A Deterministic Flowshop Scheduling Problem to minimizing the Makespan using PA-ACO
Annu Priya1, Sudip Kumar Sahana2

1Annu Priya*, Department of Computer Science Engineering, Birla Institute of Technology, Jharkhand (Ranchi), India.
2Sudip Kumar Sahana, Assistant Professor, Department of Computer Science and Engineering, Birla Institute of Technology, Jharkhand (Ranchi), India.
Manuscript received on February 06, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on February 30, 2020. | PP: 1555-1560 | Volume-9 Issue-3, February, 2020. | Retrieval Number: B4573129219/2020©BEIESP | DOI: 10.35940/ijeat.B4573.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Scheduling problems are NP-hard in nature. Flowshop scheduling problems, are consist of sets of machines with number of resources. It matins the continuous flow of task with minimum time. There are various traditional algorithms to maintain the order of resources. Here, in this paper a new stochastic Ant Colony optimization technique based on Pareto optimal (PA-ACO) is implemented for solving the permutation flowshop scheduling problem (PFSP) sets. The proposed technique is employed with a novel local path search technique for initializing and pheromone trails. Pareto optimal mechanism is used to select the best optimal path solution form generated solution sets. A comparative study of the results obtained from simulations shows that the proposed PA-ACO provides minimum makespan and computational time for the Taillard dataset. This work will applied on large scale manufacturing production problem for efficient energy utilization.
Keywords: Permutation Flowshop Scheduling Problem (PFSP), probability of Correct Selection (PS), High-performance computing (HPC)