装配式VMI 系统不同决策模式下收益共享研究

以装配系统和供应商管理库存为研究背景,构建非合作分散决策系统模型并发现其效率不足.在此基础上,分别提出分散决策模式和合作决策模式下的收益共享机制.研究表明,两种决策模式下的机制均可最大化系统效率;给定参与者利润在不同机制中相等,在一定条件下,其讨价还价能力空间在不同机制中具有相似的特征;第1种机制可能产生收益分配不公,但在第2种机制中可有效避免.     

先进制造模式下的MES应用与发展趋势

我国制造业正处于转型和提升阶段,其鲜明特征包括:由产能型转向敏捷型的竞争方式转变、产业集群模式、企业目标价值体现由产品到核心制造能力的转变。作为当前和今后制造业企业信息化自动化建设的重点和热点,MES的发展必须适应这一变化,在集成基础上更应突出其智能型和可重构能力。本文重点提出并研究了MES三个层次的提升路线:基于产销一体化的敏捷制造MES(AMES)、基于设计制造一体化的协同MES(CMES)、面向智能制造的核心制造能力系统(CMCS)。     

共享服务模式下WEB框架的设计与应用

本文将研究一种共享式服务框架（SSF）。SSF运用MVC理论，基于struts成熟框架模型提供框架的二次封装。它使公共组件模块化，提供统一的调用接口，使用xml对上层模块进行配置，实现软件开发的松耦合。与此同时，使标准的web风格的共享，提高表示层的可重用性．缩短了软件的开发周期。     

汽车零件制造企业物料配送模式优化

传统的汽车零件制造企业的物料配送模式一般都比较落后,需要比较冗余的物料拉动机制,物料配送数量的确定更是依赖于工人的经验。为了实现精确高效的物料配送,设计了一套优化的物料配送模式,并针对优化过程中的需要解决的信息共享问题提出了一套解决方案。     

共享服务模式下WEB框架的设计与应用

本文将研究一种共享式服务框架(SSF)。SSF运用MVC理论,基于struts成熟框架模型提供框架的二次封装。它使公共组件模块化,提供统一的调用接口,使用xml对上层模块进行配置,实现软件开发的松耦合。与此同时,使标准的web风格的共享,提高表示层的可重用性,缩短了软件的开发周期。     

基于遗传算法的网格制造资源优化选择

制造资源的选择和优化是制造网格应用过程中的关键问题.本文通过对已有制造资源搜索方法进行分析,指出其存在的局限性;在对资源选择问题进行数学描述的基础上,提出了一个基于遗传算法的制造资源组合优化模型,并对该模型的算法进行了设计;最后,给出了一个典型算例,以说明该模型和算法的有效性.     

关系知识表达模式及在专家系统中的应用

采用关系模式表达知识，可以利用当前流行的关系数据库管理系统（ＲＤＢＭＳ），将专家系统与ＲＤＢＭＳ上建立的管理信息系统，决策支持系统，办公自动化系统等有机结合，有利于知识的管理、存储和利用。并可在大量的管理信息，决策支持信息以及办公自动化信息中发现知识，使信息与知识共享。本文研究用关系模式表达知识的方法，并在一分类专家系统中利用ＲＤＢＭＳ强大的数据处理能力实现知识推理。     

敏捷制造中制造资源选择问题的遗传算法

对敏捷制造系统重构中的制造资源选择问题进行了分析,建立了数学模型,提出了一种适合求解该问题的遗传算法。该算法与解决同类问题的已有算法相比,编码方案和遗传算子均比较简单。实验结果表明,遗传算法在解的质量、稳定性和收敛速度方面具有优良性能。     

财务共享模式下的会计信息化研究

新时代背景下,财务共享模式不断发展,会计工作从传统人工转变为信息化模式,这导致会计复杂程度不断提升,同时也对会计人员的技能和素质方面提出新的要求。本文分析了财务共享模式下会计信息化的应用实践及出现的问题,提出在财务共享模式下适合会计信息化发展的优化方案,并对解决方案效果进行分析,以此为会计信息化建设提供参考、借鉴,提高信息技术在企业中的应用水平。     

Tool requirements in manufacturing systems under dynamic tool sharing

This paper addresses a tooling problem in automated manufacturing systems under dynamic tool assignment policy. In this type of systems, while parts stay on a machine all through its operations, the tools required (but not available on the machine) are delivered in time for requirements. The system performance is restricted to a great extent by tooling constraints. This paper presents an analytical method to determine tool requirement levels under dynamic production environment. The appropriate tool duplication levels are determined by trading off tool duplication levels and tool waiting time. Simulation studies are used to verify the performance of the proposed analytical model.     

Simulation modeling and analysis of tool sharing and part scheduling decisions in single-stage multimachine flexible manufacturing systems

This paper focuses on a simulation-based study of tool sharing problem in single-stage multimachine Flexible Manufacturing Systems. Three different scenarios are considered for investigation. A simulation model has been developed for each of these scenarios. A number of scheduling rules are incorporated in the simulation models for the decisions such as tool request selection and part launching in the context of tool sharing environment. The performance measures evaluated are mean tardiness, conditional mean tardiness and mean flow time. Based on the analysis of the simulation results, the best possible scheduling rule combinations for part launching and tool request selection have been identified for the three scenarios.     

Equipment replacement decisions and lean manufacturing

Traditional manufacturing systems are built on the principle of economies of scale. Here, the large fixed costs of production are depreciation-intensive because of huge capital investments made in high-volume operations. These fixed costs are spread over large production batch sizes in an effort to minimize the total unit costs of owning and operating the manufacturing system. As an alternative to “batch-and-queue,” high-volume, and inflexible operations, the principles of the Toyota Production System (TPS) and lean manufacturing have been widely adopted in recent years in the US [1, 2, 3 and 4]. In this paper, we illustrate an equipment replacement decision problem within the context of lean manufacturing implementation. In particular, we demonstrate how the value stream mapping (VSM) suite of tools can be used to map the current state of a production line and design a desired future state. Further, we provide a roadmap for how VSM can provide necessary information for analysis of equipment replacement decision problems encountered in lean manufacturing implementation.     

Integration of strategic and tactical decisions for vendor selection under capacity constraints

We examine a generalized vendor selection problem of a multi-store firm where the goal is the simultaneous determination of (i) the set of vendors the firm should work with and (ii) how much each store should order from the selected vendors. In addition to the typical costs associated with vendor selection and delivery between the vendors and their assigned stores, we explicitly consider the inventory-related costs and decisions of the stores. We emphasize the relationship between facility location applications and the problem at hand, and we propose an integrated vendor selection and inventory optimization model. Also, arguing that our model creates a venue for precise incorporation of realistic capacity constraints, we model throughput and dispatch capacities, explicitly. The model is a challenging mixed integer nonlinear program for which we propose an efficient solution approach that relies on Generalized Benders Decomposition (GBD).     

Semimyopic measurement selection for optimization under uncertainty

The following sequential decision problem is considered: given a set of items of unknown utility, an item with as high a utility as possible must be selected ("the selection problem"). Measurements (possibly noisy) of item features prior to selection are allowed at known costs. The goal is to optimize the overall sequential decision process of measurements and selection. Value of information (VOI) is a well-known scheme for selecting measurements, but the intractability of the problem typically leads to using myopic VOI estimates. In the selection problem, myopic VOI frequently badly underestimates the VOI, leading to inferior measurement policies. In this paper, the strict myopic assumption is relaxed into a scheme termed semimyopic, providing a spectrum of methods that can improve the performance of measurement policies. In particular, the efficiently computable method of "blinkered" VOI is proposed, and theoretical bounds for important special cases are examined. Empirical evaluation of "blinkered" VOI in the selection problem with normally distributed item values shows that it performs much better than pure myopic VOI.     

Knowledge sharing and investment decisions in information security

We study the relationship between decisions made by two similar firms pertaining to knowledge sharing and investment in information security. The analysis shows that the nature of information assets possessed by the two firms, either complementary or substitutable, plays a crucial role in influencing these decisions. In the complementary case, we show that the firms have a natural incentive to share security knowledge and no external influence to induce sharing is needed. However, the investment levels chosen in equilibrium are lower than optimal, an aberration that can be corrected using coordination mechanisms that reward the firms for increasing their investment levels. In the substitutable case, the firms fall into a Prisoners' Dilemma trap where they do not share security knowledge in equilibrium, despite the fact that it is beneficial for both of them to do so. Here, the beneficial role of a social planner to encourage the firms to share is indicated. However, even when the firms share in accordance to the recommendations of a social planner, the level of investment chosen by the firms is sub-optimal. The firms either enter into an “arms race” where they over-invest or reenact the under-investment behavior found in the complementary case. Once again, this sub-optimal behavior can be corrected using incentive mechanisms that penalize for over-investment and reward for increasing the investment level in regions of under-investment. The proposed coordination schemes, with some modifications, achieve the socially optimal outcome even when the firms are risk-averse. Implications for information security vendors, firms, and social planner are discussed.     

Topology optimization of continuum structures under hybrid additive-subtractive manufacturing constraints

Structural and Multidisciplinary Optimization - Additive manufacturing (AM) makes it possible to fabricate complicated parts that are otherwise difficult to manufacture by subtractive machining....     

Simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection

Tolerance specification is an important part of mechanical design. Design tolerances strongly influence the functional performance and manufacturing cost of a mechanical product. Tighter tolerances normally produce superior components, better performing mechanical systems and good assemblability with assured exchangeability at the assembly line. However, unnecessarily tight tolerances lead to excessive manufacturing costs for a given application. The balancing of performance and manufacturing cost through identification of optimal design tolerances is a major concern in modern design. Traditionally, design tolerances are specified based on the designer’s experience. Computer-aided (or software-based) tolerance synthesis and alternative manufacturing process selection programs allow a designer to verify the relations between all design tolerances to produce a consistent and feasible design. In this paper, a general new methodology using intelligent algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) for simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection is presented. The problem has a multi-criterion character in which 3 objective functions, 3 constraints and 5 variables are considered. The average fitness factor method and normalized weighted objective functions method are separately used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed.     

考虑碳限额的制造/再制造混合系统生产优化决策

针对碳限额交易机制下制造/再制造混合系统生产决策问题,以最大化总利润和最小化碳排放量为目标,考虑客户需求差异以及碳减排技术投入成本和收益的影响,建立多周期产品制造/再制造混合系统生产决策优化模型.根据模型特点,结合多种群协同进化思想,设计一个多种群混合布谷鸟算法(multi-population hybrid cuck...     

Web服务平台下基于遗传算法的制造资源服务选择*

针对基于Web的制造资源服务平台中的制造资源服务选择优化问题,建立了制造资源的质量属性模型,提出了一种基于质量的遗传算法。该算法设计了一种资源—任务关系矩阵编码方式;选择、交叉、变异等遗传操作只对矩阵主对角线上的任务位进行;适应度函数的设计采用制造资源服务组合的质量属性来描述。应用MATLAB遗传算法工具箱编程验证了该算法的合理性和实用性。