面向全生命周期的大规模定制生产模式的研究

针对目前关于大规模定制的研究主要囿于产品族开发的现状,提出了从全生命周期的角度研究大规模定制生产模式的思路。通过分析单件定制、大规模生产和大规模定制三种生产模式之间的差别,形成了面向大规模定制的响应模式,总结出了设计和定制分离、延迟策略以及模块化产品族的特点。提出了面向DFX的产品族设计方法、包含需求配置和工程配置等在内的定制配置过程、基于制造单元的生产线布局规划过程、基于产品族结构和JIT策略的物流网络融合处理、面向并行装配线规划的设计结构矩阵处理算法等内容。     

即时顾客化定制的基本问题探讨

从生产方式变革的原因和实践出发,探讨了即时顾客化定制这一新的生产方式出现的必然性,分析了即时顾客化定制与其他生产方式的联系和区别,建立了实现产品即时交付模型,探讨了实行即时顾客化定制的新问题和赢得负时间的途径,提出了实施即时顾客化定制的策略.     

基于大批量定制生产的产品生成模式与优化模型

有关大批量定制生产的研究大多数还处于基本概念、基本原理和方法论阶段。为了给企业提供真正有效的解决方案,依照离散型制造系统生产机理,从对象维和过程维的二维角度,提出了基于大批量定制生产的通用结构模式,探讨了产品全寿命周期的业务流程优化方向,构建了大批量定制生产的优化数学模型。该模型对于离散型制造业具有普遍适用性。     

云制造中产品个性化定制研究综述

消费者对个性化产品购买需求的增加,对制造企业的定制生产能力提出了越来越高的要求。而现有制造企业存在的信息化水平不高、制造资源不足等问题,严重影响了制造企业向个性化生产转型的进程。针对上述情况,结合云制造概念及技术提出了云制造中产品个性化定制生产模式。为了更加深入地研究云制造中产品个性化定制的运行模式及阐述云制造平台在定制生产过程中的主要作用,从体系架构、服务流程、关键技术等方面对相关研究进行了回顾和介绍,然后列举了个性化定制生产模式在汽车、软件和服装行业的应用,最后对研究趋势进行了展望。分析表明,云制造平台汇聚全球的制造资源为个性化定制服务,能够有效解决单一制造企业实施个性化定制过程中面临的一系列困难,做到以较低的成本快速高效地满足消费者日益多样化、个性化的需求。     

MTO生产模式的交货期决策模型及其分析

通过对订单生产模式下产品提前或延期交付所引发的成本分析,以单位产品收益最大为目标构建交货期决策模型,并对模型进行最优性及交货期决策分析.分析结果表明在客户对提前交货没有限制且制造商没有额外成本产生时,承诺交货期应大于平均交货期;延期交付不需支付罚款时,平均交货期是最优的选择;同时考虑产品提前或延期两项成本时,应以平均交货期为基础,结合相关条款综合确定承诺交货期.     

人体假腿的个性化适应定制模式与设计策略

人体假腿是一种典型的面向个性化需要而进行大规模定制的产品.为了改进现有假腿的产品竞争力与个性化适应程度,提出了一种新的假腿产品柔性设计策略及其对应的“个性化适应定制(APC)”的产品生产模式.这种生产模式综合了大规模生产(MP)与完全个性化定制(PC)的优点,实现了产品在保证大规模生产的竞争力优势前提下对个性化核心功能的自动适应.通过实例,对基于这种APC生产模式与柔性设计策略的假腿竞争力进行了分析评价.这种设计方法与生产模式对具有个性化要求的类似产品具有参考应用价值.     

支持大批量定制的Web2．0技术

大批量定制正在成为制造业的主流生产模式．本文研究表明Web2．0技术特点与大批量定制的需求能够较好的吻合,例如,Web2．0技术能够帮助企业提高客户需求获取能力,以便为客户提供更好的个性化服务;Web2．0技术能够更有效地支持企业间的零部件资源共享,以便降低产品成本,缩短交货期;Web2．0技术能够支持标准协同建设,提高企业的协同能力,促进产品的模块化．     

大批量定制:未来制造业的主流生产方式

大批量定制生产方式能快速和低成本地向顾客提供个性化和定制化产品，因此正在成为制造业的主流生产模式，不同的行业以往的生产方式是不同的，但它们的发展方向却是相似的，有的是从过去的大批量生产模式转向大批量定制生产模式，如汽车工业和家电工业；有的是从过去的定制生产模式转向大批量定制生产模式，如船舶工业和工业汽轮机行业；有的是从过去的批量生产模式转向大批量定制生产模式，如机床工业和航空工业，当然，不同行业采用的大批量定制的深度是不同的。     

云环境下大规模定制中资源配置研究

针对传统大规模定制生产模式无法满足日益个性化的产品市场变化,导致产品无法形成生产批量,在生产过程中增加成本和时间的问题,结合云制造的背景环境,提出云环境下大规模定制产品的生产模式,并通过建立包含生产总时间、生产总成本和产品总质量的多目标优化函数模型,使用NSGA-Ⅱ算法对所建模型进行求解,对模式运行中的资源配置问题进行研究。最后通过航模发动机进行算例验证,证明所建模型可以得到解决云环境下大规模定制产品生产过程中资源优化配置问题的最优生产方案。     

面向大规模定制的供应链驱动模型的研究与应用

大规模定制是在高效率的大规模生产的基础上,通过产品结构和制造过程的重组,运用现代信息技术,新材料技术、柔性技术等一系列高新技术,以大规模生产的成本和速度,为单个顾客或小批量多品种市场定制任意数量的产品的一种生产模式,本文提出了面向大规模定制的推拉型供应链驱动模式,构建了以装配为分离点的供应链模型,并进行了案例研究。     

Robust scheduling in an advanced planning and scheduling environment

A new scheduling problem that appears in an advanced planning and scheduling (APS) environment is discussed. Under the precondition that all materials are available when needed, the problem is formulated as follows: min N i =1 ( D i - E i )/N subject to D i - C i S 0, for all i , where N is the number of customer orders arriving randomly at the shop during a certain period, D i is the estimated due-date for customer order i , E i is the due-date estimation time for customer order i and C i is the completion time for customer order i . Then, D i and C i are endogenous variables and E i is an exogenous variable. The ability to construct a flexible scheduling process that avoids the need to fix ongoing schedules is essential to all APS systems. Therefore, the concept of a due-date buffer is introduced which is expected to enable the production schedules for each customer order to be flexible at the beginning and gradually become fixed as the processing of the order progresses. A simulation-based scheduling algorithm using the concept of a due-date buffer is developed here and subsequently examined through a series of numerical experiments. The obtained computation results show that the performance of due-date buffers is outstanding with respect to complicated production processes having higher utilizations.     

Integrating order release control with due-date assignment rules

This study integrates order release control methods with due-date assignment rules and assesses its impact on the accuracy of inter-operation time estimation and performance of due-date assignment. The assessment is made by using an experimental design with three due-date assignment rules, three scheduling rules and three order release models. The three order release models are: (1) Basic model, in which three due-date assignment rules consider the order arrival time as the order release time; (2) Control model, in which three due-date assignment rules integrate with the order release control method developed here; and (3) Adjustment model, in which the control model integrates with the order release control adjustment developed here. Simulation results in this study indicate that integrating the order release control method with due-date assignment rules will significantly enhance not only the accuracy of interoperation time estimation, but also the performance of due-date assignment rules.     

A Comparison of Due-Date Selection Rules

In sequencing and scheduling models it is usually assumed that due dates represent exogeneous information. In many practical settings, however, due dates can be discretionary, or at least negotiable. Relatively few studies have incorporated discretionary due dates, and even then the rules proposed for due-date selection have seldom been developed from normative, analytic results. In this research we return to very basic scheduling models in search of fundamental insights and relationships that suggest guidelines for due-date selection in more complicated situations. We exploit some fundamental results from scheduling theory involving the single-machine model in order to compare three basic strategies for due-date selection.     

Due-date determination with resequencing

Traditional scheduling and due-date determination models assume that the production system is operating in a static and deterministic environment and that the system carries no workload at each scheduling epoch. In this research we consider a due-date determination model where the scheduler wishes to update the existing schedule when some new jobs have: arrived into the system. In this model, jobs are categorized as either "old" or "new" jobs, where the due-dates of the old jobs are treated as given parameters and those of the new jobs are decision variables. The objective is to minimize the maximum weighted tardiness penalty and the due-date assignment cost. The computational complexity of this model is analyzed, and an efficient algorithm is developed for an important special case.     

Multi-objective due-date setting in a make-to-order environment

This paper presents a new dual-objective problem of due-date setting over a rolling planning horizon in make-to-order manufacturing and proposes a bi-criterion integer programming formulation for its solution. In the proposed model the due-date setting decisions are directly linked with available capacity. A simple critical load index is introduced to quickly identify the system bottleneck and the overloaded periods. The problem objective is to select a maximal subset of orders that can be completed by the customer requested dates and to quote delayed due dates for the remaining acceptable orders to minimise the number of delayed orders or the total number of delayed products as a primary optimality criterion and to minimise total or maximum delay of orders as a secondary criterion. A weighted-sum program based on a scalarisation approach is compared with a two-level due-date setting formulation based on the lexicographic approach. In addition, a mixed-integer programming model is provided for scheduling customer orders over a rolling planning horizon to minimise the maximum inventory level. Numerical examples modeled after a real-world make-to-order flexible flowshop environment in the electronics industry are provided and, for comparison, the single-objective solutions that maximise total revenue subject to service level constraints are reported.     

Regression trees approach for flow-time prediction in wafer manufacturing processes using constraint-based genetic algorithm

Understanding the factors associated with the flow-time of wafer production is crucial for workflow design and analysis in wafer fabrication factories. Owing to wafer fabrication complexity, the traditional human approach to assigning the due-date is imprecise and prone to failure, especially when the shop status is dynamically changing. Therefore, assigning a due-date to each customer order becomes a challenge to production planning. The paper proposes a constraint-based genetic algorithm approach to determine the flow-time. The flow-time prediction model was constructed and compared with other approaches. Better computational effectiveness and prediction results from the constraint-based genetic algorithm are demonstrated using experimental data from a wafer-manufacturing factory.     

Optimal due-date assignment in a job shop†

This paper presents an analytical model for determining the optimal processing-time and number of operations multiples for the TWK and TWK + NOP due-date assignment methods in a dynamic job shop subject to restrictive assumptions on queue discipline and processing time distribution. The analytical results are compared with the experimental results obtained from simulation of a hypothetical job shop under various shop conditions. The close agreement of the results reveals the validity of the analytical model. In addition, the results show that the TWK + NOP method is more effective in minimizing the missed due-date cost in a job shop.     

Real-time batch sequencing using arrival time control algorithm

This paper proposes a new feedback control method for batch sequencing in process industries where meeting customer due-date is important. Specifically, short-term batch sequencing for Just-In-Time is solved using an arrival time control approach. An integral controller embedded in each order adjusts the arrival time in real-time using the feedback obtained from a fast-mode simulation of the batch processing system. The applicability of the proposed batch sequencing method for the Penn State University Creamery is studied. Comparison of due-date performance with commonly used dispatching rules is presented. The proposed method has also been tested for general batch processing systems and has been found to produce good, feasible schedules with significantly better performance than the dispatching rules in terms of due-date deviation minimization.     

Dynamic rules for due-date assignment

This paper presents two new dynamic due-date assignment rules which utilize shop congestion information. The new rules estimate job flowtime based on a sampling of recently completed jobs. These rules are compared with other established flowtime estimate models on the criterion of due-date performance via computer simulation. To evaluate the robustness of the rules, an experimental design with three different queue sequencing heuristics and two different shop balance levels was used. The results of this investigation clearly indicate that flowtimes from recently completed jobs provide very useful information for establishing effective due-dates in a job shop environment. In addition it is shown how the use of particular sequencing rules greatly increases the precision of flowtime estimates.     

Real-time production control of an FMS in a produce-to-order environment

This paper deals with real-time control of an FMS, operating in a produce-to-order environment, with machines subject to failure. A two-level production control system (PCS) is developed and examined. The proposed PCS incorporates three principles: no need for pre-planning procedures; separation between due-date and operational considerations; and full exploitation of process flexibility. In an extensive series of simulation experiments it was demonstrated that the proposed PCS leads to good results in both criteria: maximum throughput and minimum tardiness of orders. A comparison study between this PCS and one which is not based on separation between due-date and operational considerations, indicated the advantage of the former.