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Model Scheduling Optimization Workforce Management Marketing

APTISI Transactions on Management (ATM) · Vol. 4 No. 2 (2020) · hlm. 92–100

DOI
10.33050/atm.v4i2.1259
ISSN
2622-6804, 2622-6812
Terbit
2020-02-20
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This research focuses on completing workforce management marketing scheduling using genetic metaheuristic methods. Optimal scheduling to determine the duration of a job, the quality of employees, and meet the challenges to increase work scheduling targets according to marketing employee skills by maximizing operational time while providing satisfactory services to customers. Marketing employees increasingly rely on the right time to connect with their customers. The problem in this study considers marketing skills and activities to carry out work activities, namely promotion, follow-up, and stand-by in the office with the limitations of the existing workforce. This problem has the nature of NP-Hard so a quick solution requires the use of metaheuristic methods. The metaheuristic method was built in 11 constraints. The results show that the genetic metaheuristic method is capable of producing far better results. Therefore, employee scheduling is very important. This study aims to develop an optimization model for employee scheduling and to maximize operational time work. With this model, it is expected to achieve optimal scheduling for marketing management workforce.

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  • Universitas Raharja
  • Satya Wacana Christian University
  • University of Mataram
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Qurotul AiniUniversitas Raharja16
Ninda LutfianiUniversitas Raharja8
Yuli Prasetyo AdhiAfiliasi tidak diketahui5
Irwan SembiringSatya Wacana Christian University5
Danny ManonggaSatya Wacana Christian University5
Muhyidin MuhyidinUniversity of Mataram4
Po Abas SunaryaAfiliasi tidak diketahui4
Alfiah KhoirunisaUniversitas Raharja3
Muhtarom MuhtaromAfiliasi tidak diketahui3
Agung RizkyAfiliasi tidak diketahui3
Sutarto WijonoAfiliasi tidak diketahui3
Shofiyul MillahAfiliasi tidak diketahui3
Desy AprianiAfiliasi tidak diketahui2
Dwi ApriliasariAfiliasi tidak diketahui2
Mohamad RakhmansyahAfiliasi tidak diketahui2
Ihsan Nuril HikamAfiliasi tidak diketahui2
Adam FaturahmanAfiliasi tidak diketahui2
Natasya Aprila YusufAfiliasi tidak diketahui2
Umi NarimawatiAfiliasi tidak diketahui2
Hindriyanto Dwi PurnomoSatya Wacana Christian University2
Nur RahmawatiAfiliasi tidak diketahui2
Muarifuddin MuarifuddinAfiliasi tidak diketahui1
Sungkowo Edy MulyonoAfiliasi tidak diketahui1
Woro Apriliana SariAfiliasi tidak diketahui1
Anisya Pratama NingsihAfiliasi tidak diketahui1
Rama Dirga Dwi SaktiAfiliasi tidak diketahui1
Chairunnisa AsmawiAfiliasi tidak diketahui1
Alexander WilliamsAfiliasi tidak diketahui1
Sella AvionitaAfiliasi tidak diketahui1
Dini HidayatiAfiliasi tidak diketahui1
Andriyansah AndriyansahAfiliasi tidak diketahui1
Galih Putra CesnaAfiliasi tidak diketahui1
Ahmad Yadi FauziAfiliasi tidak diketahui1
Nur AzizahAfiliasi tidak diketahui1
Raihan RaihanAfiliasi tidak diketahui1
Marviola HardiniAfiliasi tidak diketahui1
Aan KaniviaAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://ijc.ilearning.co/ATM/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 6 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Model Scheduling Optimization Workforce Management Marketing</dc:title>
	<dc:title xml:lang="id">Model Scheduling Optimization Workforce Management Marketing</dc:title>
	<dc:creator xml:lang="en">Rahardja, Untung</dc:creator>
	<dc:creator xml:lang="en">Andriyani, Fitri</dc:creator>
	<dc:creator xml:lang="en">Triyono, Triyono</dc:creator>
	<dc:subject xml:lang="en">Workforce Management</dc:subject>
	<dc:subject xml:lang="en">Marketing</dc:subject>
	<dc:subject xml:lang="en">Genetic Metaheuristic Methods</dc:subject>
	<dc:description xml:lang="en">This research focuses on completing workforce management marketing scheduling using genetic metaheuristic methods. Optimal scheduling to determine the duration of a job, the quality of employees, and meet the challenges to increase work scheduling targets according to marketing employee skills by maximizing operational time while providing satisfactory services to customers. Marketing employees increasingly rely on the right time to connect with their customers. The problem in this study considers marketing skills and activities to carry out work activities, namely promotion, follow-up, and stand-by in the office with the limitations of the existing workforce. This problem has the nature of NP-Hard so a quick solution requires the use of metaheuristic methods. The metaheuristic method was built in 11 constraints. The results show that the genetic metaheuristic method is capable of producing far better results. Therefore, employee scheduling is very important. This study aims to develop an optimization model for employee scheduling and to maximize operational time work. With this model, it is expected to achieve optimal scheduling for marketing management workforce.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2020-02-20</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://ijc.ilearning.co/ATM/article/view/1259</dc:identifier>
	<dc:identifier>10.33050/atm.v4i2.1259</dc:identifier>
	<dc:source xml:lang="en">APTISI Transactions on Management (ATM); Vol. 4 No. 2 (2020): July; 92-100</dc:source>
	<dc:source xml:lang="id">APTISI Transactions on Management (ATM); Vol 4 No 2 (2020): ATM (APTISI Transactions on Management); 92-100</dc:source>
	<dc:source>2622-6804</dc:source>
	<dc:source>2622-6812</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://ijc.ilearning.co/ATM/article/view/1259/349</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 Untung Rahardja, Fitri Andriyani, Triyono Triyono (Author)</dc:rights>
	<dc:rights xml:lang="en">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>