In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central t...In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station selection.Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness.The core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator dispatching.This method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple objectives.Rigorous empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations.The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost reduction.Additionally,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational effectiveness.This research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction situations.Furthermore,the methodologies and principles developed in this study have potential applications in various other sector展开更多
提出一种基于GA改进算法的兼顾经济性和可靠性的多目标机组检修计划UMS(unit maintenance schedu-ling)优化模型。模型分别采用研究周期内各时段的电量不足期望值EENS(energy expectation not served)之和与电力不足期望值LOLP(Loss of ...提出一种基于GA改进算法的兼顾经济性和可靠性的多目标机组检修计划UMS(unit maintenance schedu-ling)优化模型。模型分别采用研究周期内各时段的电量不足期望值EENS(energy expectation not served)之和与电力不足期望值LOLP(Loss of Load Expectation)之和为经济性目标和可靠性目标。采用改进排序适应函数法构造能够同时判断检修计划经济性和可靠性优劣的适应值函数。最后对一5机算例进行了分析,并与传统等风险度法的结果进行了比较。结果表明基于GA改进算法的多目标优化检修方案在经济性及系统可靠性方面均具有较好性能。展开更多
The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement co...The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users.This paper presents a state-of-the-art review of multi-objective optimization(MOO)problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities.First,the paper presents a taxonomy and hierarchy for these activities,the role of funding sources,and levels of jurisdiction.The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling(historical practices,expert opinion,and explicit mathematical optimization)and identifies the pros and cons of each mechanism.The paper then focuses on the optimization mechanism and presents the types of optimization problems,formulations,and objectives that have been used in the literature.Next,the paper examines various solution algorithms and discusses issues related to their implementation.Finally,the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities,and makes recommendations to overcome some of these barriers.展开更多
In a market environment of power systems, each producer pursues its maximal profit while the independent system operator is in charge of the system reliability and the minimization of the total generation cost when ge...In a market environment of power systems, each producer pursues its maximal profit while the independent system operator is in charge of the system reliability and the minimization of the total generation cost when generating the generation maintenance scheduling(GMS). Thus, the GMS is inherently a multi-objective optimization problem as its objectives usually conflict with each other. This paper proposes a multi-objective GMS model in a market environment which includes three types of objectives, i.e., each producer's profit, the system reliability, and the total generation cost. The GMS model has been solved by the group search optimizer with multiple producers(GSOMP) on two test systems. The simulation results show that the model is well solved by the GSOMP with a set of evenly distributed Pareto-optimal solutions obtained. The simulation results also illustrate that one producer's profit conflicts with another one's, that the total generation cost does not conflict with the profit of the producer possessing the cheapest units while the total generation cost conflicts with the other producers' profits, and that the reliability objective conflicts with the other objectives.展开更多
With the continuous expansion of power distribution grid, the number of distribution equipments has become larger and larger. In order to make sure that all the equipments can operate reliably, a large amount of maint...With the continuous expansion of power distribution grid, the number of distribution equipments has become larger and larger. In order to make sure that all the equipments can operate reliably, a large amount of maintenance tasks should be conducted. Therefore, maintenance scheduling of distribution network is an important content, which has significant influence on reliability and economy of distribution network operation. This paper proposes a new model for maintenance scheduling which considers load loss, grid active power loss and system risk as objective functions. On this basis, Differential Evolution algorithm is adopted to optimize equipment maintenance time and load transfer path. Finally, the general distribution network of 33 nodes is taken for example which shows the maintenance scheduling model’s effectiveness and validity.展开更多
基金support from the National Science and Technology Council of Taiwan(Contract Nos.112-2221-E-011-115 and 111-2622-E-011019)the support from Intelligent Manufacturing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei 10607,Taiwan,which is a Featured Areas Research Center in Higher Education Sprout Project of Ministry of Education(MOE),Taiwan(since 2023)was appreciated.
文摘In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station selection.Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness.The core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator dispatching.This method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple objectives.Rigorous empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations.The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost reduction.Additionally,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational effectiveness.This research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction situations.Furthermore,the methodologies and principles developed in this study have potential applications in various other sector
文摘提出一种基于GA改进算法的兼顾经济性和可靠性的多目标机组检修计划UMS(unit maintenance schedu-ling)优化模型。模型分别采用研究周期内各时段的电量不足期望值EENS(energy expectation not served)之和与电力不足期望值LOLP(Loss of Load Expectation)之和为经济性目标和可靠性目标。采用改进排序适应函数法构造能够同时判断检修计划经济性和可靠性优劣的适应值函数。最后对一5机算例进行了分析,并与传统等风险度法的结果进行了比较。结果表明基于GA改进算法的多目标优化检修方案在经济性及系统可靠性方面均具有较好性能。
基金This work is supported by the Next Generation Transportation Systems Center(NEXTRANS),USDOT's Region 5 University Transportation CenterThe work is also affiliated with Purdue University College of Engineering's Institute for Control,Optimization,and Networks(ICON)and Center for Intelligent Infrastructure(CII)initiatives.
文摘The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users.This paper presents a state-of-the-art review of multi-objective optimization(MOO)problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities.First,the paper presents a taxonomy and hierarchy for these activities,the role of funding sources,and levels of jurisdiction.The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling(historical practices,expert opinion,and explicit mathematical optimization)and identifies the pros and cons of each mechanism.The paper then focuses on the optimization mechanism and presents the types of optimization problems,formulations,and objectives that have been used in the literature.Next,the paper examines various solution algorithms and discusses issues related to their implementation.Finally,the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities,and makes recommendations to overcome some of these barriers.
基金Project supported by the National High-Tech R&D Program(863) of China(No.2011AA05A120)the National Basic Research Program(973) of China(No.2012CB215100)the Zhejiang Provincial Natural Science Foundation of China(No.LZ12E07002)
文摘In a market environment of power systems, each producer pursues its maximal profit while the independent system operator is in charge of the system reliability and the minimization of the total generation cost when generating the generation maintenance scheduling(GMS). Thus, the GMS is inherently a multi-objective optimization problem as its objectives usually conflict with each other. This paper proposes a multi-objective GMS model in a market environment which includes three types of objectives, i.e., each producer's profit, the system reliability, and the total generation cost. The GMS model has been solved by the group search optimizer with multiple producers(GSOMP) on two test systems. The simulation results show that the model is well solved by the GSOMP with a set of evenly distributed Pareto-optimal solutions obtained. The simulation results also illustrate that one producer's profit conflicts with another one's, that the total generation cost does not conflict with the profit of the producer possessing the cheapest units while the total generation cost conflicts with the other producers' profits, and that the reliability objective conflicts with the other objectives.
文摘With the continuous expansion of power distribution grid, the number of distribution equipments has become larger and larger. In order to make sure that all the equipments can operate reliably, a large amount of maintenance tasks should be conducted. Therefore, maintenance scheduling of distribution network is an important content, which has significant influence on reliability and economy of distribution network operation. This paper proposes a new model for maintenance scheduling which considers load loss, grid active power loss and system risk as objective functions. On this basis, Differential Evolution algorithm is adopted to optimize equipment maintenance time and load transfer path. Finally, the general distribution network of 33 nodes is taken for example which shows the maintenance scheduling model’s effectiveness and validity.
