Simulation Reduction Models Approach Using Neural Network

Thomas, Philippe; Choffel, Denise; Thomas, André

doi:10.1109/uksim.2008.73

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…The selected studies exploring the economic profits of optimal decision-making processes can be generally divided into three groups: production efficiency improvement [16], [18], [19], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [20], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], energy cost savings [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], and space savings [53], [56], [82], [83], [84], [85], [86],…”

Section: ) Economic Profitsmentioning

confidence: 99%

“…Environmental footprint reduction [16], [18], [19], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [20], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70] [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81] [53], [56], [82], [83], [84], [85], [86], [87] [99], [100], [101], [102],…”

Section: Manufacturing Waste Managementmentioning

confidence: 99%

“…This work uses some problem-specific metrics and traditional machine learning metrics to evaluate the fitted metamodels which can compliment the sawing simulator. By incorporating machine learning within simulators, studies in [69] and [70] propose the use of neural network models to simplify and accelerate the simulation of sawmill operation planning problems. Based on the results presented by these studies, the proposed integration strategies can increase efficiency and reduce computational complexity of the decision-making simulations.…”

Section: Artificial Intelligence Approachesmentioning

confidence: 99%

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Wood Products Manufacturing Optimization: A Survey

Hosseini

Peer

2022

IEEE Access

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The wood industry is a basic industry supplying primary materials to produce a wide range of products. The wood processing in sawmills bridges the transformation flow of raw materials to final products using machinery. Over the last decade, the global market for wood products has become highly competitive. As a result, sawmills are continually striving to improve their efficiency throughout their production process. Recent advances in automation systems and manufacturing technologies provide traditional sawmills with an unprecedented opportunity to be transformed into automated manufacturing plants to increase their efficiency. This opportunity motivates sawmill planners to adopt decision optimization technologies for enhancing their production yields. This transformation will support sawmills to remain resilient in current and future competitive markets. Although there is a vast amount of literature on decision-making in wood manufacturing in sawmills, a comprehensive overview of related approaches and findings with a special focus on optimization of decision processes is still lacking. The review firstly focuses on exploring opportunities provided by optimizing decision-making processes in the wood manufacturing along with associated roadblocks and challenges. Then, we explore methodologies adopted in the literature when aiming for the design and implementation of optimal decision-making systems. Finally, we provide references and guidelines for researchers and manufacturers interested in the topic, as well as formulate research gaps and recommendations for future research directions.INDEX TERMS autonomous production machines, automated wood processing, decision-making, decision support systems, optimization

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Section: ) Economic Profitsmentioning

confidence: 99%

Section: Manufacturing Waste Managementmentioning

confidence: 99%

Section: Artificial Intelligence Approachesmentioning

confidence: 99%

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Wood Products Manufacturing Optimization: A Survey

Hosseini

Peer

2022

IEEE Access

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“…The learning of the network is supervised, so it is necessary to divide the database into learning and validation data sets. Previous work [8,10] using the OBS algorithm has shown that 24 hidden neurons are sufficient for modeling the system, so the initial network structure uses 25 hidden neurons. The initial learning is therefore carried out with a neural network comprising 10 inputs, 25 hidden neurons, and one output (i.e., 301 parameters), for a maximum of 50,000 iterations.…”

Section: Database and Initialmentioning

confidence: 99%

“…Neural networks can extract performing models from experimental data [9]. Consequently, the use of neural networks has been proposed in order to reduce simulation models [8][9][10]. To build a neural model, an important issue is determining the structure of the network.…”

Section: Introductionmentioning

confidence: 99%

Variance Sensitivity Analysis of Parameters for Pruning of a Multilayer Perceptron: Application to a Sawmill Supply Chain Simulation Model

Thomas

Suhner

Thomas

2013

Advances in Artificial Neural Systems

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Simulation is a useful tool for the evaluation of a Master Production/Distribution Schedule (MPS). The goal of this paper is to propose a new approach to designing a simulation model by reducing its complexity. According to the theory of constraints, a reduced model is built using bottlenecks and a neural network exclusively. This paper focuses on one step of the network model design: determining the structure of the network. This task may be performed by using the constructive or pruning approaches. The main contribution of this paper is twofold; it first proposes a new pruning algorithm based on an analysis of the variance of the sensitivity of all parameters of the network and then uses this algorithm to reduce the simulation model of a sawmill supply chain. In the first step, the proposed pruning algorithm is tested with two simulation examples and compared with three classical pruning algorithms from the literature. In the second step, these four algorithms are used to determine the optimal structure of the network used for the complexity-reduction design procedure of the simulation model of a sawmill supply chain.

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Multilayer perceptron for simulation models reduction: Application to a sawmill workshop

Thomas¹,

Thomas²

2011

Engineering Applications of Artificial Intelligence

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Simulation is often used to evaluate supply chain or workshop management. This simulation task needs models, which are difficult to construct. The aim of this work is to reduce the complexity of a simulation model design. The proposed approach combines discrete and continuous approaches in order to construct speeder and simpler reduced model. The simulation model focuses on bottlenecks with a discrete approach according to the theory of constraints. The remaining of the workshop must be taken into account in order to describe how the bottlenecks are fed. It is modeled through a continuous approach thanks to a neural network. In particular, we use a multilayer perceptron. The structure of the network is determined by using a pruning procedure. For validation, this approach is applied to the modelisation of a sawmill workshop.

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Simulation Reduction Models Approach Using Neural Network

Abstract: Simulation is often used for the evaluation of a Master Production Schedule (MPS

Cited by 5 publications

References 19 publications

Wood Products Manufacturing Optimization: A Survey

Wood Products Manufacturing Optimization: A Survey

Variance Sensitivity Analysis of Parameters for Pruning of a Multilayer Perceptron: Application to a Sawmill Supply Chain Simulation Model

Multilayer perceptron for simulation models reduction: Application to a sawmill workshop

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