Identifying Appropriate Job and Machine Sequence Through Artificial Fish Swarm Optimisation Technique

Shivashankreppa*, Prashant G. Kamble**, Ashok Vangeri***
* Department of Mechanical Engineering, Guru Nanak Dev Engineering College, Bidar, Karnataka, India.
** Department of Industrial and Production, Poojya Doddappa Appa College of Engineering, Kalburgi, Karnataka, India.
*** Shetty Institute of Technology, Kalaburagi, Karnataka, India.
Periodicity:August - October'2019


Scheduling plays a vital role in various industries especially in auto industries, where job and machine should be arranged in an appropriate sequence for effective outcome. To pursue this type of sequence manually takes a long time and is complex to compute. This urges to incorporate optimization techniques to predict the optimal job and machine sequence. This research incorporates five different sizes of Bench Mark (BM) Job Shop Scheduling Problems (JSSP). These problems intent to solve with the aid of Teaching–Learning Based Optimization (TLBO), Greedy Randomized Adaptive Search Procedure (GRASP), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Fish Swarm Optimization (AFSO). The investigation reveals the superiority of proposed AFSO over other comparative techniques in all performance evaluations.


Job Shop Scheduling Problems (JSSP), Makespan Time, Auto Industries, Artificial Fish Swarm Optimization (AFSO).

How to Cite this Article?

Shivashankreppa, Kamble, P. G., and Vangeri, A. (2019). Identifying Appropriate Job and Machine Sequence Through Artificial Fish Swarm Optimisation Technique i-manager’s Journal on Mechanical Engineering, 9(4), 38-46.


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