U型生产单元可重构设施布局方法研究

针对制造企业生产单元重新布局的决策问题,以U型生产单元为研究对象,以物料搬运成本、单元重构成本和生产时间损失成本之和最低为优化目标,考虑了重新布局的预算约束以及各类惩罚约束,建立了单元生产可重构设施布局决策的集成模型。设计了决策问题的遗传算法,以实验测试方式确定遗传算法的参数设置,避免陷入局部最优,并通过实例求解,验证了模型和算法的可行性。根据U型单元现有布局、下一周期订单数量和产品工艺要求等信息,利用上述模型和算法进行分析和优化,为企业进行新一轮的生产组织提供决策支持。     

MTO运营模式的订单交货期决策模型

承诺交货期过晚,没有时间优势,容易丢失订单;承诺交货期过早,企业超负荷生产,增加生产成本,可能出现亏损。针对基于订单式生产(MTO)的中小制造企业的客户订单交货期预报问题,综合考虑价格、订单需求量、提前完工和延期交货损失、加班、外协和损耗率因素等对任务完工期的影响,以收益最大化为目标,建立了订单任务交货期决策模型,并运用Matlab编程和计算工具对实例进行分析,验证了模型的有效性。模型对MTO生产企业交货期决策具有一定的参考意义。     

面向MES的外协企业决策支持系统研究

通过描述某机床制造企业机加工车间中外协管理的流程,指出制造执行系统(MES)的外协管理中存在外协企业多、外协企业评价指标不全以及指标权重分配不合理等一系列问题.依据层次分析法(AHP)的原理,运用基于新旧标度的层次分析法(AHP)进行外协企业的选择和决策,建立决策的数学模型,并最终以数据模型实现该决策支持系统.     

基于故障状态演化的租赁设备定周期多维护策略研究

为同时保证设备承租方对租赁设备的可用度,以及优化设备出租方的设备维护成本,提出了基于故障状态的定周期检测的多维护方式策略。首先,对租赁设备进行定周期检测,基于设备故障率来选择相应的维护策略,采取役龄回退的方式描述采取不同的维护策略后设备状态的恢复情况。其次,综合考虑周期维护成本、小修成本、惩罚成本和租赁延迟成本,建立了以出租方成本最低为目标的多维护策略优化模型。通过数学建模和数理统计方法,利用MATLAB仿真进行算例分析,将其与定周期单一预防性维护策略进行对比,证明了对租赁设备进行定周期多策略维护,其维护效果较单一预防性维护有较大的提升。     

MRPⅡ中生产计划与控制的缺陷分析及改进

分析了制造资源计划的生产计划与控制模型不适合"按订单生产"的因素,如"同类项归并"、"事后性能力约束"等.结合按订单生产方式的特点,提出了一种如何接收订单和计划生产的方法.根据订单产品的物料清单、工艺路线,结合工作中心的能力计划,预估能满足客户需求的程度,来安排自身的生产和外协,以提高对客户需求的响应能力.在此基础上,提出了一个面向客户订单的生产计划与控制系统框架.     

基于缩短物流多阶响应周期的LRP模型研究

建立了以缩短物流多阶响应周期和成本为优化目标的多目标设施定位-运输路线安排问题(LRP)模型。在模型的构建过程中，加入了对不同运输方式选择的变量，以满足用户对不同运输速度和成本的要求，并在成本计算中考虑了订货成本和分拣中心的固定成本。     

外协生产能力管理研究

以有业务外协的网络制造系统为背景,结合企业的综合能力与任务规模确定协作伙伴,并采用多目标优化进行订单分配;借鉴UPS及时跟踪技术并结合条形码技术提出了核心企业对协作企业任务完成情况的查询与跟踪的方法,从而实现企业的分布式生产能力管理.     

旧设备租赁的周期预防维护策略优化

针对具有一定年龄的旧设备租赁的特殊性,提出周期预防维护策略。该策略综合考虑设备的年龄、预防维护、小修以及惩罚机制等多种因素对成本的影响,建立以出租方维护成本最小化为目标的周期预防维护模型。经算例分析,研究表明,该方法是有效的,并且在实际维护过程中是易于操作的。     

基于规则的钢铁订单归并及炉料结构优化方法

针对订单生产型钢铁企业的组炉计划和原料配方优化问题,综合考虑钢铁订单的产品结构属性、加工过程特征和客户交货要求等多约束条件,提出了基于规则的订单归并及炉料结构优化方法.首先利用订单组批合炉冶炼规则,建立以炼钢余材量最小和拖期/提前惩罚最小为目标的订单归并优化模型;然后利用精炼阶段合金投料配方规则,建立以投入原料成本最小和合金元素目标成分偏差最小为目标的炉料结构优化模型;并分别设计了求解上述两模型的启发式微粒群算法.案例企业的实际数据验证结果表明,基于该方法形成的组炉计划和原料配方方案,能够在满足订单交货期的前提下有效地减少炼钢余材量,合理地降低铁合金等原材料的投入成本.     

不确定环境下多目标绿色供应链生产计划模型

针对近年来不断引发关注的工业环境污染问题以及制造业绿色供应链运营困难的问题,本文考虑分销商需求量、生产能力、成本和运输等参数为区间数,以供应链总成本最小、响应时间最短和碳排放量最少为目标,构建不确定环境下的多运输方式、多工厂、多分销商的绿色供应链生产计划模型。利用区间序关系和区间可能度定义给出不确定模型的清晰等价形式,并采用多目标规划方法对模型进行求解。最后通过算例分析可能度水平和区间变化率对企业决策的影响。研究表明,可能度水平较小或区间参数变化率较大时,会使生产条件变得更加宽厚,有利于企业降低供应链总成本。     

Generalisation of unidirectional loop layout problem and solution by a genetic algorithm

Unidirectional loop layouts (ULLs) are the preferred layouts in manufacturing systems owing to their relative low investment costs, high material handling elasticity and routing flexibility. Existing formulations of the unidirectional loop layout problem are concentrated on the arrangement of workstations under the assumption that the number and location of loading and unloading stations are known. In this study, the unidirectional loop layout problem is generalised by consideration of potentially attachable loading/unloading equipment to each workstation and releasing of the predetermined number of loading and unloading stations. Thus, more efficient and effective loop layout designs are allowed by eliminating some artificial restrictions. The present ULL model is generalised and a genetic algorithm is developed to solve the problem. Solutions obtained by the genetic algorithm outperformed those obtained by conventional methods. Additionally, comparisons of the generalised model with existing models on randomly generated test problems yielded encouraging results.     

A Material Handling System Selection Model

A technique is presented which can be used to design, or assist in the design of, an integrated materials handling system for a manufacturing facility or a major department of the facility. The developed procedure selects the materials handling equipment to be used to perform a given set of moves in order to miriimize the system's costs associated with the handling, assuming the plant arrangement remains fixed. To demonstrate the selection procedure, an example problem is presented. Since the method is heuristic, no claim of optimality is made.     

Availability consideration in the optimal selection of multiple-aspect RMS configurations

Machine availability has a profound influence on the performance of manufacturing systems. This paper extends a model for optimizing reconfigurable manufacturing systems (RMS) configurations with multiple-aspects to incorporate the effect of machine availability using the universal generating function (UGF). Two powerful meta-heuristic optimization techniques, namely genetic algorithms (GAs) and tabu search (TS), are used for optimizing the capital cost and system availability of the RMS configurations. The optimized configurations can handle multiple-parts and their structure is that of flow lines allowing paralleling of identical machines in each production stage. The various aspects considered in the RMS configurations include arrangement of machines, equipment selection and assignment of operations. A case study is presented and implementation of the optimization model is carried out using MATLAB software. The results of using both GAs and TS to solve the problem are then reported and compared for validation. Analysis of different cases of availability consideration including infinite and no buffer capacity is performed and results are compared to those obtained when machine availability is not considered. It has been shown that considering availability affects the optimal configuration selection and increases the required equipment. This increases the costs of the near-optimal configurations obtained especially in the case without buffers. The presented model can support the manufacturing systems configuration selection decisions at both the initial design and reconfiguration stages.     

制造企业责任转移机制模型研究

为了解决企业在经营过程中常常忽略过程控制而只考核最终目标,以及基于统计的考核指标不能对业务活动进行有效的事前约束问题,提出了一个责任转移机制模型。在责任、责任流等基本概念基础之上给出了企业责任转移和分配机制的原理、责任追踪的方法以及算法。通过加强过程控制、业务活动间协作,为经营过程整体目标的实现提供了一个新思路。最后通过实例对责任转移机制进行了分析。     

混合工作日历下批量生产柔性作业车间多目标调度方法

提出了一种混合工作日历下批量生产柔性作业车间多目标调度方法。考虑设备的混合工作日历约束,构建了以生产周期最短、制造成本最低为优化目标的批量生产柔性作业车间多目标调度模型。设计了一种带精英策略的非支配排序遗传算法(NSGA II)求解该模型。算法中,采用“基于工序和设备的分段编码”方式分别对工序和设备进行编码;采用“基于工序和设备的分段交叉和变异方式”进行交叉和变异操作,采用“遗传算子改进策略”保证交叉、变异后子代个体的可行性;解码操作采用“基于平顺移动的原理”和“基于工作日历的时间推算技术”推算工序的调整开始、调整结束、加工开始和加工结束时刻。最后,通过案例分析验证了所提方法的有效性。     

Production scheduling optimisation with machine state and time-dependent energy costs

The increase of energy costs specially in manufacturing system encourages researchers to pay more attention to energy management in different ways. This paper investigates a non-preemptive single-machine manufacturing environment to reduce total energy costs of a production system. For this purpose, two new mathematical models are presented. The first contribution consists of an improvement of a mathematical formulation proposed in the literature which deals and deals with a scheduling problem at machine level to process the jobs in a predetermined order. The second model focuses on the generalisation of the previous one to deal simultaneously with the production scheduling at machine level as well as job level. So, the initial predetermined fixed sequence assumption is removed. Since this problem is NP-hard, an heuristic algorithm and a genetic algorithm based on the second model are developed to provide good solutions in reasonable computational time. Finally, the effectiveness of the proposed models and optimisation methods have been tested with different numerical experiments. In average, for small size instances which the mathematical model provides a solution in reasonable computational time, a gap of 2.2% for the heuristic and 1.82% for GA are achieved comparing to the exact method’s solution. These results demonstrate the accuracy and efficiency of both proposed algorithms.     

Improved evolutionary algorithm for parallel batch processing machine scheduling in additive manufacturing

With the increasing prosperity of additive manufacturing, the 3D-printing shop scheduling problem has presented growing importance. The scheduling of such a shop is imperative for saving time and cost, but the problem is hard to solve, especially for simultaneous multi-part assignment and placement. This paper develops an improved evolutionary algorithm for application to additive manufacturing, by combining a genetic algorithm with a heuristic placement strategy to take into account the allocation and placement of parts integrally. The algorithm is designed also to enhance the optimisation efficiency by introducing an initialisation method based on the characteristics of the 3D printing process through the development of corresponding time calculation model. Experiments show that the developed algorithm can find better solutions compared with state-of-the-art algorithms such as simple genetic algorithm, particle swarm optimisation and heuristic algorithms.     

基于粒子群算法的半导体制造设备预维修调度

对半导体制造设备的预维修调度问题进行了研究,建立了半导体制造设备预维修调度的数学模型,分别提出了用于求解该问题的启发式方法和粒子群算法方案,阐明了两种算法方案的具体实现过程。通过对仿真实例进行计算和结果比较,表明了粒子群算法优于启发式方法,取得了不错的优化效果。     

Research on resources optimisation deployment model and algorithm for collaborative manufacturing process

Agility is the competitive advantage in the global manufacturing environment. It is believed that agility can be realised by networked manufacturing resource optimisation deployment. However, this is a challenge to us now. To solve this question, logical manufacturing unit and logical manufacturing process are proposed to decompose and model the networked manufacturing task, and networked manufacturing resources are organised and modelled based on physical manufacturing unit. During the deployment of manufacturing resources to the task, many factors should be taken into consideration. Of these, manufacturing cost, time and quality are the most important factors. In this paper, before these factors are considered, networked manufacturing resources pre-deployment is carried out to find the candidate manufacturing resources on the basis of manufacturing abilities. Then, resources optimisation deployment is modelled as a multi-objectives optimisation. This optimisation problem is solved based on genetic algorithm after transforming the multi-objectives optimisation problem to a single objectives optimisation problem. Although we may not find the optimal solution for the problem by genetic algorithm, the better and feasible solution is produced. Thus, this algorithm is efficient and can be applicable to practical problem. At last, an illustrative example is presented to show the application of the proposed algorithm.     

Collaboration of large equipment complete service under cloud manufacturing mode

This paper focuses on the need of the large equipment manufacturing industry to adapt collaborative operation to transform the industry to cloud manufacturing services and to solve the new problem of federal resources coordination in complete service operation. We systematically study federal resources cooperation under cloud manufacturing mode to complete a large complex project. The primary research contents are divided into four points. First, a system structure of cloud manufacturing service mode is presented. Second, a synergy logic framework from the global system perspective is designed based on generalised partial global planning. Third, a multi-level system coordination mechanism is established by integrating various methods, including the bidding game mechanism for enterprise external resources, the planning control mechanisms for enterprise internal resource and the global collaborative optimisation mechanism for enterprise global federal resources. Finally, a cloud manufacturing service platform for a typical enterprise is developed by combining theory with practice. The results can realise collaborative management in resource selection and configuration, service process planning control and service information feedback in cloud manufacturing service, as well as achieve overall synergy effect for the system.