# Scheduling Job Machines with Swap Sequence to Minimize Makespan Using Spider Monkey Optimization Algorithm
> Asnan Cirua A.A.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_85150476138
Jurnal / Konferensi: Proceeding 6th International Conference on Information Technology Information Systems and Electrical Engineering Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability Icitisee 2022
Tahun terbit: 2022
DOI: https://doi.org/10.1109/ICITISEE57756.2022.10057926
Citations: 1

## Authors
- Asnan Cirua A.A.

## Abstract
Job scheduling is an NP-Hard combinatorial problem, so it requires optimization techniques to solve the problem. Several studies were conducted using various heuristic and meta-heuristic techniques. This paper presents the spider monkey optimization (SMO) algorithm, a newcomer algorithm in Swarm Intelligence. We added the swap sequence technique in the solution search process. Swap rate parameter testing was carried out at 0.2, 0.5, and 0.8; the results show that a swap rate of 0.2 can provide a smaller makespan but increases computation time. We analyze against the JSPLIB dataset test, which indicates the feasibility of the proposed algorithm. A comparison was made of the SMO, GA, HGA, and MCSA algorithms, and the results showed that MCSA was still superior for the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10\mathrm{x}10$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10\mathrm{x}5$</tex> datasets. The results of the 15×5 and 20×5 datasets show that SMO tends to be superior to GA and HGA.

## Keywords
- Job shop scheduling
- Swap (finance)
- Computer science
- Swarm intelligence
- Scheduling (production processes)
- Algorithm
- Computation
- Mathematical optimization
- Mathematics
- Particle swarm optimization
- Operating system
- Finance
- Economics
- Schedule

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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