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All Journal Journal Sensi: Strategic of Education in Information System
Nurfilael, Gagas Nurfilae
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Optimizing Workforce Scheduling Using Ant Colony Optimization Algorithm: Case Study PT. Cloud Hosting Indonesia Nurfilael, Gagas Nurfilae; Widodo, Agung Mulyo; Anwar, Nizirwan; Ichwani, Arief
Journal Sensi: Strategic of Education in Information System Vol 11 No 1 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i1.3766

Abstract

Effective workforce scheduling is crucial for enhancing productivity and maintaining service quality at PT. Cloud Hosting Indonesia. Workforce scheduling is the process of organizing and allocating labor to various tasks and responsibilities within an organization. Ant Colony Optimization (ACO) is a probabilistic technique used to solve computation problems by finding the best path through a graph. Inspired by the behavior of ants, particularly how they find food, Ant Colony Optimization can optimize shift schedules, reduce conflicts, and improve employee performance. However, there are current irregularities, insufficient rest periods, and unpredictable holidays. Ant colony optimization is applied to address these problems. The result of this shows that the Ant Colony Optimization algorithm is capable of producing more optimal schedules with high efficiency, achieving a Best Cost of 100 in 1 minute and 6 seconds. This is better compared to other methods such as Particle Swarm Optimization (PSO), which achieved a Best Cost of 7600 in 4 seconds, and Genetic Algorithm (GA), which achieved a Best Fitness of 8500 in 5 seconds.
Co-Authors Ichwani, Arief Nizirwan Anwar Widodo, Agung Mulyo