International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 853KB)
AFSO-JSSP: Artificial Fish Swarm Optimization for Efficient Job Shop Scheduling
PDF (853KB), PP.105-118
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Author(s)
Shunmuga Priya Subramanian
1
Muthiah Athi
2
Pitchipoo Pandian
2,*
Rajakarunakaran Sivaprakasam
1
Index Terms
Scheduling, JSSP, Artificial Fish Swarm Optimization, Multi-Objective Optimization
Abstract
Job Shop Scheduling Problem (JSSP) has become one of the key issues in a contemporary manufacturing system in which the task is to optimally schedule jobs to the machines to reduce the time and resources used in production. Good scheduling is critical in enhancing the productivity and competitiveness of manufacturing industries. In this research, Artificial Fish Swarm Optimization (AFSO) algorithm is used to optimize the JSSP in minimizing makespan, total work load and maximum work load in machines. The AFSO strategy models the swarm behaviour of fishes to search and forage the search space in an efficient manner to prevent its early convergence to local optima. The model incorporates a disturbed state in the world to improve the direction in search and the speed of convergence. The effectiveness of the suggested AFSO method is compared and tested with the traditional and sophisticated optimization algorithms like Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) methods. The experimental findings prove that the offered technique provides better results in convergence rate and solution quality. The results prove that AFSO is a useful and promising method of solving complex problems in production system scheduling.
Cite This Paper
Shunmuga Priya Subramanian, Muthiah Athi, Pitchipoo Pandian, Rajakarunakaran Sivaprakasam, "AFSO-JSSP: Artificial Fish Swarm Optimization for Efficient Job Shop Scheduling", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.4, pp. 105-118, 2026. DOI:10.5815/ijieeb.2026.04.07
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