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Parameter Tuning of A Binary Pareto Whale Optimization Algorithm Using Taguchi Grey Relational Analysis

Haleel Aseel Jameel, Dawood Lamyaa Mohammed

Management Systems in Production Engineering

2025

nr 1 (33)

93--99

Parameter values of any metaheuristic algorithm affect the performance of the algorithm search. However, using statistics to estimate the proper values for the algorithm’s parameters will be feasible to make optimization algorithm more robust and effective. The aim of this paper is to investigate the optimal control parameter values for the Binary Pareto Whale Optimization Algorithm BPWOA, which is used for solving maintenance scheduling problem at a power plant. Three algorithm control parameters involving population size, iteration number, and archive size were fine-tuned using Taguchi-Grey Relational Analysis GRA to achieve an optimal maintenance schedule with maximum power supply, minimum fuel expense, and minimum Carbone Dioxide CO2 emissions. The algorithm runs carried out based on Taguchi experiment design using L25 orthogonal array. The Grey Relational Grade GRG metric is utilized to evaluate the BPWOA performance. The results show that the Taguchi-Grey relational analysis approach is a dependable and efficient way to generate new optimal values for the BPWOA control parameters, allowing for multi-objective power plant maintenance scheduling with fewer runs in less time and a 20% improvement in GRG of objectives.

Performance Evaluation of a Production Control Architectures for Flexible Manufacturing System

Ismayir Dhuha Kadhim, Dawood Lamyaa Mohammed, Al-Khafaji Muhannad M.H.

Advances in Science and Technology. Research Journal

2024

Vol. 18, no. 3

175--187

The study offers an analytical approach for assessing production control system performance in terms of volume and variety features of the product being manufactured for the Flexible Manufacturing System. Throughput, resource utilisation, cycle time, and maximum completion time are the four performance indicators taken into account. The objective is to quantify the production system's performance and define the reactive capability of production control architectures in a market that is becoming increasingly competitive. The examination of control systems' production performances motivates the evaluation of these architectures through the introduction of scheduling approaches that deal with uncertainty. Results revealed the semi-heterarchical control architecture outperforms the hierarchical control architecture through multiple performance criteria. A case study with regard to a manufacturing control system is presented in order to highlight the significance of our methodology and the contribution of the research.