Web 2.0时代,消费者对产品或服务的在线评论逐渐受到商家的重视,许多商家也开始通过制造虚假评论的方式主动影响消费者购买意愿。在这个背景下,从天猫商城收集到大量美妆产品和数码产品的评论数据,引入BERT模型对评论进行分类,识别出真...Web 2.0时代,消费者对产品或服务的在线评论逐渐受到商家的重视,许多商家也开始通过制造虚假评论的方式主动影响消费者购买意愿。在这个背景下,从天猫商城收集到大量美妆产品和数码产品的评论数据,引入BERT模型对评论进行分类,识别出真实评论及虚假评论,进而讨论虚假评论对于消费者购买意愿的影响。研究发现:消费者在选购体验品时,虚假评论会对消费者的购买决策产生显著影响。在消费者选购搜索品时,虚假评论则不会产生显著影响。归纳了虚假评论的主要特点,为消费者识别虚假评论提供了方法,同时实证研究结果也表明,许多电商卖家操纵评论的行为是无意义的,并不能正面影响其效益。展开更多
This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage ti...This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.展开更多
基金Thailand Research Fund (Grant #MRG5480176)National Research University Project of Thailand Office of Higher Education Commission
文摘This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.
