Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,...Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.展开更多
A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard...A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard, in the strong sense, or open problems, therefore approximation algorithms are studied. The review reveals that there exist some potential areas worthy of further research.展开更多
现有的基于评论与评分的方法通常使用相同的模型分别对用户和项目进行建模,但其局限在浅层特征层面,如果能够充分挖掘用户个性化偏好与项目深层特征,则会促进模型学习两种表示之间更深层次的关系从而提升预测结果.因此,本文提出一种融...现有的基于评论与评分的方法通常使用相同的模型分别对用户和项目进行建模,但其局限在浅层特征层面,如果能够充分挖掘用户个性化偏好与项目深层特征,则会促进模型学习两种表示之间更深层次的关系从而提升预测结果.因此,本文提出一种融合评论与评分的个性化推荐方法,用于深度挖掘用户偏好与项目特征.在对评论文本进行处理的过程中,首先通过ALBERT获得评论文本中单词的向量表示.其次,提出的个性化注意模块将用户的个性化偏好信息与评论文本向量结合,得到深层的基于评论的用户表示.在Amazon Digital Music、Grocery and Gourmet Food、Video Games数据集上进行实验,本文方法较基准方法在NDCG指标上分别提升了5%、11%、8%.代码已在https://github.com/ZehuaChenLab/paperCode/tree/main/DuWenNa/PRM-RR公开.展开更多
基金supported in part by the National Natural Science Foundation of China(61603169,61773192,61803192)in part by the funding from Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technologyin part by Singapore National Research Foundation(NRF-RSS2016-004)
文摘Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.
基金the National Natural Science Foundation of China (70631003)the Hefei University of Technology Foundation (071102F).
文摘A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard, in the strong sense, or open problems, therefore approximation algorithms are studied. The review reveals that there exist some potential areas worthy of further research.
文摘现有的基于评论与评分的方法通常使用相同的模型分别对用户和项目进行建模,但其局限在浅层特征层面,如果能够充分挖掘用户个性化偏好与项目深层特征,则会促进模型学习两种表示之间更深层次的关系从而提升预测结果.因此,本文提出一种融合评论与评分的个性化推荐方法,用于深度挖掘用户偏好与项目特征.在对评论文本进行处理的过程中,首先通过ALBERT获得评论文本中单词的向量表示.其次,提出的个性化注意模块将用户的个性化偏好信息与评论文本向量结合,得到深层的基于评论的用户表示.在Amazon Digital Music、Grocery and Gourmet Food、Video Games数据集上进行实验,本文方法较基准方法在NDCG指标上分别提升了5%、11%、8%.代码已在https://github.com/ZehuaChenLab/paperCode/tree/main/DuWenNa/PRM-RR公开.
