Implementing Sensible Algorithmic Decisions in Manufacturing
Source abstract
Abstract
A significant component of making intelligent decisions is optimizing algorithms. In this context, it is imperative to develop algorithms that are more efficient in order to efficiently and accurately process large quantities of intricate data. In addition, the main contribution of this study lies in the integration of optimization theory with swarm intelligence through multicriteria decision-making methods (MCDMs). This study indicates that combining dimensional analysis (DA) with particle swarm optimization (PSO) can smartly and efficiently improve analysis and decision making, resolving PSO’s shortcomings. A convergence investigation between the bat algorithm (BA), MOORA-PSO, TOPSIS-PSO, DA-PSO, and PSO is carried out to substantiate this assertion. Additionally, the ANOVA method is used to validate data dependability in order to evaluate the algorithms’ correctness.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization · Scheduling and Optimization Algorithms · Advanced Manufacturing and Logistics Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Av. Plutarco Elías Calles #1210 Fovissste Chamizal, Ciudad Juárez, Chihuahua C.P. 32310, Mexico
Tecnologías de la Información e Innovación Digital, Universidad Tecnológica de Chihuahua, Av. Montes Americanos #9501 Col. Sector 35, Ciudad Chihuahua, Chihuahua C.P. 31216, Mexico
Departamento de Ingeniaría Eléctrica y Computación, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Av. Plutarco Elías Calles #1210 Fovissste Chamizal, Ciudad Juárez, Chihuahua C.P. 32310, Mexico
Funding
Consejo Nacional de Ciencia y Tecnología (CONACYT) · Universidad Autónoma de Ciudad Juárez
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