Selected Application Tools for Creating Models in the Matlab Environment

Hrehová, Stella; Husár, Jozef

doi:10.1007/978-3-031-15101-9_13

Cited by 2 publications

(1 citation statement)

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“…The objective is to optimize the combination of supplying and manufacturing processes from the manufacturer to execute the orders in minimum time, minimum cost or both to achieve the customer goal. The three mathematical models and GA were implemented using MATLAB [30]. Two of these mathematical models used the single objective GA.…”

Section: Discussion and Resultsmentioning

confidence: 99%

AI-Driven Optimization Approach Based on Genetic Algorithm in Mass Customization Supplying and Manufacturing

Alfayoumi,

Eltazi,

Elgammal

2023

IJACSA

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Numerous artificial intelligence (AI) techniques are currently utilized to identify planning solutions for supply chains, which comprise suppliers, manufacturers, wholesalers, and customers. Continuous optimization of these chains is necessary to enhance their performance. Manufacturing is a critical stage within the supply chain that requires continuous optimization. Mass Customization Manufacturing is one such manufacturing type that involves high-volume production with a wide variety of materials. However, genetic algorithms have not been used to minimize both time and cost in the context of mass customization manufacturing. Therefore, we propose this study to present an artificial intelligence solution using genetic algorithm to build a model that minimizes the time and cost which associated with mass customized orders. Our problem formulation is based on a real-world case, and it adheres to expert descriptions. Our proposed optimization model incorporates two strategies to solve the optimization problem. The first strategy employs a single objective function focused on either time or cost, while the second strategy applies the multi-objective function NSGAII to optimize both time and cost simultaneously. The effectiveness of the proposed model was evaluated using a real case study, and the results demonstrated that leveraging genetic algorithms for mass customization optimization outperformed expert estimations in finding efficient solutions. On average, the evaluation revealed a 20.4% improvement for time optimization, a 29.8% improvement for cost optimization, and a 25.5% improvement for combined time and cost optimization compared to traditional expert optimization.

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Section: Discussion and Resultsmentioning

confidence: 99%