Journal Sensi: Strategic of Education in Information System
Vol 11 No 1 (2025): Journal SENSI

Optimizing Workforce Scheduling Using Ant Colony Optimization Algorithm: Case Study PT. Cloud Hosting Indonesia

Nurfilael, Gagas Nurfilae (Unknown)
Widodo, Agung Mulyo (Unknown)
Anwar, Nizirwan (Unknown)
Ichwani, Arief (Unknown)



Article Info

Publish Date
28 Feb 2025

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.

Copyrights © 2025






Journal Info

Abbrev

sensi

Publisher

Subject

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Description

Riset Soft Computing dengan penelitian dari yang berfokus pada Data Mining, Neural Network, Swarm Intelligence, Decision Tree, Data Clustering, Data Classification, Rough Set, Pattern Recognition, Image Processing. Software Engineering yang fokus pada software Requirement and Specification, Software ...