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A Quantitative Fitness Analysis Workflow

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Cost optimization for Computer Numerical Control machining workshop: A queueing modeling approach using the

Parmeet Kaur Chahal¹, Kamlesh Kumar¹

¹Department Of Mathematics, Central University of Jammu, Samba, Jammu & Kashmir, 181143, India.

ISA Transactions

|April 5, 2025

Summary

This summary is machine-generated.

This study optimizes Computer Numerical Control (CNC) machine repair using queueing theory, improving workshop efficiency and reducing costs. It models machine failures, server breakdowns, and repair policies for better operational management.

Keywords:

Ant Colony Optimization(ACO)Bernoulli’ s phase repairs Computer Numerical Control (CNC) machining workshop Control F-policy Feedback Flower Pollination Algorithm(FPA)Particle Swarm Optimization(PSO)Recovery policy (Q)Reneging

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Area of Science:

Operations Research
Manufacturing Engineering
Industrial Engineering

Background:

CNC machines are critical in manufacturing, and their failures disrupt production.
Existing repair models often lack comprehensive real-world scenario considerations.

Purpose of the Study:

To develop and optimize a queueing model for CNC machine repair in workshops.
To enhance efficiency and reduce operational costs in CNC machining environments.

Main Methods:

Queueing theory with F-policy arrivals and Bernoulli's p-phases recovery policy.
Matrix-analytic techniques for solving steady-state equations and deriving queue size distributions.
Meta-heuristic optimization algorithms: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Flower Pollination Algorithm (FPA).

Main Results:

Developed a queueing model incorporating CNC machine failures, robotic server breakdowns, reneging, and imperfect repairs.
Derived steady-state equations and analyzed system performance through numerical and graphical representations.
Comparative analysis of PSO, ACO, and FPA demonstrated their effectiveness in optimizing system costs.

Conclusions:

The proposed queueing model provides a robust framework for managing CNC machine repairs.
Meta-heuristic optimization significantly enhances cost-effectiveness and operational efficiency.
This integrated approach offers a comprehensive solution for improving CNC machining workshop performance.