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Network-Aware Smart Scheduling for Semi-Automated Ceramic Production via Improved Discrete Hippopotamus Optimization

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Publicado en:Electronics vol. 14, no. 17 (2025), p. 3543-3573
Autor principal: Zhang, Qi
Otros Autores: Zhang Changtian, Yao, Man, Guo Xiwang, Qin Shujin, Zhu, Haibin, Liang, Qi, Hu, Bin
Publicado:
MDPI AG
Materias:
China
Linear programming
Integer programming
Decision making
Production scheduling
Kilns
Optimization
Algorithms
Mixed integer
Automation
Manufacturing
Constraints
Heuristic methods
Batch processing
Acceso en línea:Citation/Abstract
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Resumen:The increasing integration of automation and intelligent sensing technologies in daily-use ceramic manufacturing poses new challenges for efficient scheduling under hybrid flow-shop and shared-kiln constraints. To address these challenges, this study proposes a Mixed-Integer Linear Programming (MILP) model and an Improved Discrete Hippopotamus Optimization (IDHO) algorithm designed for smart, network-aware production environments. The MILP formulation captures key practical features such as batch processing, no-idle kiln constraints, and machine re-entry dynamics. The IDHO algorithm enhances global search performance via segment-based encoding, nonlinear population reduction, and operation-specific mutation strategies, while a parallel evaluation framework accelerates computational efficiency, making the solution viable for industrial-scale, time-sensitive scenarios. The experimental results from 12 benchmark cases demonstrate that IDHO achieves superior performance over six representative metaheuristics (e.g., PSO, GWO, Jaya, DBO), with an average ARPD of 1.04%, statistically significant improvements (p < 0.05), and large effect sizes (Cohen’s d > 0.8). Compared to the commercial solver CPLEX, IDHO provides near-optimal results with substantially lower runtime. The proposed approach contributes to the development of intelligent networked scheduling systems for cyber-physical manufacturing environments, enabling responsive, scalable, and data-driven optimization in smart sensing-enabled production settings.
ISSN:2079-9292
DOI:10.3390/electronics14173543
Fuente:Advanced Technologies & Aerospace Database