Emergency Local Searching Approach for Job Shop Scheduling

Existing methods of local search mostly focus on how to reach optimal solution.However,in some emergency situations,search time is the hard constraint for job shop scheduling problem while optimal solution is not necessary.In this situation,the existing method of local search is not fast enough.This paper presents an emergency local search(ELS) approach which can reach feasible and nearly optimal solution in limited search time.The ELS approach is desirable for the aforementioned emergency situations where search time is limited and a nearly optimal solution is sufficient,which consists of three phases.Firstly,in order to reach a feasible and nearly optimal solution,infeasible solutions are repaired and a repair technique named group repair is proposed.Secondly,in order to save time,the amount of local search moves need to be reduced and this is achieved by a quickly search method named critical path search(CPS).Finally,CPS sometimes stops at a solution far from the optimal one.In order to jump out the search dilemma of CPS,a jump technique based on critical part is used to improve CPS.Furthermore,the schedule system based on ELS has been developed and experiments based on this system completed on the computer of Intel Pentium(R) 2.93 GHz.The experimental result shows that the optimal solutions of small scale instances are reached in 2 s,and the nearly optimal solutions of large scale instances are reached in 4 s.The proposed ELS approach can stably reach nearly optimal solutions with manageable search time,and can be applied on some emergency situations.

从目的论谈The Job Search Handbook的翻译

翻译目的论是德国功能翻译学派极其重要的理论,它认为决定一切翻译过程的首要准则是整个翻译行为的目的。The Job Search Handbook一书旨在帮助求职者获得他们理想的工作。由于该书是以读者为导向的,并具有信息功能文本和呼语文本的特点,因此,可以翻译目的论为指导对该书进行翻译。

Combined Timetabling Procedure and Complete Local Search for No-Wait Job Shop Scheduling with Total Tardiness

The strong non-deterministic polynomial-hard( NP-hard)character of job shop scheduling problem( JSSP) has been acknowledged widely and it becomes stronger when attaches the nowait constraint,which widely exists in many production processes,such as chemistry process, metallurgical process. However,compared with the massive research on traditional job shop problem,little attention has been paid on the no-wait constraint.Therefore,in this paper, we have dealt with this problem by decomposing it into two sub-problems, the timetabling and sequencing problems,in traditional frame work. A new efficient combined non-order timetabling method,coordinated with objective of total tardiness,is proposed for the timetabling problems. As for the sequencing one,we have presented a modified complete local search with memory combined by crossover operator and distance counting. The entire algorithm was tested on well-known benchmark problems and compared with several existing algorithms.Computational experiments showed that our proposed algorithm performed both effectively and efficiently.

An Tentative Taboo Search Algorithm for Job Shop Scheduling

An effective heuristic algorithm for solving the minimum makespan problem of job shop scheduling is presented. With a given feasible solution as a start point, we use Taboo Search technique to get a schedule for each neighbor of all its neighbors, and select the best one of the schedules as the new start point. We repeat the procedure, unless the new start point is not better than the proceeding one. To make sure of better results, we introduce reverse technique to the algorithm as well. The computational experiments of the 45 standard instances show that our algorithm yields better results than TSAB algorithm.

Local Search Algorithm with Hybrid Neighborhood and Its Application to Job Shop Scheduling Problem

A new local search method with hybrid neighborhood for Job shop scheduling problem is developed. The proposed hybrid neighborhood is not only efficient in local search, but also can help overcome entrapments while search procedure get trapped at local optima and carry the search to areas of the feasible set with better prospect. New strategies used for breaking out of entrapments are presented and they are helpful for the procedure to improve local optima. A performance comparison of the proposed method with some best-performing algorithms on all 10-job, 10-machine benchmark problems and the other two problems generated by Fisher and Thompson (ie., FT6 and FT20)is made. The experiment results show the better optimal performance of the proposed algorithm.

The Relationship between College Graduate’s Dual Self-Consciousness and Job Search Clarity: The Mediating Role of Perceived Stress

The aim of the study was to explore the relationship between college graduates’dual self-consciousness,job search clarity and perceived stress,and reveal the mediating role of perceived stress between dual self-consciousness and job search clarity.In this study,467 college graduates were investigated using the Dual Self-Consciousness Scale,Job Search Clarity Scale,and Perceived Stress Scale.After controlling for gender,age,and region,the results revealed that:(1)private self-consciousness has a significant positive predictive effect on job search clarity;(2)perceived stress has a significant negative predictive effect on job search clarity;(3)perceived stress plays partial mediation effects between private self-consciousness and job search clarity;(4)perceived stress plays complete mediation effects between public self-consciousness and job search clarity;(5)perceived stress has suppressing effects between public self-consciousness and job search clarity.

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%.

改进文化基因算法求解双资源约束柔性作业车间调度问题

针对具有机器和工人的双资源约束柔性作业车间调度问题,以最小化最大完工时间为目标构建调度模型,并设计一种改进文化基因算法对其进行求解。由于该调度问题需要同时考虑工序排序、机器选择及工人选择3个子问题,故采用三层序列编码。考虑传统解码方式存在收敛速度慢、收敛不完全的弊端,设计一种扩展型插入式主动解码方式,以提高算法的收敛速度;针对进化算法易陷入局部最优的缺陷,设计一种基于负载平衡的机器和工人再分配算子,增强算法的全局搜索能力,对种群中的优秀个体采用改进变邻域搜索以提高算法的局部寻优能力。最后,利用仿真算例及航空设备生产实例进行实验,验证所提算法求解双资源约束调度问题的有效性。

互联网工作搜寻对非正式工作搜寻的替代效应研究

本文基于中国综合社会调查和中国流动人口动态监测调查数据,研究了互联网使用对求职者工作搜寻渠道选择的影响。研究发现,互联网使用总体上促成了网络渠道对熟人推荐这一非正式渠道的替代,但后者仍是更为主要的工作搜寻方式。进一步的分析表明,这种替代效应的存在与有限性来源于两方面。一方面,互联网渠道在工薪水平等职位基本信息维度上更具优势,通过网络渠道获取的工作,其小时工资显著高于非正式渠道。另一方面,网络渠道无法有效解决求职者能力信息及岗位深层次信息的信息不对称问题。所以,只有拥有高学历这一强能力信号的求职者更倾向于利用网络渠道取代非正式渠道;相比于非正式渠道,通过网络渠道获得工作的求职者会有更强烈的换工作意愿,反映出相对更差的搜寻匹配效果。

混合遗传变邻域搜索算法求解柔性车间调度问题

针对考虑生产成本的柔性作业车间调度问题(flow job shop scheduling problem, FJSP),以完工时间与加工成本为优化指标,提出一种求解FJSP的混合遗传变邻域搜索算法。根据个体适应度对种群分割,结合自适应交叉概率改进子代种群产生方式;设计两种邻域结构增强算法的局部搜索能力;提出一种基于动态交叉变异概率的优化算法流程提高求解效率。运用提出的算法求解基准实例与实际问题测试,验证了算法的有效性。

基于ISSA和IA^(*)的AGV集成作业调度及其路径规划

针对单一算法在求解车间调度和路径问题时最优性和多样性方面的缺陷,提出了优化飞鼠搜索算法ISSA(improved squirrel search algorithm)和优化A^(*)算法并建立集成作业调度和AGV路径规划的双层模型。首先,采用贪婪策略融合飞鼠搜索算法建立考虑能耗的AGV集成作业调度上层模型;其次,将安全距离因子引入A^(*)算法,构建AGV路径规划下层模型,并通过梯度下降法进行路径平滑;进而,运用6个测试函数和kacem实例验证ISSA的寻优能力,结果表明ISSA的其收敛速度较快,运行效率较高,且不容易陷入局部最优;最后,基于栅格法建模进行对比仿真实验,IA^(*)比A^(*)算法拐点数量降低了22%,同时节约了21%的行驶时间,ISSA和IA^(*)均得到了良好的验证。结果表明,ISSA和IA^(*)能够更有效求解AGV集成作业调度及其路径规划问题。

混合白鲸优化算法求解柔性作业车间调度问题

针对柔性作业车间调度问题(flexible job-shop scheduling problem,FJSP),提出一种混合白鲸优化算法(hybrid beluga whale optimization,HBWO)对其求解,旨在最小最大化完工时间。采用既定策略改进标准白鲸优化算法(beluga whale optimization,BWO),加快其收敛速度;基于机器选择和工序排序问题设计双层编码方案,解决FJSP离散化问题;采用主动编码及种群初始化策略,提高求解质量;基于工序的开始和结束时间确定关键路径和关键块,注重各工序时间维度;引入贪心思想至基于关键路径的混合变邻域搜索策略中,加大勘测搜索空间及减少无效搜索;此外,引入遗传算子防止算法陷入局部最优;通过35个标准算例的仿真实验与分析,证明了算法在求解FJSP问题中具有有效性。

碳交易下分布式双资源柔性作业车间节能调度

在碳交易政策的背景下,企业需统筹考虑效率和环境因素,以实现企业效益最大化。文章研究了考虑机器和工人的分布式双资源柔性作业车间节能调度问题和方案,以最短完工时间、最小能耗和碳交易成本为目标,建立了混合整数规划模型。依据此问题多资源约束的特点,研究设计了一种改进麻雀搜索算法,嵌入了“工厂—工序—机器&工人”三层编码的主动解码策略,提高了资源利用率。为了扩大搜索空间,在算法中引入了6种局部搜索策略,通过2、3、4个工厂共30组算例,将改进的麻雀搜索算法与其他3种算法进行对比。实验结果表明,改进的麻雀搜索算法优于其他对比的算法,对比结果验证了此改进算法的有效性。

高校毕业生未来时间洞察力对求职行为的影响——基于职业规划能力和职业决策自我效能感的作用机制

以天津市高校毕业生为例,实证分析了未来时间洞察力、职业决策自我效能感、职业规划能力与毕业生求职行为的关系。研究表明:高校毕业生的未来时间洞察力、职业决策自我效能感、职业规划能力、求职行为之间两两显著正相关。高校毕业生未来时间洞察力可以直接预测求职行为,也可以通过职业决策自我效能感的中介作用间接影响求职行为,职业规划能力在其中有负向调节作用。

生涯决策模糊容忍度对大学生求职行为的影响:一个有调节的中介模型

本研究基于社会认知职业理论探讨了模糊容忍度和大学生求职行为的关系及其作用机制。采用模糊容忍度量表、未来时间知觉量表、未来工作自我量表以及求职行为量表对731名大学生进行问卷调查。结果表明:(1)模糊容忍能够显著正向预测大学生的求职行为;(2)未来工作自我在模糊容忍与求职行为之间起部分中介作用;(3)未来时间知觉调节了该中介模型的前半段路径和直接路径。

基于POX交叉的遗传算法求解Job-Shop调度问题

通过改进传统的遗传算法求解Job -Shop调度问题。为基于工序的编码提出了一种新的POX交叉算子 ,并与其他交叉算子进行了比较以显示其高效性。为了保留父代的优良特征和减少遗传算子的破坏性 ,设计了一种子代交替模式的交叉方式。将提出的改进遗传算法应用于muthandthompson’s基准问题的实验运行 ,显示该算法的有效性。

求解Job Shop调度问题的改进禁忌搜索算法

提出一种改进的禁忌搜索算法,解决传统禁忌搜索算法优化效果对运行次数和初始解依赖的不足,提高这类问题的求解质量。根据Job Shop调度问题解的特征,提出基于关键工序的邻域选择方法,采用此邻域选择方法构造禁忌搜索算法,当无邻域时,重新产生初始解进行禁忌搜索,将传统的禁忌搜索算法从单起始点搜索改进成多起始点搜索。采用改进的禁忌搜索算法对13个难的benchmarks问题进行10次求解,得到的平均值8个优于TSAB算法,得到的最优解6个优于TSAB算法、4个与TSAB算法相同。采用基于关键工序的邻域结构构造的改进TS算法具有较强的搜索能力。

一种求解Job Shop调度问题的混合粒子群优化算法

目的解决单一粒子群算法求解Job Shop调度问题存在的不足,提高这类问题的求解质量.方法采用粒子群算法进行全局搜索,将禁忌搜索算法用于并行局部搜索,禁忌搜索在找到改进解的邻域时采用动态记忆的方式.结果在较短时间内,找到了LA21,LA24等典型benchmarks问题的最优解,十次求解的平均值的平均相对误差百分比比并行遗传算法和禁忌搜索算法分别小了2.94%和0.56%.结论提出一种混合粒子群算法,增强了粒子群算法的局部搜索能力,说明该混合粒子群优化算法是有效的.

一种求解Job-shop调度问题的遗传局部搜索算法

利用遗传局部搜索算法求解了作业车间调度问题,遗传算法中的染色体编码采用基于工序的编码,并用插入式贪婪解码机制将染色体解码至主动调度。为了克服传统遗传算法易于早熟收敛的缺点,设计了一种改进的优先操作交叉IPOX操作和子代产生模式的遗传算法。对于遗传算法每个染色体个体,使用基于N6邻域结构的局部搜索进一步使它们得到改善。利用所提出的混合遗传算法求解基准问题,验证了算法的有效性。

一类解决Job Shop问题的禁忌搜索算法

针对Job shop问题,设计了一种改进的禁忌搜索算法(MTS算法)。MTS算法从多个初始解开始,将传统禁忌搜索算法由串行搜索结构变为并行搜索结构;采用互换和交叉两种邻域搜索函数,既有利于新邻域的探索又有利于交换信息;基于目标值的禁忌表保证了群体的多样性。实验表明,MTS算法克服了传统禁忌搜索算法的缺陷,具有较高的求解质量和鲁棒性。