TikTok Shop在东南亚五国的发展策略研究——基于SWOT-AHP模型的分析

伴随全球电商渗透率持续提升,我国的一批优秀企业纷纷开通跨境电商平台,以更好地助力品牌出海。字节跳动旗下的全球最大的短视频网站之一Tiktok就是其中的典型代表,其在东南亚五国开通了Titok Shop,为我国企业开拓东南亚市场搭建优质的跨境电商平台。首先总结了TikTok Shop在东南亚五国开展业务的现状,采用SWO T-AHP方法对TikTok Shop在东南亚五国的发展进行深入剖析,认为其战略发展方向应以SO战略为主;提出TikTok Shop应完善跨境电商物流体系、深耕本土化运营、完善品牌化建设、加强基础设施建设、培养跨境电商复合型人才等发展策略,以期为我国企业搭建优质的跨境电商平台,助力我国品牌出海提供有价值的参考。

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

Variety Selection and Green Cultivation Technology of Courtyard Ornamental and Edible Peppers

In order to comply with the development trend of the multifunctional use of peppers,we conducted an investigation into the characteristics and features of varieties,potting management techniques,and the methods of extending the fruit ornamental period and other aspects of courtyard ornamental and edible peppers.A set of cultivation techniques suitable for courtyard ornamental and edible peppers has been developed,including timely sowing and seedling,nutrient soil preparation,water and fertilizer management,trimming and pruning,preservation of flowers and fruits,green prevention and control of diseases and pests,harvesting,and so on.

Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian new area,China:A cross-sectional survey-Retraction

In the article titled“Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian New Area,China:A cross-sectional survey”by Zhang Q and Liu L(J Integr Nurs 2021;3(3):117-121.doi:10.4103/jin.jin_14_21),[1]the content and results data of this article was questioned by International database(Web of Science)institution.This article was then investigated by the publisher and Journal of Integrative Nursing(JIN).

Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

Deep Reinforcement Learning Solves Job-shop Scheduling Problems

To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time.

Modern Courtyard Design against Chinese and Western Cultural Background

Characteristics and differences of Chinese and westerners in cultural background,lifestyle,and psychological needs were analyzed,on the basis of which gardening arts in both regions explored,and trend of modern courtyard design summarized.Taking Hollister House Garden(Connecticut)and Taihe"Yard by Canal"(Beijing)for example,differences of modern Chinese and western yard designs were compared.

Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV

As a typical transportation tool in the intelligent manufacturing system,Automatic Guided Vehicle(AGV)plays an indispensable role in the automatic production process of the workshop.Therefore,integrating AGV resources into production scheduling has become a research hotspot.For the scheduling problem of the flexible job shop adopting segmented AGV,a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function,and an improved genetic algorithmis designed to solve the problem in this study.The algorithmdesigns a two-layer codingmethod based on process coding and machine tool coding and embeds the task allocation of AGV into the decoding process to realize the real dual resource integrated scheduling.When initializing the population,three strategies are designed to ensure the diversity of the population.In order to improve the local search ability and the quality of the solution of the genetic algorithm,three neighborhood structures are designed for variable neighborhood search.The superiority of the improved genetic algorithmand the influence of the location and number of transfer stations on scheduling results are verified in two cases.

A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling

Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods.

An Index for Measuring Aesthetic Perception of Shopping Building Frontages

Architects treat building frontages with careful attention to aesthetic detail.The ability of a building’s frontage to draw attention is directly proportional to its aesthetic perception resulting from the composition of its constituent physical features.While deciding on the building’s aesthetics is the architect’s prerogative,a question arises as to on what basis can an architect compare various conceptual designs and decide on the best option,considering the building users’“likeability”factor.Aesthetic perception ratings and rankings from 206 regular shoppers were elicited.Further,52 architects were asked to evaluate the aesthetics of these ranked shopping buildings.Combining Architects’Gestalt indices with Birkhoff’s aesthetic measures as well as ranking weightages given by the public,a frontage aesthetic perception index of likeability—FAPIL,is proposed as an indexical aid for design decision making by architects.The findings were elicited by giving due consideration to building users’as well as architect’s judgement so that they can be embodied into the design under conceptualization.Given site and cost constraints faced by the architect,the architect needs to ensure that the aesthetic features contributing to visual order be at least 2.3 times those that contribute to visual complexity for positive aesthetic perception of a shopping building.

An Effective Neighborhood Solution Clipping Method for Large-Scale Job Shop Scheduling Problem

The job shop scheduling problem(JSSP)is a classical combinatorial optimization problem that exists widely in diverse scenarios of manufacturing systems.It is a well-known NP-hard problem,when the number of jobs increases,the difficulty of solving the problem exponentially increases.Therefore,a major challenge is to increase the solving efficiency of current algorithms.Modifying the neighborhood structure of the solutions can effectively improve the local search ability and efficiency.In this paper,a genetic Tabu search algorithm with neighborhood clipping(GTS_NC)is proposed for solving JSSP.A neighborhood solution clipping method is developed and embedded into Tabu search to improve the efficiency of the local search by clipping the search actions of unimproved neighborhood solutions.Moreover,a feasible neighborhood solution determination method is put forward,which can accurately distinguish feasible neighborhood solutions from infeasible ones.Both of the methods are based on the domain knowledge of JSSP.The proposed algorithmis compared with several competitive algorithms on benchmark instances.The experimental results show that the proposed algorithm can achieve superior results compared to other competitive algorithms.According to the numerical results of the experiments,it is verified that the neighborhood solution clippingmethod can accurately identify the unimproved solutions and reduces the computational time by at least 28%.

Prevalence and Antimicrobial Susceptibility Status of Gram-Negative and Gram-Positive Bacteria on Handheld Shopping Trolleys and Baskets in Supermarkets in Ndola, Zambia

Background: Supermarkets are a place visited by individuals with different health conditions daily where microbiological contaminants through touch onto fomites such as trolleys and baskets can be passed on to other people hence potentially spreading infectious diseases. This study aimed to investigate the presence of Gram-negative and Gram-positive bacteria on handheld shopping trolleys and baskets and their antimicrobial susceptibility status against commonly used antibiotics in Zambia. Methods: A cross-sectional study was conducted. Trolleys and basket handles were swabbed and standard microbiological methods were used to identify the bacteria and disc diffusion to determine their antimicrobial susceptibility status. Data was collected from December 2021 to April 2022. Data was analysed using IBM Statistical Package for Social Sciences (SPSS) Version 22. Results: Twenty-eight percent of the 200 total samples were found to be culture-positive and predominant isolates were Staphylococcus aureus (17.3%), Pseudomonas species (4.5%), Escherichia coli (2%), Corynebacterium species (2%), Staphylococcus species (1.5%) and Enterobacter aerogenes (0.5%). Staphylococcus aureus showed the most resistance to azithromycin (17%) followed by ciprofloxacin (2.8%), nitrofurantoin (2.8%) and chloramphenicol (2.8%). Escherichia coli showed 100% resistance to amoxicillin, cloxacillin and ampicillin, 75% resistance to ciprofloxacin and the least resistance to azithromycin (25%) while it was susceptible to nitrofurantoin. Staphylococcus species, Corynebacterium species, Enterobacter aerogenes and Pseudomonas species showed no resistance to any antibiotics. Conclusion: The study showed the presence of microorganisms with considerable antimicrobial resistance to antibiotics in Zambia on trolley and basket handles indicating the need for more initiatives to address proper hygiene in public environmental sites for better infection prevention and control.

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

To solve the distributed hybrid flow shop scheduling problem(DHFS)in raw glass manufacturing systems,we investigated an improved hyperplane assisted evolutionary algorithm(IhpaEA).Two objectives are simultaneously considered,namely,the maximum completion time and the total energy consumptions.Firstly,each solution is encoded by a three-dimensional vector,i.e.,factory assignment,scheduling,and machine assignment.Subsequently,an efficient initialization strategy embeds two heuristics are developed,which can increase the diversity of the population.Then,to improve the global search abilities,a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions.Furthermore,a local search heuristic based on three parts encoding is embedded to enhance the searching performance.To enhance the local search abilities,the cooperation of the search operator is designed to obtain better non-dominated solutions.Finally,the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms.The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises(SMEs)manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities.The optimal job shop scheduling is generated by utilizing the scheduling system of the platform,and a minimum production time,i.e.,makespan decides whether the scheduling is optimal or not.This scheduling result allows manufacturers to achieve high productivity,energy savings,and customer satisfaction.Manufacturing in Industry 4.0 requires dynamic,uncertain,complex production environments,and customer-centered services.This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform.The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors.The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors:early delivery date and fulfillment of processing as many orders as possible.The genetic algorithm(GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem(JSSP)by comparing with the real-world data from a textile weaving factory in South Korea.The proposed platform will provide producers with an optimal production schedule,introduce new producers to buyers,and eventually foster relationships and mutual economic interests.

Hybrid Flow Shop with Setup Times Scheduling Problem

The two-stage hybridflow shop problem under setup times is addressed in this paper.This problem is NP-Hard.on the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high performance-computing.Tackling this kind of problem requires the development of adapted algorithms.In this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this paper.These approximate solutions are using the optimal solution of the parallel machines under release and delivery times.Indeed,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be solved.The general solution is then a concatenation of all the solutions in each stage.In addition,three lower bounds based on the relaxation method are provided.These lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative gap.An experimental result is discussed to evaluate the performance of the developed algorithms.In total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and heuristics.Several indicators are given to compare between algorithms.The results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce TEC.Finally,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard benchmark.The numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.

Long-Awaited Comeback

As waves of local consumers converge on China’s festively adorned shopping malls and markets,the general ambience conjured is of a long-awaited return to normalcy.Optimization of COVID-19 response measures has revitalized markets and expanded the traffic flow in cities.And as the Chinese Lunar New Year approached,images of rabbits,one of the 12 rotating animals of the Chinese Zodiac,emblematic of each year,could be seen everywhere.Some global brands indeed launched special-edition products in honor of the Year of Rabbit,as well as to cater to the rapidly-recovering Chinese market.

The Psychology of Shopping Addiction in Consumer Behaviour

This comprehensive article examines the phenomenon of consumer addiction,primarily focusing on shopping addiction and its dimensions,including brand addiction.It delves into the underlying causes,manifestations,and consequences of consumer addiction from both consumer and marketer perspectives,shedding light on the ethical and cultural considerations within today's society.Consumer addiction is characterized by recurrent,irresistible purchasing behaviors driven by negative emotions such as anxiety and impulsivity.It is recognized as a behavioral addiction closely intertwined with consumerism.The article emphasizes the imperative for ethical marketing practices to mitigate the exacerbation of addictive behaviors while acknowledging the impact of culture on consumer choices.The article also discusses the crucial role of research in understanding the implications of consumer addiction on the economy,and it suggests that marketers should focus on fostering positive brand addiction rather than exploiting consumerism.It underscores the influence of cultural factors on addictive consumption and calls for responsible marketing practices and governmental regulations.In conclusion,this article highlights the critical significance of consumer addiction in the field of marketing and its multifaceted implications for both consumers and businesses.It underscores the need for ethical marketing strategies,cultural awareness,and responsible brand management to address this complex phenomenon in contemporary society.