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A Data-Driven Multi-Objective optimization framework for dynamic job shop scheduling with order Acceptance, inventory and Energy-Aware decisions
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AI
DOI：10.1016/j.cie.2026.111886.png)
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Abstract
En 中文
• Integrated Multi-Objective DJSS Model: Novel model jointly optimizes profit, order acceptance, and energy under real-world constraints. • AI-Enhanced Decision Framework: Uses neural networks for adaptive order acceptance and material purchasing decisions. • Hybrid Metaheuristic Optimization: Hierarchical method combines multi-objective GA with data mining. • Real-World Case Validation: Model validated in a stone paper factory, outperforming benchmarks. • Improved Sustainability & Satisfaction: Balances energy efficiency with customer loyalty via order acceptance.
Keywords:
Multi-Objective Optimization
Dynamic Job Shop Scheduling
Order Acceptance
Energy-Aware Decisions
Data-Driven Framework
Journal
C
IF:
6.5
Papers:
471
Citations:
0