Simulation-based metaheuristic optimization algorithm for material handling

Modern technologies and the emergent Industry 4.0 paradigm have empowered the emergence of flexible production systems suitable to cope with custom product demands, typical in this era of competitive marketplaces. However, production flexibility claims periodic changes in the setup of production fac...

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Vydáno v:Journal of intelligent manufacturing Ročník 36; číslo 3; s. 1689 - 1709
Hlavní autoři: Saavedra Sueldo, Carolina, Perez Colo, Ivo, De Paula, Mariano, Villar, Sebastián A., Acosta, Gerardo G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2025
Springer Nature B.V
Témata:
Algorithms
Artificial intelligence
Business and Management
Continuous improvement
Control
Discrete event systems
Flexibility
Heuristic methods
Industry 4.0
Machine learning
Machines
Manufacturing
Manufacturing industry
Materials handling
Mechatronics
Optimization
Optimization algorithms
Plant layout
Processes
Production
Reconfiguration
Robotics
Simulation
Trigonometric functions
Simulation
Material Handling
Artificial Intelligence
Optimization
Lean 4.0
ISSN:0956-5515, 1572-8145
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Shrnutí:Modern technologies and the emergent Industry 4.0 paradigm have empowered the emergence of flexible production systems suitable to cope with custom product demands, typical in this era of competitive marketplaces. However, production flexibility claims periodic changes in the setup of production facilities. The level of flexibility of a production process increases as the reconfiguration capacity of its facilities increases. Nevertheless, doing that efficiently requires accurate coordination between productive resources, task planning, and decision-making systems aiming to maximize value for the client, minimizing non-added-value production tasks, and continuous process improvement. In a manufacturing system, material handling within manufacturing facilities is one of the major non-value-added tasks strongly affected by changes in plant floor layouts and demands for producing customized products. This work proposes a metaheuristic simulation-based optimization methodology to address the material handling problem in dynamic environments. Our proposed approach integrates optimization, discrete event simulation, and artificial intelligence methods. Our proposed optimization algorithm is mainly based on the ideas of the novel population-based optimization algorithm called Q-learning embedded Sine Cosine Algorithm, inspired by the Sine Cosine Algorithm. Unlike those, our proposed approach can deal with discrete optimization problems. It includes in its formulation a reinforcement learning embedded algorithm for the self-learning of the parameters of the metaheuristic optimization algorithm, and discrete event simulation is used for simulating the shop floor operations. The performance of the proposed approach is evaluated through an exhaustive analysis of simple to complex cases. In addition, a comparison is made with other comparable optimization methodologies.
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-024-02327-0