Artificial intelligence in engineering risk analytics

Wu, Dandan; Olson, David L.; Dolgui, Alexandre

doi:10.1016/j.engappai.2017.09.001

Cited by 14 publications

(2 citation statements)

References 26 publications

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“…At that time, Wu and Birge (2016) vetted risk-analysing tools caring about big data. Wu, Olson, and Dolgui (2017) pointed to the viability of artificial intelligence in risk management. Nahas (2017) studied a series manufacturing system containing n machines and n-1 buffers to identify optimal level of buffers and optimum preventive maintenance time.…”

Section: Failure-prone Manufacturing Systemsmentioning

confidence: 99%

Multi‐objective optimization for stochastic failure‐prone job shop scheduling problem via hybrid of NSGA‐II and simulation method

et al. 2019

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Production scheduling and reliability of machinery are prominent issues in flexible manufacturing systems that are led to decreasing of production costs and increasing of system efficiency. In this paper, multiobjective optimization of stochastic failureprone job shop scheduling problem is sought wherein that job processing time seems to be controllable. It endeavours to determine the best sequence of jobs, optimal production rate, and optimum preventive maintenance period for simultaneous optimization of three criteria of sum of earliness and tardiness, system reliability, and energy consumption. First, a new mixed integer programming model is proposed to formulate the problem. Then, by combining of simulation and NSGA-II algorithm, a new algorithm is put forward for solving the problem. A set of Pareto optimal solutions is achieved through this algorithm. The stochastic failure-prone job shop with controllable processing times has not been investigated in the earlier research, and for the first time, a new hedging point policy is presented. The computational results reveal that the proposed metaheuristic algorithm converges into optimal or near-optimal solution. To end, results and managerial insights for the problem are presented. KEYWORDScontrollable processing times, failure-prone manufacturing system, modified hedging point policy, Pareto optimal solutions, stochastic job shop scheduling | INTRODUCTIONFailure-prone manufacturing systems (FPMSs) can be defined as a subcategory of flexible systems with stochastic breakdown and maintenance of machines. Indeed, the major goal is to identify the production rate; accordingly, holding, shortage, and maintenance costs could be reduced in a long-run planning horizon. FPMSs are models for studying manufacturing systems considering system ambiguities. Buffers in manufacturing systems play the role of decreased impact of machine breakdown on meeting demand. Thus, it seems indispensable to identify the optimal level of buffers to reduce holding and shortage costs. Seeing as machines in flexible manufacturing systems can produce with various speeds, identifying the optimal speed is particularly significant and is defined as the hedging point policy (HPP) in FPMS. Job shop problem is an NP-hard problem in terms of complexity (Garey, Tarjan, & Wilfong, 1988). Given that, the stochastic job shop problem is an NP-hard problem, as well, considering machine breakdown and variable processing speed.

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Section: Failure-prone Manufacturing Systemsmentioning

confidence: 99%

Multi‐objective optimization for stochastic failure‐prone job shop scheduling problem via hybrid of NSGA‐II and simulation method

et al. 2019

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“…[1][2][3] As these industries often experience various abnormal events (i.e., faults), diagnostic tools are required to consider safety and assess risk. 4,5 The anomalous behavior of even one operating property or variable of the process is known as a fault. 6 Gas hydrates formation is the most critical fault in gas and multiphase systems.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy

Khandakar

2022

Energy Science & Engineering

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Artificial neural networks (ANNs) can understand the behavior of a given system from the historical measurements of its associated variables. Adjusting the weight and bias of the ANN model using an optimization algorithm is known as the training process. The ANN reliability is directly related to the success of the training process. Therefore, this study investigates the effect of optimization algorithms on the prediction accuracy of the multilayer perceptron neural networks (MLPNNs). The complex gas hydrate prevention unit is simulated using the MLPNN model trained by 20 different optimization algorithms. This study investigates the gradient‐based, evolutionary, and Bayesian‐based optimization algorithms. Combining statistical and ranking analyses confirms that the Levenberg–Marquardt (LM) is the most efficient optimization technique for training the MLPNN model. This training algorithm adjusts the weight and bis parameters of the MLPNN so that the highest accurate predictions have been achieved. On the other hand, the trained MLPNN by imperialist competitive algorithm shows the lowest accuracy for the considered task. The trained MLPNN by the LM algorithm predicts 239 laboratory‐measured data sets about the methanol (MeOH) loss with the absolute average relative deviation of 6.4% and regression coefficient of 0.9643. Coupling the developed MLPNN and differential evolution optimization algorithm shows that temperature = 263 K and pressure = 6.92 MPa are the optimum condition for minimizing the MeOH loss in the gas hydrate prevention unit. Economic analysis confirms that the annual cost of methanol loss for the daily processing of 100 × 106 m3 of gas is ~17 million US dollars.

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Development of Models and Algorithms for Intellectual Support of Life Cycle of Chemical Production Equipment

Moshev

Мешалкин

Ромашкин

2019

Studies in Systems, Decision and Control

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Artificial intelligence in engineering risk analytics

Cited by 14 publications

References 26 publications

Multi‐objective optimization for stochastic failure‐prone job shop scheduling problem via hybrid of NSGA‐II and simulation method

Multi‐objective optimization for stochastic failure‐prone job shop scheduling problem via hybrid of NSGA‐II and simulation method

Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy

Development of Models and Algorithms for Intellectual Support of Life Cycle of Chemical Production Equipment

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