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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

7 authors · 12 co-authorship links · 7 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
  • Darul Ulum Islamic University
  • Universitas Nahdlatul Ulama Surabaya
  • Universitas Muhammadiyah Malang
  • 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 Nopember2
Dieky AdzkiyaInstitut Teknologi Sepuluh Nopember2
Erna AprilianiInstitut Teknologi Sepuluh Nopember1
Mayga KikiInstitut Teknologi Sepuluh Nopember1

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 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<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>