旅游纪念品开发要素评价体系探讨

旅游纪念品开发过程中面临开发要素的取舍困境.通过对淘宝中的100件旅游纪念品进行抽象归纳,概括了15项旅游纪念品要素,通过6名同学的评分评价,筛选出6项核心要素,并根据属性划分为外观系统及功能系统2个系统,经过AHP分析法对评价体系指标进行权重赋予,以SD语义分析法制定参考评价标准,通过对蒙古包插线板旅游纪念品的评价,得出蒙古包插线板处于旅游纪念品开发要素综合评价Ⅱ级.经进一步计算,以方差分析法确定了评价体系中的6要素具有显著差异,以此证明在要素归纳过程中要素间的区分度具有明显差异,强化了评价体系的科学性.     

基于属性综合评价系统的科研立项评价模型

利用属性综合评价系统对科研立项评审进行综合评价.给出了科研立项评价的属性综合评价模型,可为科研基金管理单位进行合理的科研立项评价提供决策参考.与一般方法相比,属性综合评价模型严谨、精细,评价结果更合理、更可靠.应用实例表明了模型的科学有效性.     

基于属性数学模型的试卷质量综合评价方法

建立试卷质量综合评价的多级评价指标体系,利用属性数学模型提出试卷质量综合评价的多级属性综合评价方法.与一般评价方法相比,属性综合评价方法严谨、精细,克服了模糊评价法的不足,评价结果更合理、更可靠.应用实例的评价结果与实际相吻合,表明了方法的科学有效性.     

基于联系函数的系统综合评价模型及其应用

如何合理确定综合评价函数是系统分析评价中的关键问题.目前,所采用的方法是单纯从"数学变换"的角度建立综合评价函数,没有考虑研究对象的实际含义.因此,探讨不同的综合评价函数方法具有重要意义.根据综合评价对象的实际含义,定义了联系函数的概念,在此基础上,通过对研究对象进行两次集对同异反分析,建立基于联系函数的系统综合评价模型.实例分析表明:基于联系函数的系统综合评价模型评价结果准确、合理、可信,具有推广应用价值.     

一种循环修正的组合评价方法

提出了一种循环修正的组合评价方法.首先,对所研究的问题选取几种综合评价方法进行综合评价.其次,用Sperm an等级相关系数法检验几种综合评价法的一致性.进一步重复采用平均值法、Boarda法、C om peland法和模糊Board法对上述综合评价结果进行组合评价,直到标准差均收敛于0为止,得到最优的组合评价结果.最后以辽宁省2001年实际统计资料为例,对各地区的经济实力进行了综合评价和排序.     

基于数据包络分析的模糊综合评价方法

提出基于数据包络分析的模糊综合评价方法.在该方法中,对于综合评价体系中的量化指标,采用数据包络分析方法得到各评价单元的相对效率,再对相对效率进行模糊化计算,并与非量化指标一起进行模糊综合评价.利用数据包络分析的优化结果代替模糊评价中的专家评分,使模糊综合评价更具客观说服力,为一类既有客观数据,又有主观因素的复杂系统的综合评价提供新的研究思路.最后,采用该方法对出入境检验检疫系统的8个实验室进行综合评价.     

基于模糊数学和灰色理论的多层次综合评价方法及其应用

针对多层次指标体系的综合评价问题,在模糊综合评判方法的基础上,结合灰色理论,提出了一种新的定量综合评价方法—多层次模糊灰关联聚类分析综合评价方法.应用该方法对油气钻井技术进行综合评价,取得了满意的结果.     

平均值组合评价方法的优良性研究

综合评价方法广泛应用于教育、管理等很多领域.在一定程度上可以说综合评价的结果是决策的基础.因此选用好的综合评价方法非常重要.提出了一种新的综合评价方法:平均值组合评价方法.这种方法在一定程度上克服了单一综合评价方法的片面性.研究结果还表明所提的平均值组合评价方法在平方损失函数和兼容度两种标准下具有优良性.     

基于熵权的多级模糊综合评价在水质评价中的应用

基于河流水质整体性的特点,提出了基于多级评价和熵权模糊综合评价的评价方法.将河流各监测断面与整条河流水质评价作为一个多级模糊综合评价问题,通过熵权法直接从模糊关系矩阵获得各级评价的加权系数.以DO,COD_(Mn),NH_3-N作为评价因子,对黄河流域的水质状况进行多级综合评价.结果表明,基于熵权的多级模糊综合评价均衡考虑了各种评价因子,改进了超标比赋权过于强调评价因子浓度的特点,同时不仅能对各个监测断面的水质进行评价,也能对河流整体的水质进行综合评价.     

军校课堂教学质量的属性综合评价方法

运用属性数学理论对军校课堂教学质量进行综合评价.在建立军校课堂教学质量评价指标体系的基础上,给出了军校课堂教学质量的属性综合评价方法,可为军校教学管理部门以及本科教学评价进行合理地课堂教学质量评价提供决策参考.与一般评价方法相比,属性综合评价方法严谨、精细,评价结果更合理、更可靠.应用实例表明了方法的科学有效性.     

A contribution to the stochastic flow shop scheduling problem

This paper deals with performance evaluation and scheduling problems in m machine stochastic flow shop with unlimited buffers. The processing time of each job on each machine is a random variable exponentially distributed with a known rate. We consider permutation flow shop. The objective is to find a job schedule which minimizes the expected makespan. A classification of works about stochastic flow shop with random processing times is first given. In order to solve the performance evaluation problem, we propose a recursive algorithm based on a Markov chain to compute the expected makespan and a discrete event simulation model to evaluate the expected makespan. The recursive algorithm is a generalization of a method proposed in the literature for the two machine flow shop problem to the m machine flow shop problem with unlimited buffers. In deterministic context, heuristics (like CDS [Management Science 16 (10) (1970) B630] and Rapid Access [Management Science 23 (11) (1977) 1174]) and metaheuristics (like simulated annealing) provide good results. We propose to adapt and to test this kind of methods for the stochastic scheduling problem. Combinations between heuristics or metaheuristics and the performance evaluation models are proposed. One of the objectives of this paper is to compare the methods together. Our methods are tested on problems from the OR-Library and give good results: for the two machine problems, we obtain the optimal solution and for the m machine problems, the methods are mutually validated.     

Optimal Configurations of General Job Shops

In this paper we study the problem of minimizing the expected number of jobs in a single class general open queueing network model of a job shop. This problem was originally posed by Buzacott and Shanthikumar [2] and solved by them for a special case. We extend their work in this paper. We derive feasibility conditions that simplify the analysis of the problem. We show that the optimal configuration can be completely characterized when both the utilizations of the machine centers are high and there are a large number of servers at each machine center. We also derive conditions under which the optimization problem reduces to solving a concave or a convex program and provide conditions under which the uniform flow line and the symmetric job shop (or variants of these) are optimal configurations for the job shop.     

虚拟交易能否消除地理距离?——对淘宝平台中第三方服务商的空间计量分析

运用淘宝平台数据,构建空间计量模型,对第三方服务商的空间布局进行实证分析.研究发现,各省第三方服务商的分布对卖家具有空间依赖性.卖家数量对第三方服务商增长具有正向促进作用,卖家数量高度集聚的区域,第三方服务商也会集聚.第三方服务商与卖家之间的虚拟交易并未消除地理距离的作用.这一结论揭示了第三方服务商的空间分布规律,以及对卖家的空间依存关系,为地方政府制定电商发展政策提供了依据.     

Analysis of Job Flow-Time in a Job Shop

This paper is concerned with analytical determination of the mean and standard deviation of the job flow-time in a job shop in which the F.C.F.S. dispatching rule is in use. A hypothetical job shop is constructed to generate simulation results for comparison with the analytical results. Consistency of the results reveals the validity of the analytical model. It is also shown that the model is quite robust at a low level of shop load. So the model can be applied to analyze certain types of actual shops.     

A marketing-oriented inventory model with three-component demand rate dependent on displayed stock level (DSL)

This paper investigates a deterministic inventory system for single item with three-component demand rate. This rate is dependent on selling price, frequency of advertisement and displayed stock level (DSL) in a showroom/shop. Shortages, if any, are allowed and backlogged partially. The storage capacity of the showroom/shop is assumed to be limited (finite). Different scenarios and subscenarios of the system have been mentioned and developed. Some numerical examples are given to illustrate the model. A sensitivity analysis is performed graphically to investigate the effect of changes of the system parameters on the optimal average profit.     

Solving the serial batching problem in job shop manufacturing systems

This paper addresses the serial batch scheduling problem embedded in a job shop environment to minimize makespan. Sequence dependent family setup times and a job availability assumption are also taken into account. In consideration of batching decisions, we propose a tabu search algorithm which consists of various neighborhood functions, multiple tabu lists and a sophisticated diversification structure. Computational experiments show that our algorithm outperforms a well-known tabu search approach which is developed for solving the traditional job shop problem. These results also confirm the benefits of batching.     

An Ant Colony Optimization Algorithm for Shop Scheduling Problems

We deal with the application of ant colony optimization to group shop scheduling, which is a general shop scheduling problem that includes, among others, the open shop scheduling problem and the job shop scheduling problem as special cases. The contributions of this paper are twofold. First, we propose a neighborhood structure for this problem by extending the well-known neighborhood structure derived by Nowicki and Smutnicki for the job shop scheduling problem. Then, we develop an ant colony optimization approach, which uses a strong non-delay guidance for constructing solutions and which employs black-box local search procedures to improve the constructed solutions. We compare this algorithm to an adaptation of the tabu search by Nowicki and Smutnicki to group shop scheduling. Despite its general nature, our algorithm works particularly well when applied to open shop scheduling instances, where it improves the best known solutions for 15 of the 28 tested instances. Moreover, our algorithm is the first competitive ant colony optimization approach for job shop scheduling instances.     

A Simulation Study of Due-date Assignment Rules in a Dynamic Job Shop

This paper is a report of a simulation study that investigates a dynamic approach to scheduling jobs in a multi-machine job shop. The workload information of a job is used in different forms to evaluate the shop performance based on three measures: mean job lateness, percentage of tardy jobs and lateness variance. Different combinations of due-date assignment methods and sequencing rules are compared based on specific performance criteria. The results indicate that using the cumulative distribution function of workload information can yield a better performance than using a proportional function of workload information or ignoring shop congestion information. A few situations are identified in which workload information is not critical.     

A Model for Predicting the Performance of a Job Shop

This paper is concerned with presenting a method of modelling a job shop based on queueing theory. The model is very flexible and may be used to explore the relationships between the resources available in the shop (numbers and characteristics of machines, manpower levels and skills), the workload in the shop, the mode of operation of the shop (labour allocation and priority schemes) and the resulting level of congestion within the shop.A range of results is presented, based on the application of the model to a real environment, where good agreement with the observed performance was obtained.     

An iterated greedy metaheuristic for the blocking job shop scheduling problem

In this paper we consider a job shop scheduling problem with blocking (BJSS) constraints. Blocking constraints model the absence of buffers (zero buffer), whereas in the traditional job shop scheduling model buffers have infinite capacity. There are two known variants of this problem, namely the blocking job shop scheduling with swap allowed (BWS) and the one with no swap allowed (BNS). This scheduling problem is receiving an increasing interest in the recent literature, and we propose an Iterated Greedy (IG) algorithm to solve both variants of the problem. IG is a metaheuristic based on the repetition of a destruction phase, which removes part of the solution, and a construction phase, in which a new solution is obtained by applying an underlying greedy algorithm starting from the partial solution. A comparison with recent published results shows that the iterated greedy algorithm outperforms other state-of-the-art algorithms on benchmark instances. Moreover it is conceptually easy to implement and has a broad applicability to other constrained scheduling problems.