# Scheduling and Optimization Algorithms

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/scheduling-and-optimization-algorithms/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on scheduling problems in manufacturing systems, particularly addressing issues such as setup times, batching, dynamic scheduling, energy efficiency, and multi-objective optimization. It explores various techniques including genetic algorithms, agent-based control, and hybrid optimization to improve scheduling efficiency and effectiveness in manufacturing processes. |
| Domain | Physical Sciences |
| Field | Engineering |
| OpenAlex ID | t10551 |
| Works | 82 |

## Topic papers all

Showing 15 of 82.

- [Optimization of global production scheduling with deep reinforcement learning](https://scholariq.org/papers/optimization-of-global-production-scheduling-with-deep-reinforcement-learning/)
- [A practical approach to job-shop scheduling problems](https://scholariq.org/papers/a-practical-approach-to-job-shop-scheduling-problems/)
- [Agent-based supply chain management—1: framework](https://scholariq.org/papers/agent-based-supply-chain-management-1-framework/)
- [Open Controller Architecture – Past, Present and Future](https://scholariq.org/papers/open-controller-architecture-past-present-and-future/)
- [PriMa: a prescriptive maintenance model for cyber-physical production systems](https://scholariq.org/papers/prima-a-prescriptive-maintenance-model-for-cyber-physical-production-systems/)
- [A new paradigm of cloud-based predictive maintenance for intelligent manufacturing](https://scholariq.org/papers/a-new-paradigm-of-cloud-based-predictive-maintenance-for-intelligent/)
- [Leveraging Technology for a Sustainable World](https://scholariq.org/papers/leveraging-technology-for-a-sustainable-world/)
- [The development of intelligent decision support tools to aid the design of flexible manufacturing systems](https://scholariq.org/papers/the-development-of-intelligent-decision-support-tools-to-aid-the-design-of/)
- [Manufacturing planning and control](https://scholariq.org/papers/manufacturing-planning-and-control/)
- [A Deep Reinforcement Learning Framework Based on an Attention Mechanism and Disjunctive Graph Embedding for the Job-Shop Scheduling Problem](https://scholariq.org/papers/a-deep-reinforcement-learning-framework-based-on-an-attention-mechanism-and/)
- [Resource-constrained project scheduling problem: review of past and recent developments](https://scholariq.org/papers/resource-constrained-project-scheduling-problem-review-of-past-and-recent/)
- [Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer](https://scholariq.org/papers/lead-time-prediction-using-machine-learning-algorithms-a-case-study-by-a/)
- [Decentralized decision support for intelligent manufacturing in Industry 4.0](https://scholariq.org/papers/decentralized-decision-support-for-intelligent-manufacturing-in-industry-4-0/)
- [Using data mining to find patterns in genetic algorithm solutions to a job shop schedule](https://scholariq.org/papers/using-data-mining-to-find-patterns-in-genetic-algorithm-solutions-to-a-job-shop/)
- [Agent-based supply chain management—2: a refinery application](https://scholariq.org/papers/agent-based-supply-chain-management-2-a-refinery-application/)

## Topic primary papers

Showing 15 of 36.

- [Optimization of global production scheduling with deep reinforcement learning](https://scholariq.org/papers/optimization-of-global-production-scheduling-with-deep-reinforcement-learning/)
- [A practical approach to job-shop scheduling problems](https://scholariq.org/papers/a-practical-approach-to-job-shop-scheduling-problems/)
- [Agent-based supply chain management—1: framework](https://scholariq.org/papers/agent-based-supply-chain-management-1-framework/)
- [Manufacturing planning and control](https://scholariq.org/papers/manufacturing-planning-and-control/)
- [A Deep Reinforcement Learning Framework Based on an Attention Mechanism and Disjunctive Graph Embedding for the Job-Shop Scheduling Problem](https://scholariq.org/papers/a-deep-reinforcement-learning-framework-based-on-an-attention-mechanism-and/)
- [Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer](https://scholariq.org/papers/lead-time-prediction-using-machine-learning-algorithms-a-case-study-by-a/)
- [Using data mining to find patterns in genetic algorithm solutions to a job shop schedule](https://scholariq.org/papers/using-data-mining-to-find-patterns-in-genetic-algorithm-solutions-to-a-job-shop/)
- [Deep reinforcement learning for semiconductor production scheduling](https://scholariq.org/papers/deep-reinforcement-learning-for-semiconductor-production-scheduling/)
- [A neural network job-shop scheduler](https://scholariq.org/papers/a-neural-network-job-shop-scheduler/)
- [Tabu search for the optimization of household energy consumption](https://scholariq.org/papers/tabu-search-for-the-optimization-of-household-energy-consumption/)
- [Integrated optimization of production planning and scheduling for a kind of job-shop](https://scholariq.org/papers/integrated-optimization-of-production-planning-and-scheduling-for-a-kind-of-job/)
- [A two-stage ant colony algorithm for hybrid flow shop scheduling with lot sizing and calendar constraints in printed circuit board assembly](https://scholariq.org/papers/a-two-stage-ant-colony-algorithm-for-hybrid-flow-shop-scheduling-with-lot-sizing/)
- [A dynamic programming method for single machine scheduling](https://scholariq.org/papers/a-dynamic-programming-method-for-single-machine-scheduling/)
- [Synchronisation for smart factory - towards IoT-enabled mechanisms](https://scholariq.org/papers/synchronisation-for-smart-factory-towards-iot-enabled-mechanisms/)
- [Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions](https://scholariq.org/papers/deep-reinforcement-learning-for-machine-scheduling-methodology-the-state-of-the-3/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.