When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global s...When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.展开更多
In this paper, the problem of maintenance task scheduling is tackled with a twofold objective: meeting the performance criteria of a company and taking into account some operators’ requirements. The production manage...In this paper, the problem of maintenance task scheduling is tackled with a twofold objective: meeting the performance criteria of a company and taking into account some operators’ requirements. The production manager makes sure that makespan is optimised while developing operators’ flexibility. The use of skill matrixes enables him to make pairs and to develop training in order to make trainees more autonomous. Operators’ requirements are in particular related to periods of unavailability and their wishes relating to their tasks. Given the complexity of the problem, an exact solution isn’t conceivable and our research focuses on a metaheuritic method giving us a solution that is considered satisfactory. A multi-criteria analysis of the results is performed in order to reach a compromise among conflicting goals.展开更多
In this paper, a manufacturing supply chain system composed by a single-product machine, a buffer and a stochastic demand is considered. A stochastic fluid model is adopted to describe the system and to take into acco...In this paper, a manufacturing supply chain system composed by a single-product machine, a buffer and a stochastic demand is considered. A stochastic fluid model is adopted to describe the system and to take into account stochastic delivery times. The objective of this paper is to evaluate the optimal buffer level used in hedging point policy taken into account planned delivery times, machine failures and random demands. This optimal buffer allows minimizing the sum of inventory, transportation, lost sales and late delivery costs. Infinitesimal perturbation analysis method is used for optimizing the proposed system. Using the stochastic fluid model, the trajectories of buffer level are studied and the infinitesimal perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm, which determines the optimal buffer level in the presence of planned delivery time. Also in this work, we discuss the advantage of the use of the infinitesimal perturbation analysis method comparing to classical simulation methods.展开更多
Due to the fourth revolution experiencing,referred to as Industry 4.0,many production firms are devoted to integrating new technological tools to their manufacturing process.One of them,is rescheduling the tasks on th...Due to the fourth revolution experiencing,referred to as Industry 4.0,many production firms are devoted to integrating new technological tools to their manufacturing process.One of them,is rescheduling the tasks on the machines responding to disruptions.While,for static scheduling,the efficiency criteria measure the performance of scheduling systems,in dynamic environments,the stability criteria are also used to assess the impact of jobs deviation.In this paper,a new performance measure is investigated for a flowshop rescheduling problem.This one considers simultaneously the total weighted waiting time as the efficiency criterion,and the total weighted completion time deviation as the stability criterion.This fusion could be a very helpful and significant measure for real life industrial systems.Two disruption types are considered:jobs arrival and jobs cancellation.Thus,a Mixed Integer Linear Programming(MILP)model is developed,as well as an iterative predictive-reactive strategy for dealing with the online part.At last,two heuristic methods are proposed and discussed,in terms of solution quality and computing time.展开更多
In several industrial fields like air transport,energy industry and military domain,maintenance actions are carried out during downtimes in order to maintain the reliability and availability of production system.In su...In several industrial fields like air transport,energy industry and military domain,maintenance actions are carried out during downtimes in order to maintain the reliability and availability of production system.In such a circumstance,selective maintenance strategy is considered the reliable solution for selecting the faulty components to achieve the next mission without stopping.In this paper,a novel multi-level decision making approach based on data mining techniques is investigated to determine an optimal selective maintenance scheduling.At the first-level,the age acceleration factor and its impact on the component nominal age are used to establish the local failures.This first decision making employed K-means clustering algorithm that exploited the historical maintenance actions.Based on the first-level intervention plan,the remaining-levels identify the stochastic dependence among components by relying upon Apriori association rules algorithm,which allows to discover of the failure occurrence order.In addition,at each decision making level,an optimization model combined to a set of exclusion rules are called to supply the optimal selective maintenance plan within a reasonable time,minimizing the total maintenance cost under a required reliability threshold.To illustrate the robustness of the proposed strategy,numerical examples and a FMS real study case have been solved.展开更多
文摘When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.
文摘In this paper, the problem of maintenance task scheduling is tackled with a twofold objective: meeting the performance criteria of a company and taking into account some operators’ requirements. The production manager makes sure that makespan is optimised while developing operators’ flexibility. The use of skill matrixes enables him to make pairs and to develop training in order to make trainees more autonomous. Operators’ requirements are in particular related to periods of unavailability and their wishes relating to their tasks. Given the complexity of the problem, an exact solution isn’t conceivable and our research focuses on a metaheuritic method giving us a solution that is considered satisfactory. A multi-criteria analysis of the results is performed in order to reach a compromise among conflicting goals.
文摘In this paper, a manufacturing supply chain system composed by a single-product machine, a buffer and a stochastic demand is considered. A stochastic fluid model is adopted to describe the system and to take into account stochastic delivery times. The objective of this paper is to evaluate the optimal buffer level used in hedging point policy taken into account planned delivery times, machine failures and random demands. This optimal buffer allows minimizing the sum of inventory, transportation, lost sales and late delivery costs. Infinitesimal perturbation analysis method is used for optimizing the proposed system. Using the stochastic fluid model, the trajectories of buffer level are studied and the infinitesimal perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm, which determines the optimal buffer level in the presence of planned delivery time. Also in this work, we discuss the advantage of the use of the infinitesimal perturbation analysis method comparing to classical simulation methods.
文摘Due to the fourth revolution experiencing,referred to as Industry 4.0,many production firms are devoted to integrating new technological tools to their manufacturing process.One of them,is rescheduling the tasks on the machines responding to disruptions.While,for static scheduling,the efficiency criteria measure the performance of scheduling systems,in dynamic environments,the stability criteria are also used to assess the impact of jobs deviation.In this paper,a new performance measure is investigated for a flowshop rescheduling problem.This one considers simultaneously the total weighted waiting time as the efficiency criterion,and the total weighted completion time deviation as the stability criterion.This fusion could be a very helpful and significant measure for real life industrial systems.Two disruption types are considered:jobs arrival and jobs cancellation.Thus,a Mixed Integer Linear Programming(MILP)model is developed,as well as an iterative predictive-reactive strategy for dealing with the online part.At last,two heuristic methods are proposed and discussed,in terms of solution quality and computing time.
文摘In several industrial fields like air transport,energy industry and military domain,maintenance actions are carried out during downtimes in order to maintain the reliability and availability of production system.In such a circumstance,selective maintenance strategy is considered the reliable solution for selecting the faulty components to achieve the next mission without stopping.In this paper,a novel multi-level decision making approach based on data mining techniques is investigated to determine an optimal selective maintenance scheduling.At the first-level,the age acceleration factor and its impact on the component nominal age are used to establish the local failures.This first decision making employed K-means clustering algorithm that exploited the historical maintenance actions.Based on the first-level intervention plan,the remaining-levels identify the stochastic dependence among components by relying upon Apriori association rules algorithm,which allows to discover of the failure occurrence order.In addition,at each decision making level,an optimization model combined to a set of exclusion rules are called to supply the optimal selective maintenance plan within a reasonable time,minimizing the total maintenance cost under a required reliability threshold.To illustrate the robustness of the proposed strategy,numerical examples and a FMS real study case have been solved.
