柔性Job Shops集成调度启发式算法

为有效解决柔性作业车间(Job Shops)的加工与搬运集成调度问题,以最小化最大完工时间(Makespan)为调度目标,建立非线性规划模型,提出基于贪婪启发式策略的变邻域搜索算法(GRS-RVNS).根据准时(JIT)生产和均衡生产思想构建贪婪启发式策略快速求初始解.利用析取图表示可行解并根据析取图调度的性质定理构建有效的搜索邻域,进而利用随机变邻域搜索算法对初始解进行优化.对提出的算法进行仿真实验分析,结果表明:该算法求解时间短、调度方法有竞争性.

Food safety knowledge and practices of abattoir and butchery shops and the microbial profile of meat in Mekelle City,Ethiopia

Objective:To assess the food safety knowledge and practices in meat handling,and to determine microbial load and pathogenic organisms in meat at Mekelle city.Methods:A descriptive survey design was used to answer questions concerning the current status of food hygiene and sanitation practiced in the abattoir and butcher shops.Workers from the abattoir and butcher shops were interviewed through a structured questionnaire to assess their food safety knowledge.Bacterial load was assessed by serial dilution method and the major bacterial pathogens were isolated by using standard procedures.Results:15.1%of the abattoir workers had no health certificate and there was no hot water,sterilizer and cooling facility in the abattoir.11.3%of the butchers didn’t use protective clothes.There was a food safety knowledge gap within the abattoir and butcher shop workers.The mean values of bacterial load of abattoir meat,butcher shops and street meat sale was found to be 1.1×10~5,5.6×10~5and 4.3×10~6 cfu/g,respectively.The major bacterial pathogens isolated were Escherichia coli,Staphylococcus aureus and Bacillus cereus.Conclusions:The study revealed that there is a reasonable gap on food safely knowledge by abattoir and butcher shop workers.The microbial profile was also higher compared to standards set by World Health Organization.Due attention should be given by the government to improve the food safety knowledge and the quality standard of meat sold in the city.

Factors Affecting the Rent of Shops in Commercial Complexes: A Case Study of Shenzhen Shekou Sea World

The rent of shops in commercial complex reflects consumers’ preferences to some extent, and provides references for urban planning and the construction and operation of commercial complex. In this paper, Sea World Commercial Complex in Nanshan District of Shenzhen City is taken as the research object, and correlation and variability analysis on 41 groups of shop data are conducted. It is found that rent of shops in the same commercial complex is affected by location, visibility and area of outdoor stall, while the influence of shop area on rent is not obvious. Additionally, there is no significant correlation between the accessibility to traffic stations and the rent of shops in pedestrian business district.

Art and Print Shops

Do you enjoy art?Maybe you like to draw or color.If you want to do those things,you need supplies.You can go to an art store to buy them.You can buy colored pencils,markers or crayons.You can buy paper to draw pictures on.You can also buy coloring books.If you want to make things out of paper,you can buy colorful paper,too.Are you a painter?You can buy many different kinds of paint at an art store.

Scheduling on 2-Machine Flow Shops Considering Disturbance on Job Processing Times

In this paper the scheduling problem to minimize the expected makespan is discussed on two-machine flow shops with random disturbance on job processing times. The problem is represented by a stochastic programming model. We approximate the stochastic problem by a deterministic problem which can be solved by Johnson's rule. The estimation of approximation error is also discussed by analyzing the stochastic model and its approximate LP model.

Workshops Teach Children, Adults to Stand Up for Children's Rights

She clambers up a rock, reaches both hands onto comers of the shabby cement wall and heaves herself up, being careful not to catch her school bag on the protruding bricks. She straddles the top and then jumps gingerly down onto the pebbled ground beside the railway line.

A Cross Entropy-Genetic Algorithm for m-Machines No-Wait Job-ShopScheduling Problem

No-wait job-shop scheduling (NWJSS) problem is one of the classical scheduling problems that exist on many kinds of industry with no-wait constraint, such as metal working, plastic, chemical, and food industries. Several methods have been proposed to solve this problem, both exact (i.e. integer programming) and metaheuristic methods. Cross entropy (CE), as a new metaheuristic, can be an alternative method to solve NWJSS problem. This method has been used in combinatorial optimization, as well as multi-external optimization and rare-event simulation. On these problems, CE implementation results an optimal value with less computational time in average. However, using original CE to solve large scale NWJSS requires high computational time. Considering this shortcoming, this paper proposed a hybrid of cross entropy with genetic algorithm (GA), called CEGA, on m-machines NWJSS. The results are compared with other metaheuritics: Genetic Algorithm-Simulated Annealing (GASA) and hybrid tabu search. The results showed that CEGA providing better or at least equal makespans in comparison with the other two methods.

Dynamic Scheduling of Flexible Job Shops

Aim of this research is to minimize makespan in the flexible job shop environment by the use of genetic algorithms and scheduling rules. Software is developed using genetic algorithms and scheduling rules based on certain constraints such as non-preemption of jobs, recirculation, set up times, non-breakdown of machines etc. Purpose of the software is to develop a schedule for flexible job shop environment, which is a special case of job shop scheduling problem. Scheduling algorithm used in the software is verified and tested by using MT10 as benchmark problem, presented in the flexible job shop environment at the end. LEKIN software results are also compared with results of the developed software by the use of MT10 benchmark problem to show that the latter is a practical software and can be used successfully at BIT Training Workshop.

Modified Shifting Bottleneck Heuristic for Scheduling Problems of Large-Scale Job Shops

A modified shifting bottleneck algorithm was proposed to solve scheduling problems of a large-scale job shop.Firstly,a new structured algorithm was employed for sub-problems so as to reduce the computational burden and suit for large-scale instances more effectively.The modified cycle avoidance method,incorporating with the disjunctive graph model and topological sort algorithm,was applied to guaranteeing the feasibility of solutions with considering delayed precedence constraints.Finally,simulation experiments were carried out to verify the feasibility and effectiveness of the modified method.The results demonstrate that the proposed algorithm can solve the large-scale job shop scheduling problems(JSSPs) within a reasonable period of time and obtaining satisfactory solutions simultaneously.

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。

Heuristic for no-wait flow shops with makespan minimization based on total idle-time increments

No-wait flow shops with makespan minimization are classified as NP-hard. In this paper, the optimization objective is equivalently transformed to total idle-time minimization. The independence relationship between tasks is analyzed, and objective increment properties are established for the fundamental operators of the heuristics. The quality of the new schedules generated during a heuristic is judged only by objective increments and not by the traditional method, which computes and compares the objective of a whole schedule. Based on objective increments, the time complexity of the heuristic can be decreased by one order. A seed phase is presented to generate an initial solution according to the transformed objective. Construction and improvement phases are introduced by experimental analysis. The FCH （fast composite heuristic） is proposed and compared with the most effective algorithms currently available for the considered problem. Experimental results show that the effectiveness of the FCH is similar to that of the best methods but requires far less computation time. The FCH can also be efficient in real time scheduling and rescheduling for no-wait flow shops.

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.

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.

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).

A Windy and Rainy Shanghai

On Suzhou Road in Shanghai,there stood a mercer’s shop.Its shopkeeper was surnamed Yu,but the shop was named Wu’s Silk Shop instead of Yu’s Silk Shop.Yu Jide,the shopkeeper,was born in the town of Yushan,Songjiang Prefecture(a former administrative area of today’s Shanghai).

Spring Festival Celebrations Fire Maltese Fascination with Chinese Culture--An Exclusive Interview with Director of the China Cultural Center in Malta Yuan Yuan

THRONGS of shoppers,in a department store adorned with colorful Spring Festival posters,vying to try their hand either at Chinese calligraphy or traditional Chinese lantern-making,might seem incongruous in a European setting.The background to this vibrant scenario was The Point,Malta’s largest shopping mall,as part of the island country’s 2023 Chinese New Year celebrations,courtesy of the China Cultural Center in Malta.

TikTok招商

“数据帝国在悄悄生新枝了。”1月下旬,TikTok洛杉矶总部,参加完Shop招商大会,从事外贸生意的Louis感概。Louis告诉《21CBR》记者,TikTokShop正大力扶持卖家,贴钱给流量,非常慷慨。例如,商家会得到半邮或全邮补贴福利,全美邮费价格约5~10美金/磅。

MEET YOUR TEACHER Hazel

Q:Do you enjoy shopping?What do you like to buy?A:I love shopping for fun things such as puppets and props.I also enjoy shopping for musical instruments and interesting things for my apartment.But my apartment is getting full,so it's time to stop shopping.