory.Methods: In this paper, we use heurisitic Particle Swarm Optimization-Genetic Algorithm (PSOGA) for solving production planning optimization. PSOGA is the algorithm combining Particle Swarm Optimization (PSO) and mutation operator of Genetic Algorithm (GA) to improve optimal solution resulted by PSO. Three simulations using three different mutation probabilies : 0, 0.01 and 0.7 are applied to PSOGA. Futhermore, some mutation probabilities in PSOGA will be simulated and percent of improvement will be computed.Results: From the simulations, PSOGA can improve optimal solution of PSO and the position of improvement is also determined by mutation probability. The small mutation probability gives smaller chance to the particle to explore and form new solution so that the position of improvement of small mutation probability is in middle of iteration. The large mutation probability gives larger chance to the particle to explore and form new solution so that the position of improvement of large mutation probability is in early of iteration.Conclusion: Overall, the simulations show that PSOGA can improve optimal solution resulted by PSO and therefore it can give optimal cost spent by the company for the  planning.Keywords: Constrained Optimization, Genetic Algorithm, Linear Programming, Particle Swarm Optimization, Production Planning</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2019-10-24</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/11799</dc:identifier>
	<dc:identifier>10.20473/jisebi.5.2.120-130</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 5 No. 2 (2019): October; 120-130</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/11799/8499</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 The Authors. Published by Universitas Airlangga.</dc:rights>
</oai_dc:dc>