加工能力受限的单机干扰管理研究

针对单机环境下最小化加权折扣加工时间和的排序问题,研究如何应对可预见的干扰事件。由于干扰事件使得机器加工能力受限,初始最优加工时间表不再可行,采用外包的方式来进行干扰管理。构建了排序模型,同时考虑原目标和与初始计划偏离的扰动目标,选择外包工件集并对所有工件进行重排序。为了求解得到的双目标排序问题,基于理想点法设计了一种动态规划算法和量子遗传算法相结合的算法。最后通过一个数值算例说明,该排序模型对于求解加工能力受限的单机干扰管理问题是有效的。     

大规模集成电路预烧作业中分批排序问题的数学模型

分批排序(Batch Scheduling)是在半导体生产过程的最后阶段提炼出来的一类重要的排序问题。单机分批排序问题就是n个工件在一台机器上加工,要将工件分批,每批最多可以同时加工B个工件,每批的加工时间等于此批工件中的最大的加工时间。Skutella^[8]1998年把平行机排序的P｜｜∑ω_jC_j和R｜｜∑ω_jC_j表述成二次的0-1整数规划,得到一些令人满意的结果;国内罗守成等^[9]、张倩^[10]给出了单机排序问题1｜｜∑ω_jC_j的数学规划表示,对于用数学规划来研究排序问题是一个很有意义的进展。本文首先介绍总完工时间和最小的带权单机分批排序问题1｜B｜∑ω_jC_j,然后将1｜B｜∑ω_jC_j表示成数学规划的形式,并且用数学规划中的对偶理论证明了SPT序是其特殊情况1｜B=1｜∑C_j的最优解。     

考虑加工与运输协同调度的单机排序问题

在考虑加工与运输协同调度的单机排序问题中,每个工件尺寸不同,工件在一台机器加工后,由m辆有容量限制的运输工具运送到同一个顾客处,目标是极小化最后一个送到其顾客的工件的到达时间,本文给出了该问题的一个最优算法,并且证明了该算法的最坏情况界为3/2。     

加权折扣单机排序干扰管理模型和算法研究

为解决机器排序中由于干扰事件的发生使初始最优加工时间表无法按计划执行的问题,构建同时考虑原目标和扰动目标的双目标干扰管理模型,对初始最优加工时间表进行调整并对未完工工件进行重排序;在双目标干扰管理模型中,原目标由所有工件的加权折扣完工时间和来度量,扰动目标由重排序后工件完工时间的变化来度量;结合量子比特在表示解的多样性方面的优点和非支配排序遗传算法在处理多目标排序问题上的优点,设计一种量子遗传算法和非支配排序遗传算法相结合的启发式进化算法对构建的模型进行求解。在数值算例中,通过比较若干项针对有效解集的性能指标发现,该混合算法求得的有效解集在多样性和与最优有效前沿的邻近性等方面优于目前得到广泛应用的非支配排序遗传算法,验证了构建的模型和算法对于求解机器排序干扰管理问题的有效性。     

三台平行机排序的一个复合半在线问题的算法

本文讨论一个三台平行机半在线排序问题.对预先知道工件的总加工时间和最大的工件的加工时间的复合半在线模型,我们证明了不存在半在线算法,其竞争比为4/3,并给出了一个竞争比为7/5的半在线算法,两者的差距小于0.067.     

基于改进遗传算法的置换装配线调度问题研究

企业的置换装配线调度问题（Permutation Assembly-line Scheduling Problem,PASP）是一类典型的NP-hard型生产调度问题,是现代集成制造系统CIMS极为关心的问题。该问题可以具体描述为n个工件要在m台机器上加工,每个工件需要经过m道工序,每道工序要求不同的机器,这n个工件通过m台机器的顺序相同,它们在每台机器上的加工顺序也相同,问题的主要目标是找到n个工件在每台机器上的最优加工顺序,使得最大完工时间最小。由于PASP问题的NP-hard性质,本文使用遗传算法对其进行求解。尽管遗传算法常用以求解调度问题,但其选择与交叉机制易导致局部最优及收敛慢。因此,本文提出基于区块挖掘与重组的改进遗传算法用于求解置换装配线调度问题。首先通过关联规则挖掘出不同的优秀基因,然后将具有较优结果的基因组合为优势区块,产生具优势的人工解,并引入高收敛性的局部搜索方法,提高搜索到最优解的机会与收敛效率。本文以OR-Library中Taillard标准测试例来验证改进遗传算法的求解质量与效率,结果证明：本文所提算法与其它求解调度问题的现有5种知名算法相比,不仅收敛速度较快,同时求解质量优于它们。     

平行机作业环境下的订单分配与排序

针对一类平行机作业环境下的订单分配与排序问题,从整体的角度协同优化供应链中的订单分配、生产调度和分批运输调度。以完成所有订单的总订货提前期与生产运输总成本的加权和最小化为目标,构建了问题的数学模型。将基于向量组编码结构的禁忌搜索算法与基于动态规划方法的启发式算法相结合,设计了一种混合优化算法以求解问题。对不同策略和不同算法进行比较,数据实验结果显示了订单分配与排序策略的优越性及所提算法的有效性。     

服务水平约束下流水车间成套订单调度研究

本文针对流水车间环境,研究了服务水平约束下成套订单的调度问题,通过建立与分析该问题的模型,研究并设计了用于排序的三种构造式启发算法,以及用于确定订单成套数的构造式启发算法、改进遗传算法和模拟退火算法,最后通过各种规模及不同约束度的算例进行模拟计算与比较分析,结果表明了改进遗传算法的有效性,但其它两种算法也各有优点。     

等待时间受限的流水车间调度问题的启发式算法

针对等待时间受限的流水车间调度问题,分析了等待时间上限与可行解的解析关系以及目标函数的特殊性质,以此为基础,提出了一种启发式算法.算法采用贪婪与插入相结合的启发式规则构造工件加工序列,通过递归回溯解消其等待时间受限约束.仿真实验表明,该启发式工件排序规则在等待时间约束较紧或问题规模较大时,较其他几种常用排序规则具有更好的效果.     

具有优先约束和加工时间依赖开工时间的单机排序问题

研究工件间的优先约束为串并有向图的单机加权总完工时间问题,通过证明在工件加工时间是开工时间的线性函数的情况下,模块M的ρ因子最大初始集合I中的工件优先于模块M中的其它工件加工,并且被连续加工所得的排序为最优排序,从而将Lawler用来求解约束为串并有向图的单机加权总完工时间问题的方法推广到这个问题上来。     

Taking into account the rescheduling problem during the scheduling phase

The scheduling problem in production management has been studied for a considerable time, and several types of software are used. A problem arises in updating the production planning, or ‘rescheduling’, when an unexpected event occurs in the shop control. Solving this problem is difficult because the implications of such events are usually impossible to forecast. To prevent this problem, we propose to manipulate a set of equivalent schedules during the short time schedule. Then, if an unexpected event prevents realization of a given schedule, it will be possible to find an equivalent one, without full rescheduling. The primary requirement is to find a formal representation of a set of schedules. This has already been explored using CPM graphs with nodes associated to a set of tasks. We propose in this paper to use an extension of such graphs, PQR trees, that represent both precedence and group constraints. We first reiterate the notion of PQR trees. We present methods to take into account date constraints in such a structure, and we give a model for the general job-shop problem.     

随机多阶段分销网络设计模型

为了更合理的设计分销网络,本文提出了一种随机多阶段的联合选址-库存模型。在该模型中,不仅考虑了经济规模和分摊效益的影响。同时通过情景规划,考虑了在多阶段的分销网络设计中,对未来市场环境的不确定性。该模型的目标是使整个战略周期内的总期望成本(包括库存、运输、选址成本与损失的收益)最小。本文将该模型建立成为了一个非线性的整数规划模型,同时提出了一种基于拉格朗日松弛的求解算法。最后,本文使用该算法求解了三组不同规模的算例,得到的计算结果证明了拉格朗日算法是求解该模型的有效算法。     

Single-machine scheduling with general sum-of-processing-time-based and position-based learning effects

In this paper, we propose a new learning effect model in which the actual job processing time is a general function of the normal processing time of jobs already processed and its scheduled position. This model has the advantage that different learning curves can be constructed easily, such as the plateau function. It is found that most of the models in the literature are special cases of our proposed model. The optimal sequences for some single-machine problems are then provided.     

考虑不确定履约率的全局鲁棒供应组合决策研究

生产和采购的全球化为供应链企业带来了更多的复杂性与不确定性。收集全面信息和使用有效的工具是控制决策风险、减少不确定性危害的有效途径。在供应商选择与订单分配管理中,供应商履约率是影响企业供应组合决策的主要因素,如何对其进行定量刻画是迫切需要解决的问题。已有的研究通常是由期望方法或鲁棒方法来刻画履约率。然而,期望履约率法往往忽略了履约率的波动性,从而造成需求损失;而鲁棒履约率法通常存在过于保守、退化过于迅速两个缺点,使企业承担不必要的成本。本文研究供应商选择与订单分配决策问题,使用全局鲁棒优化的两个不确定集合来刻画供应商履约率,同时给出了这一问题的确定性鲁棒表达和一种多项式时间求解算法。最后,通过仿真实验证实本文的全局鲁棒优化模型可克服期望模型和一般鲁棒模型的缺点。本文提出的算法能有效求解本文全局鲁棒优化等价确定性问题,为企业的供应商选择和订单分配决策提供更为灵活精确的辅助工具。     

An Integrated Text Analytic Framework for Product Defect Discovery

The recent surge in the usage of social media has created an enormous amount of user‐generated content (UGC). While there are streams of research that seek to mine UGC, these research studies seldom tackle analysis of this textual content from a quality management perspective. In this study, we synthesize existing research studies on text mining and propose an integrated text analytic framework for product defect discovery. The framework effectively leverages rich social media content and quantifies the text using various automatically extracted signal cues. These extracted signal cues can then be used as modeling inputs for product defect discovery. We showcase the usefulness of the framework by performing product defect discovery using UGC in both the automotive and the consumer electronics domains. We use principal component analysis and logistic regression to produce a multivariate explanatory analysis relating defects to quantitative measures derived from text. For our samples, we find that a selection of distinctive terms, product features, and semantic factors are strong indicators of defects, whereas stylistic, social, and sentiment features are not. For high sales volume products, we demonstrate that significant corporate value is derivable from a reduction in defect discovery time and consequently defective product units in circulation.     

Evaluating productive performance: A new approach based on the product-mix problem consistent with Data Envelopment Analysis

We propose a new approach to estimate technical coefficients from a set of Decision Making Units (DMUs) under the assumption that their production plans are set by process engineers through Linear Programming (LP) techniques. The idea behind this approach is that most manufacturing and agricultural firms routinely resort to LP-based modeling in their decision making processes in order to plan output production and, therefore, this particularity should be taken into account when estimating their technical efficiency. A usual model of LP for these sectors is the so-called product-mix problem, which we relate to a standard Data Envelopment Analysis (DEA) model in terms of the Directional Distance Function. In this paper, we finally show how to estimate the technical coefficients of a sample of Andalusian farms in Spain and how this information can be seen as a complement to the usual by-products associated with estimating technical efficiency by DEA.     

An Arborescent Network Formulation and Dual Ascent Based Procedure for the Two-Stage Multi-Item Dynamic Demand Lotsize Problem

Traditional approaches for modeling and solving dynamic demand lotsize problems are based on Zangwill's single-source network and dynamic programming algorithms. In this paper, we propose an arborescent fixed-charge network (ARBNET) programming model and dual ascent based branch-and-bound procedure for the two-stage multi-item dynamic demand lotsize problem. Computational results show that the new approach is significantly more efficient than earlier solution strategies. The largest set of problems that could be solved using dynamic programming contained 4 end items and 12 time periods, and required 475.38 CPU seconds per problem. The dual ascent algorithms averaged .06 CPU seconds for this problem set, and problems with 30 end items and 24 time periods were solved in 85.65 CPU seconds. Similar results verify the superiority of the new approach for handling backlogged demand. An additional advantage of the algorithm is the availability of a feasible solution, with a known worst-case optimality gap, throughout the problem-solving process.     

The optimum targeting for a single filling operation with rectifying inspection

One of the important issues in quality control is finding the optimal target value for a process under various inspection policies. Researchers have considered various aspects of this problem with 100% inspection and with quality sampling plans. In this paper we consider the problem of targeting the set point when rectifying inspection is used. We develop a model for this problem and propose a scheme for solving it. We also provide a numerical example.     

Horn Clause Computation with DNA Molecules

In this paper, we will propose Horn clause computation as an underlying computational framework of DNA computer. Horn clause program is a subclass of the formulas of first order logic and has close relation to PROLOG language. The computational power of Horn clauses was discussed in (Tärnland, 1977) and it was shown that a finite set of Horn clauses is computationally equivalent to Turing Machine. Furthermore, it should be noted that Horn programs are adequate for representing nondeterministic computation. Thus, its parallel implementation with huge number of molecules might have possibility to overcome the computational power of conventional computers. It should also be noted that the clear logical semantics of Horn programs might enable us to accept it as a higher-level programming language of DNA computer. The aim of this paper is to propose an experimental method for implementing deduction with a subclass of Horn programs, called simple Horn programs. The computational power of this model is theoretically investigated and demonstrated with some applications to NP-complete problems.     

测量系统的质量监控技术

在制造业中,测量系统是保证和提高产品质量的必需技术手段。本文提出了将统计过程控制(SPC)技术应用于监控测量过程的思想,首先指出了测量过程与一般生产过程之间的差异,并建立了测量过程的一般模型;在此基础上,引入了监控测量过程的同分布原则和实现技术,并给出了实证分析。