Data-driven automated job shop scheduling optimization considering AGV obstacle avoidance

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 2, 2025

The production stage of an automated job shop is closely linked to the guided vehicle (AGV), which needs be planned in integrated manner achieve overall optimization. In order improve collaboration between stages and AGV operation system, a two-layer scheduling optimization model proposed for simultaneous decision making batching problems, sequences obstacle avoidance. Under automatic path seeking mode, this paper adopts data-driven Bayesian network method portray transportation time AGVs based on historical data control uncertainty AGVs. Meanwhile, window established risk delay, constructed optimize AGV. To solve model, we design improved particle swarm algorithm combining genetic operators, crossover operators elite retention operator. results show that can effectively system within floor, successfully actual scale case enhance effectiveness system.

Language: Английский

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Greedy mechanism-based bi-objective optimization for green scheduling in manufacturing systems considering transportation

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113093 - 113093

Published: March 1, 2025

Language: Английский

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Optimization of machine configuration and scheduling in the hybrid flow shop using a linear programming-driven evolutionary approach

Robotics and Computer-Integrated Manufacturing, Journal Year: 2025, Volume and Issue: 95, P. 103029 - 103029

Published: April 21, 2025

Language: Английский

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Deep learning model for optimizing control and planning in stochastic manufacturing environments

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 257, P. 125075 - 125075

Published: Aug. 12, 2024

Language: Английский

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Artificial bee colony algorithm based on multi-neighbor guidance

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 259, P. 125283 - 125283

Published: Sept. 6, 2024

Language: Английский

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Dynamic payment on microtasking platforms using bee colony optimization

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124862 - 124862

Published: July 22, 2024

Microtasking involves breaking down a job into smaller tasks and assigning them to group of workers who voluntarily complete the receive payment. However, joint estimation quality determination payment remains an underexplored area in microtask crowdsourcing research. This paper addresses limitation unestimated on microtasking platforms by introducing dynamic worker algorithm known as Dynamic Quality Payment (DQP). Utilizing Bee Colony Optimization (BCO) algorithm, proposed approach integrates determination. DQP incorporates Gaussian Process Model (GPM) initially estimate then optimize payments based work. Empirical testing is conducted using two datasets from Amazon Mechanical Turk. Additionally, compared against novel algorithms, demonstrating superior performance. The experimental results illustrate that not only reduces cost but also accurately estimates quality.

Language: Английский

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Integrated diagnosis optimization design of the electronic equipment based on spatial mapping

Science Progress, Journal Year: 2024, Volume and Issue: 107(4)

Published: Oct. 1, 2024

The complexity of test and fault information within electronic devices makes their integrated diagnosis a challenging problem when designing equipment reliability. Current is analyzed for optimization resource optimization. However, this neglects the connection between them. This paper proposes design strategy based on spatial mapping principle to quantitatively describe constraint relationship model established by constructing logical space, optimal configuration are sought grey wolf algorithm. Seven high-dimensional benchmark functions an used verify efficiency algorithm proposed in paper. compared with other four terms algorithm’s speed accuracy. results indicate that after has critical detection, isolation, false alarm rates 100%, 99.99%, 98.99%, 0.2993%, respectively. After optimization, number tests reduced 88.9%, cost saved 89%. Compared algorithms, achieves best results, reduces 42%–55%, decreases 77.63%–83.91%. not only considers resources but also dramatically while improving efficiency.

Language: Английский

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Optimization of task assignment for multi-farm multi-weeding robots based on discrete artificial bee colony algorithm

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 126182 - 126182

Published: Dec. 1, 2024

Language: Английский

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