The recycling and remanufacturing of end-of-life products are significant for environmental protection and resource conservation.Disassembly is an essential process of remanufacturing end-of-life products.Effective di...The recycling and remanufacturing of end-of-life products are significant for environmental protection and resource conservation.Disassembly is an essential process of remanufacturing end-of-life products.Effective disassembly plans help improve disassembly efficiency and reduce disassembly costs.This paper studies a disassembly planning problem with operation attributes,in which an integrated decision of the disassembly sequence,disassembly directions,and disassembly tools are made.Besides,a mathematical model is formulated with the objective of minimizing the penalty cost caused by the changing of operation attributes.Then,a neighborhood modularization-based artificial bee colony algorithm is developed,which contains a modular optimized design.Finally,two case studies with different scales and complexities are used to verify the performance of the proposed approach,and experimental results show that the proposed algorithm outperforms the two existing methods within an acceptable computational time.展开更多
The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispens...The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.展开更多
Order release is a key production planning and control function,specifically in high variety contexts.A large literature on release methods that balance the workload consequently emerged.These Workload Control methods...Order release is a key production planning and control function,specifically in high variety contexts.A large literature on release methods that balance the workload consequently emerged.These Workload Control methods can be rule based,using a simple greedy heuristic,optimization based or optimization based with lead times that are exogenous.Although all three types of methods have the same objective,their performance has never been compared.Using simulation,this study shows that a better on time delivery performance of jobs can be achieved by the two optimization based release methods.Most importantly,optimization based methods that assume lead times to be exogenous significantly outperform alternative methods in terms of tardiness performance.Rule based and optimization based Workload Control without exogenous lead times overemphasize average lateness reduction,which leads to sequence deviations that offset performance improvements through balancing.In contrast,Workload Control methods that assume lead times to be exogenous limit sequence deviations,which leads to a significant reduction in dispersion of lateness.This has important implication for the future design of order release methods,and managerial practice.展开更多
基金National Natural Science Foundation of China(Grant Nos.52205526,52205529)Basic and Applied Basic Research Project of the Guangzhou Basic Research Program of China(Grant No.202201010284)+6 种基金National Foreign Expert Project of the Ministry of Science and Technology of China(Grant No.G2021199026L)National Key Research and Development Program of China(Grant Nos.2021YFB3301701,2021YFB3301702)Guangdong Provincial Graduate Education Innovation Program of China(Grant No.82620516)Guangzhou Municipal Innovation Leading Team Project of China(Grant No.201909010006)Guangdong Provincial"Quality Engineering"Construction Project of China(Grant No.210308)Guangdong Provincial Basic and Applied Basic Research Foundation of China(Grant No.2019A1515110399)Fundamental Research Funds for the Central Universities of China(Grant No.21620360).
文摘The recycling and remanufacturing of end-of-life products are significant for environmental protection and resource conservation.Disassembly is an essential process of remanufacturing end-of-life products.Effective disassembly plans help improve disassembly efficiency and reduce disassembly costs.This paper studies a disassembly planning problem with operation attributes,in which an integrated decision of the disassembly sequence,disassembly directions,and disassembly tools are made.Besides,a mathematical model is formulated with the objective of minimizing the penalty cost caused by the changing of operation attributes.Then,a neighborhood modularization-based artificial bee colony algorithm is developed,which contains a modular optimized design.Finally,two case studies with different scales and complexities are used to verify the performance of the proposed approach,and experimental results show that the proposed algorithm outperforms the two existing methods within an acceptable computational time.
基金supported by the Guangdong-Macao Joint Funding Project(No. 2021A0505080015)Science and Technology Planning Project of Guangdong Province (No. 2019B010137006)Science and Technology Development Fund,Macao SAR (No. SKL-IOTSC(UM)-2021-2023)。
文摘The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.
基金supported by National Key Research and Development Program of China(2021YFB3301701)2019 Guangdong Special Support Talent Program Innovation and Entrepreneurship Leading Team(China)(2019BT02S593)+1 种基金2018 Guangzhou Leading Innovation Team Program(China)(201909010006)the Science and Technology Development Fund(Macao SAR)(0078/2021/A).
文摘Order release is a key production planning and control function,specifically in high variety contexts.A large literature on release methods that balance the workload consequently emerged.These Workload Control methods can be rule based,using a simple greedy heuristic,optimization based or optimization based with lead times that are exogenous.Although all three types of methods have the same objective,their performance has never been compared.Using simulation,this study shows that a better on time delivery performance of jobs can be achieved by the two optimization based release methods.Most importantly,optimization based methods that assume lead times to be exogenous significantly outperform alternative methods in terms of tardiness performance.Rule based and optimization based Workload Control without exogenous lead times overemphasize average lateness reduction,which leads to sequence deviations that offset performance improvements through balancing.In contrast,Workload Control methods that assume lead times to be exogenous limit sequence deviations,which leads to a significant reduction in dispersion of lateness.This has important implication for the future design of order release methods,and managerial practice.
