结构设计的决策方法研究

本研究揭示了结构设计的决策方法，在决策模型的约束条件下，不仅要考虑约束关系的模糊性，还要考虑结构效应、设计变量以及几何尺寸限制中存在的不确定性因数。这些不确定因数可以是模糊变量、随机变量，也可以是随机模糊变量等，从而使结构设计的决策方法最且有一般性，最后通过数值例题验证本研究建议的决策方法的有效性和可行性。     

六西格玛项目选择的TOPSIS方法

依据六西格玛管理理论和多属性决策理论,提出了一种六西格玛项目选择的TOPSIS方法。在分析六西格玛项目选择的特点的基础上,按照系统性、可行性、多目的性、实用性的原则,构建了项目选择的决策指标体系;给出了项目选择的TOPSIS方法的计算步骤;通过一个算例说明了给出方法的应用。     

基于TOC的业务外包决策模型研究

建立了业务外包决策问题的数学模型,阐述了标准约束理论下业务外包决策过程,指出了多瓶颈情形下标准TOC方法的不足,提出了集成作业成本法的改进模型,较好地解决了多瓶颈问题,分析了新模型制定短期决策和长期决策的侧重点.实践证明,新模型制定的业务外包决策比标准TOC方法模型更合理有效,企业获取的利润更大.     

基于前景理论的移动理财决策体验引导设计

目的研究决策体验引导的交互设计,减少用户认知成本,帮助用户进行移动理财决策。方法基于前景理论中关于决策者在风险决策过程中的心理和行为机制的研究,通过定性分析得出决策行为中的影响因素的价值与权重,提出了移动理财的决策体验引导设计模型,结合交互设计方法、流程、要素,对引导用户的决策行为进行研究。结论通过移动理财产品首页的设计案例,验证了基于前景理论的决策体验引导设计模型的可行性。在实现可用性目标的同时,也提升了用户在理财决策过程中的体验。     

基于Petri网原油处理短期生产计划可调度性分析

由于原油运作短期调度的复杂性,现有的建模过程会忽略一些关键约束,导致不可行解。为了避免这个问题,从控制理论的角度研究短期调度问题,将短期调度中的一个运作决策作为一个控制。基于这个观点,对厂区油罐的数量以及它们的容量、厂区油罐中不同类型原油的库存、管道的运输速率存在的情况,利用Petri网分析其可行性。最后,利用一个实例对短期调度的可行性条件进行验证。     

面向思维约束的产品造型概念设计

目的针对概念设计思维的发散特性,基于约束理论的思想流程,在产品造型概念设计过程中引入思维约束理论,解决方案设计繁琐及控制难度较大的问题。方法对思维约束在产品造型概念设计中的可行性进行分析,从用户的角度归纳个体差异化、群体公共化和功能技术化3类导向模式。然后详细论述思维约束在产品造型概念设计中的系统性、理念导向性、层次性和时序性,形成约束体系。结论面向思维约束的产品造型概念设计方法,由产品情景构建、产品概念定位约束形成概念约束环节,通过约束环节中约束要素的解构,进行造型方案设计。以案例应用进行验证,理论在降低造型概念设计思维流程的复杂性方面具有可用性,为产品造型概念设计提供参考。     

基于非线性双层规划的产品族低碳配置优化决策

在碳限额约束下,针对竞争市场中不同决策主体的产品族配置优化问题,剖析各决策主体间的博弈关系,提出一种基于Stackelberg博弈理论的产品族低碳配置主从关联优化理念与方法。在主从对策决策框架基础上,建立了主从关联优化的非线性0—1双层规划模型,模型的上、下层分别对应竞争市场中主导制造商与跟随制造商各自的产品族低碳配置优化问题,并构建模型求解的双层嵌套遗传算法。将该优化方法并应用于产品笔记本电脑的案例研究中,分别对考虑碳限额约束与不考虑碳限额约束情境下的优化结果进行比较。     

汽车维修服务站瓶颈工序的实时调度

在结合优化调度理论和约束理论的基础上,从最小化目标、机器环境、加工特征和约束几方面分析了汽车维修服务站瓶颈工序的实时调度问题的特征,建立了对应的数学模型。根据问题特性,设计了包含复合动态分派规则的启发式调度算法。以实例分析验证了算法的可行性,仿真结果展示了所用算法在优化目标函数值上的优越性和计算时间的可行性。     

基于组合权重的灰色关联理想解法及其应用

引入灰色系统理论,对传统理想解法(TOPSIS)进行了拓展,提出了一种基于组合权重的灰色关联理想解法(GC-TOPSIS).利用AHP和熵值法对决策指标进行组合赋权,依据灰色关联分析理论,以灰色关联度为决策单元构造GC-TOPSIS模型,最后通过一个供应商选择的实例验证了方法的有效性和可行性.     

约束理论在回收逆向物流中的应用

将约束理论应用于回收逆向物流中,以有效产出作为决策目标,构建了一个逆向物流回收配送整合决策模型.针对模拟算例,通过对比基于TOC的决策模型和基于成本最小化的传统决策模型的运算结果,说明了约束理论在解决这类问题中的有效性.     

An improved algorithm for optimizing product mix under the theory of constraints

One of the most important decisions made in production systems is determining the product mix in such a way that maximum throughput would be obtained. Several algorithms to determine the product mix under the theory of constraints (TOC) have been developed. In this paper, the inefficiency of the traditional TOC algorithm in handling the multiple bottleneck problem is discussed through an example. Then, the latest algorithm and its disadvantages will be discussed and an improved algorithm, which is much more efficient and can reach the optimum solution with considerable speed, will be presented. Finally, the improved algorithm and the integer linear programming (ILP) method will be compared with each other through the example.     

Tabu search-based algorithm for the TOC product mix decision

The theory of constraints (TOC) is a management philosophy that maximizes profits in a manufacturing plant with a demonstrated bottleneck. The product mix decision is one application of TOC that involves determination of the quantity and the identification of each product to produce. However, the original TOC heuristic is considered to produce unrealizable solution when a manufacturing plant has multiple resource constraints. This paper presents a tabu search-based TOC product mix heuristic to identify optimal or near optimal product mix for small problem instances under conditions where the original TOC heuristic failed. The tabu search-based TOC product mix heuristic is further used to solve large problem instances typical of practical manufacturing scenario. The experimental results for small to medium size problem show that the tabu search-based TOC heuristic compares favourably with those of optimal methods. Large size problems for which optimal methods have not been established in terms of feasibility in computation times were also solved in reasonable times with good quality solutions, thus confirming that the proposed approach is appropriate for adoption by production planners for the product mix problem in the manufacturing industry.     

TOC会计

主要介绍TOC的基本原理、TOC会计以及产品组合。     

When less is better: Insights from the product mix dilemma from the Theory of Constraints perspective

Perhaps due to its origins in a production scheduling software called Optimised Production Technology (OPT), plus the idea of focusing on system constraints, many believe that the Theory of Constraints (TOC) has a vocation for optimal solutions. Those who assess TOC according to this perspective indicate that it guarantees an optimal solution only in certain circumstances. In opposition to this view and founded on a numeric example of a production mix problem, this paper shows, by means of TOC assumptions, why the TOC should not be compared to methods intended to seek optimal or the best solutions, but rather sufficiently good solutions, possible in non-deterministic environments. Moreover, we extend the range of relevant literature on product mix decision by introducing a heuristic based on the uniquely identified work that aims at achieving feasible solutions according to the TOC point of view. The heuristic proposed is tested on 100 production mix problems and the results are compared with the responses obtained with the use of Integer Linear Programming. The results show that the heuristic gives good results on average, but performance falls sharply in some situations.     

Optimizing the multiple constrained resources product mix problem using genetic algorithms

The theory of constraints (TOC) is s a management philosophy for maximizing throughput. Since its introduction, many have criticized it as being inefficient when multiple constrained resources exist. The application of the five steps contained in TOC have been criticized by some researchers on the grounds that the application of five steps of TOC to the product mix decision leads to implicit or unrealizable solutions when multiple resource constraints in a plant exist. This paper views TOC as a management philosophy and a genetic algorithm-based TOC procedure is presented for solving combinatorial problems encountered in practice which cannot be solved using linear-integer programming or similar techniques. For smaller size problems, the results of the proposed procedure are compared with results of optimal methods published in the literature. The results are encouraging and therefore support the use of the proposed approach in an industrial setting.     

Cyclic optimisation for localisation in freeform surface inspection

Over the last decade, several papers have been published on the contribution of the Theory of Constraints (TOC) and Throughput Accounting to product mix decisions. In these papers, the quality of the TOC-based approach to generate good or even optimal solutions is assessed with very different results, particularly when compared with other product mix decision tools. It is the objective of this article to expose the often, not explicitly revealed, premises that are mandatory for generating optimal product mix decisions using a TOC-based approach. By analysing several examples, which are modifications of one and the same basic example, premises on the solution space and the objective function are examined and conclusions made, which are shown in a checklist at the end of the paper. If more than one binding multi-product constraint occurs, optimal product mix decisions can only be derived in special cases. Also, the assumption of integer solutions may lead to non-optimal TOC-based solutions. Furthermore, a correct cost allocation is mandatory, even if sometimes not easily conductible. In particular, taking into account only (all) material costs may lead to wrong decisions. Finally, it is shown that discontinuities in the objective functions deriving, for example, from price discounts, may affect the product mix decision and require special modifications of the TOC-based approach.     

Unidirectional AGV guidepath network design: A heuristic algorithm

In an AGV system, the design of the guidepath network is one of the most important factors that determine the system effectiveness. Many alternative guidepath design schema have been introduced in the literature. However, the methods generally address the static production environment where the product mix or machine routings are assumed to be stable over time. In today's dynamic production systems with small lot sizes and short product cycles, the assumption of an unchanging product mix over an extended period is unrealistic in some situations. Considerable system inefficiency can be introduced when a network designed under the assumption of a stable product mix is used when indeed the product mix has changed. To avoid this kind of hidden source of inefficiency, what is needed is a tool that can recognize when a previously designed network is inappropriate for a new product mix and then uses the new product routing information to reconfigure a new network appropriate for the new production condition. A heuristic algorithm for the design of AGV guidepath in an environment with changing product mix is proposed here. The algorithm not only makes it possible to adapt the system network as the product mix changes, but also produces new designs at reasonably short computational time.     

TOC-based performance measures and five focusing steps in a job-shop manufacturing environment

Theory of constraints (TOC) views a company as a set of interdependent processes working in harmony to achieve the profit goal of the company as a whole, and thus it emphasizes total system performance over localized measures to guide operational decisions. This paper demonstrates the usefulness of employing TOCbased global performance measures to make operational-decisions (e.g. product mix, continuous improvement, inventory management, production planning and scheduling) to strengthen the internal supply chain in a relatively complex manufacturing environment, i.e. a job shop. An ARENA-based simulation model is presented and a number of scenarios are discussed that provide insights regarding the characteristic features of TOC, such as goals and necessary conditions, performance measures, five-focusing steps for continuous improvement, and drumbuffer-rope scheduling. These insights will assist managers in making important decisions regarding approaches to successful TOC implementations, and will provide academics with a broad range of future research opportunities.     

Developing assembly line layout for delayed product differentiation using phylogenetic networks

Effective formation of product platforms helps adapt to product demand changes and decrease time-to-market and lead time. The product platform groups the core elements of product family members into a common module used to derive different product variants by combining it with different components. A new delayed product differentiation (DPD) platform network model, which applies median-joining phylogenetic networks (MJPN), is proposed. It is used for forming product platforms and determining the assembly line layout of modular product families. The MJPN is traditionally used for DNA sequences’ mapping, analysis, clustering and tracing evolutionary trends. The concept of assembly/disassembly modular platforms, whereby both assembly and disassembly of components are used to derive the final product variants from the platform, is utilised. The proposed model determines the required number and composition of a product platform and defines the DPD points. The developed dynamic assembly/disassembly platforms enhance routing and product mix flexibility due to having different platforms that can be used to produce the same product variant. A family of household kettles is used to demonstrate the application of the proposed model. A metric is presented for determining the effectiveness of a given platform in delaying the product differentiation, hence increasing the efficiency of mass customisation. The proposed metric, applied to the case study, demonstrated that the proposed platform formation model using MJPN is more capable of postponing the product differentiation point.     

Weighted least-square estimation of demand product mix and its applications to semiconductor demand

Estimation of demand product mix is important for effective production plans. Unlike most research in the literature where the product mix is either given or treated as a decision variable in optimization of the production efficiency, this paper focuses on the product mix itself and how to estimate it from the market demand. With more accurate information on the demand product mix, aggregate production plans for product families can be disaggregated into quality detailed plans for individual product items. In this paper, least-square estimates of demand product-mix proportions are first derived. To take into account the effect of the product life cycle, dynamic weighting schemes are then developed to improve the accuracy of the product-mix estimates. For applications, we concentrate particularly on semiconductor demand where new generations of semiconductor products emerge at the pace of every six months, as manifested by the celebrated Moore's laws. The proposed methodologies will be tested with simulated DRAM demands and actual semiconductor demands of different technology generations.