Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.Th...Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings.展开更多
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location al...To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.展开更多
基金supported by the National Natural Science Foundation of China(71871219).
文摘Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings.
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
基金National Natural Science Foundation of China(62202477)。
文摘To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.
