基于主从博弈的两部门并行系统的效率评价

本文研究了一类典型并行系统的效率评价问题：（1）决策单元由两个并行的子单元组成;（2）在整个系统中,某一子单元居于主导地位,另一子单元居于从属地位;（3）两个子单元之间存在部分共享的投入资源,且无法明显区别该资源在不同子单元之间的分配比例。在分析决策单元整体效率及内部子单元效率的基础上,基于主从博弈思想,提出一种能同时确定系统整体效率及内部子单元效率的评价方法,该方法能够在评价系统效率的同时,实现共享资源的有效分配。最后,采用一个实例分析说明了所提方法的合理性和有效性。     

网络化制造系统的建立与运行控制策略

针对网络化制造的组织、管理、信息、决策与控制方式,提出了半分散结构多智能体的网络化制造系统的建立与进行控制的体系和方案,探索了网络化制造平台、建模方法、资源配置、协同规划调度、异地监控等技术路线和关键技术,在适应国情的基础上,突出了系统的实用性和可操作性.     

基于3D打印技术的备件生产与运输协同调度问题研究

基于3D打印的智能制造技术已经成为推动备件供应链转型升级的重要手段。在此背景下，本文研究“随时需要随时生产+即时配送”新型模式下的计划性维修备件的供应链协同问题。生产与运输协同调度问题在制造业领域普遍存在，属于NP-Hard问题。本文研究带时间窗的生产与运输协同调度问题，建立混合整数规划模型，根据Dantzig-Wolfe分解原理分别建立主问题和子问题数学优化模型，并采用改进的分支定价算法进行求解。在求解过程中，首先，构造可行解，基于可行路径调用CPLEX优化软件对主问题求解；其次，针对子问题的求解，根据研究问题的属性设计占优原则和加速策略，并对求得的非整数解进行分支；最后，通过对多组规模算例进行测试，数值实验结果表明：1)验证了所建立模型和改进算法的有效性；2)通过求解时间对比可知所使用加速策略能够将算法效率提高10倍左右；3)通过将生产和运输协同决策与实际运作中的分阶段决策结果对比，本文设计的方案可以将目标函数值平均优化50.33%。本研究不仅能够有效解决新型备件生产模式下的生产与运输协同调度问题，而且可以为企业实际运营决策提供科学依据，实现降本增效的目标。     

考虑需求波动的单元装配系统构建问题的多目标模型

顾客个性化需求的增强导致企业需要应对动态变化的市场需求，在非确定性需求的环境下提升生产效率和应变能力。日本式单元装配模式以其较高的生产效率和柔性已经逐步成为应对多品种小批量需求的生产方式。由于现有研究多集中于确定性需求，本文针对日本式单元系统构建问题的特点，考虑需求波动的情境下流水线装配向单元装配系统转化问题，构建单元装配系统转化的多目标优化模型。该模型以最小化总加工周期的期望和方差为目标，决策单元装配系统的构建方案，以提升需求波动情境下系统的稳定性和对需求波动的应变能力。根据问题特点设计基于NSGA-Ⅱ的优化算法求解大规模问题，并结合文献数据说明了模型的应用。数值算例验证了方法的有效性，结果表明考虑需求波动情境下的单元系统构建可以提升生产系统应对波动需求的能力以及稳定性。     

服务型制造网络的Holon协同需求评价方法

依据复杂网络理论分析服务型制造网络Holon协同需求问题，给出一种考虑以直觉正态模糊数表示且多Holon协同的服务型制造网络协同需求评价方法。首先，在区分Holon复合协同和递归协同基础上构建服务型制造网络协同需求评价指标体系；其次，考虑基于网络结构特征的Holon相似稳定性，根据服务型制造网络节点相似性测度分析Holon协同需求特征，并测算不同相似性测度修正的Holon网络结构熵；再次，建立面向服务型制造网络和Holon的有专家信息双向触动反馈机制，反馈包括评价指标和网络协同状况，反馈Holon信息包括基于服务型制造网络演化方向的Holon间协同需求建议和基于服务型制造网络现有状态的网络连通性Holon协同需求建议；最后由INFCWA（INFCWA_R）算子或INFCWG（INFCWG_R）算子分别对复合协同和递归协同评价信息集结并得出评价结论。方法设计过程通过例证分析说明根据服务型制造网络目标控制的Holon协同需求评价改进了群组评价效率。     

基于遗传算法的虚拟企业协同资源优化问题研究

面向复杂零件的协同制造,以工艺流程为核心将协同制造任务进行分解,并有效利用"逻辑制造单元"和"逻辑加工路线"等概念描述复杂零件的协同制造任务,最终目标是形成基于复杂零件工艺流程的、可支撑异地协同生产的加工路线。对复杂零件协同制造的制造资源优化配置问题进行了数学分析和描述,建立了问题的目标函数与约束条件。本文以加工时间、运输费用和加工质量作为目标,约束条件包括顺序约束、释放期约束、时间约束、交货期约束、成本约束和质量约束,最终将资源优化配置问题归结为多目标优化问题,并利用遗传算法进行求解,得到了较为满意的结果。通过实例分析,将模型应用于某型号发动机叶片协同制造,说明采用本论文的模型可以有效解决复杂零件协同制造的资源优化配置问题。     

一种有限信息共享的全局寻优供应链双边协同计划方法

针对有限信息共享条件下,供应链协同计划难以实现计划调整的收敛性和达到全局最优的问题,提出一种具有多点搜索舜口概率搜索特征的协同计划方法.将协商中交流的备选计划及其所产生的成本变化信息融入到计划调整算法中.以遗传算法的选择、交叉和变异过程为调整机制,将算法的迭代过程与计划协商过程结合在一起.在确定上下游企业能力约束多产品批量计划模型和最小累积需求求解方法及其模型的基础上,分析了协同计划过程中的编码、适应度函数、初始计划组生成和选择、交叉、变异算法等主要要素,并给出协同计划流程.通过仿真实验验证了该方法的有效性和相对的优越性.     

多群体并行遗传算法在动态车间调度中的应用

生产调度对企业的生产作业过程具有重要的作用。有效的调度方法和优化技术是实现先进制造和提高生产效益的基础和关键。本文论述利用多群体并行遗传算法可满足动态车间调度的应用,采用一种特殊构造遗传编码方法采改进遗传算法,提供有效的最优化查询。利用MATLaB工具以实例证明该算法的有效性。该算法特别适合于job-shop调度问题。     

高端装备智能制造价值链的生产自组织与协同管理：设计制造一体化协同研发实践

新一代信息技术环境下，高端装备制造企业正在积极寻求和推动产品研制过程实现全球更大范围的物理空间、社会空间和信息空间中的数据串联、信息融合、业务协同和价值活动整合。作为一种以数据与模型集成融合为核心的新型制造服务模式，智能制造价值链成为数字化时代高端装备制造企业打造全新竞争优势的战略方向和核心途径。本文结合高端装备技术与管理深度融合的设计制造研发实践，探讨了高端装备设计制造一体化协同管理架构，从价值场景与工作场景两个维度揭示高端装备设计制造一体化协同流程的生成过程，分析了面向组织协同、资源协同、协同决策的生产自组织构建过程，为高端装备的协同创新和高质量研制提供理论方法支撑。     

冷轧生产批量计划与调度问题模型及算法

对冷轧生产系统特点进行分析,将冷轧生产批量计划与调度问题抽象为多阶段、多品种带有中间库的批量计划与调度问题。针对该问题建立了数学模型,通过对库存成本和调整成本惩罚系数的控制可以协调库存水平和调整次数的关系。对所建立的模型,提出了基于二进制粒子群优化与局部搜索的混合求解算法。最后,通过对企业实际生产数据的计算和分析,验证了模型和算法的可行性和有效性。     

Scheduling in an assembly-type production chain with batch transfer

This paper addresses a three-machine assembly-type flowshop scheduling problem, which frequently arises from manufacturing process management as well as from supply chain management. Machines one and two are arranged in parallel for producing component parts individually, and machine three is an assembly line arranged as the second stage of a flowshop for processing the component parts in batches. Whenever a batch is formed on the second-stage machine, a constant setup time is required. The objective is to minimize the makespan. In this study we establish the strong NP-hardness of the problem for the case where all the jobs have the same processing time on the second-stage machine. We then explore a useful property, based upon which a special case can be optimally solved in polynomial time. We also study several heuristic algorithms to generate quality approximate solutions for the general problem. Computational experiments are conducted to evaluate the effectiveness of the algorithms.     

大规模集成电路预烧作业中分批排序问题的数学模型

分批排序(Batch Scheduling)是在半导体生产过程的最后阶段提炼出来的一类重要的排序问题。单机分批排序问题就是n个工件在一台机器上加工,要将工件分批,每批最多可以同时加工B个工件,每批的加工时间等于此批工件中的最大的加工时间。Skutella^[8]1998年把平行机排序的P｜｜∑ω_jC_j和R｜｜∑ω_jC_j表述成二次的0-1整数规划,得到一些令人满意的结果;国内罗守成等^[9]、张倩^[10]给出了单机排序问题1｜｜∑ω_jC_j的数学规划表示,对于用数学规划来研究排序问题是一个很有意义的进展。本文首先介绍总完工时间和最小的带权单机分批排序问题1｜B｜∑ω_jC_j,然后将1｜B｜∑ω_jC_j表示成数学规划的形式,并且用数学规划中的对偶理论证明了SPT序是其特殊情况1｜B=1｜∑C_j的最优解。     

考虑分时电价的多目标批调度问题蚁群算法求解

对同时优化电力成本和制造跨度的多目标批处理机调度问题进行了研究,设计了两种多目标蚁群算法,基于工件序的多目标蚁群算法(J-PACO,Job-based Pareto Ant Colony Optimization)和基于成批的多目标蚁群算法(B-PACO,Batch-based Pareto Ant Colony Optimization)对问题进行求解分析。由于分时电价中电价是时间的函数,因而在传统批调度进行批排序的基础上,需要进一步确定批加工时间点以测定电力成本。提出的两种蚁群算法分别将工件和批与时间线相结合进行调度对此类问题进行求解。通过仿真实验将两种算法对问题的求解进行了比较,仿真实验表明B-PACO算法通过结合FFLPT(First Fit Longest Processing Time)启发式算法先将工件成批再生成最终方案,提高了算法搜索效率,并且在衡量算法搜索非支配解数量的Q指标和衡量非支配集与Pareto边界接近程度的HV指标上,均优于J-PACO算法。     

Economic Lot Scheduling Problem with Returns

Motivated by a case study of a company that produces car parts, we study the multi‐product economic lot scheduling problem for a hybrid production line with manufacturing of new products and remanufacturing of returned products. For this economic lot scheduling problem with returns (ELSPR), we consider policies with a common cycle time for all products, and with one manufacturing lot and one remanufacturing lot for each product during a cycle. For a given cycle time, the problem is formulated as a mixed integer linear programming (MIP) problem, which provides the basis for an exact solution. The application of this model for one of the core products of the case study company indicates a 16% reduction in cost compared to the current lot scheduling policy.     

Energy-oriented bi-objective optimization for the tempered glass scheduling

This paper investigates a real life bi-objective hybrid flow shop scheduling problem in an energy-intensive manufacturing system, in which glass is produced successively in cutting, printing and tempering stages. The problem aims to simultaneously optimize makespan and the total electricity cost under a time-of-use electricity pricing policy. The glass production has to respect the following environments: (i) the cutting and printing operations are processed in parallel machine environments; (ii) the tempering operation is processed on a batch machine; (iii) machine eligibility and setup time have to be considered in the cutting and printing stages; (iv) the whole manufacturing system is under a time-of-use electricity pricing policy. For the problem, an integer programming model is firstly proposed and shown to be strongly NP-hard. Then a model-based heuristic is adopted and a bi-objective differential evolution algorithm (BODE) is devised based on problem features. Computational experiments on randomly generated instances demonstrated that the BODE outperforms the model-based heuristic in terms of computation time and solution quality. Moreover, with mild increase on computation burden, the BODE significantly outperforms the classic NSGA II in terms of solution quality.     

Analysis of a batch machine fed by an unreliable discrete machine

This paper analyses the behaviour of a manufacturing line consisting of two machines in series where the first machine processes lots one at a time, and is subject to failure and takes a random amount of time to repair when it fails, and the second machine is a perfectly reliable batch machine. A control limit policy is adopted to determine lot sizes for the batch machine. When the batch machine completes processing, if the number of lots in the buffer is greater than or equal to the critical number (Q?), all the lots in the buffer are loaded immediately, otherwise the batch machine waits until Q lots are accumulated. An embedded discrete time Markov-chain approach is proposed, and recursive approaches are developed to derive necessary performance measures. A numerical example explains how to obtain the optimal value of a critical number minimizing the cost functions.     

Multi-product lot scheduling with backordering and shelf-life constraints

In this paper, we revisit the economic lot scheduling problem (ELSP), where a family of products is produced on a single machine, or facility, on a continual basis. Our focus is on the determination of a feasible production schedule, including the manufacturing batch size of each item. We assume that total backordering is permissible and that each of the products has a limited post-production shelf life. Several studies examining this problem have suggested a rotational common cycle approach, where each item is produced exactly once every cycle. To ensure schedule feasibility, we resort to the technique of reducing individual production rates and allow the flexibility of producing any item more than once in every cycle, in conjunction with appropriate timing adjustments. In order to solve this more generalized model, which is NP hard, we suggest a two-stage heuristic algorithm. A numerical example demonstrates our solution approach.     

Scheduling algorithms for computer-aided line balancing in printed circuit board assembly

Generalized flexible flow line (GFFL) is a scheduling environment comprising several machine banks which the products visit in the same order but can skip some machine banks. The type of machines in a bank can differ but they are suitable for performing the same manufacturing tasks. To change one product to another demands a set-up operation of the machine. This paper describes several scheduling algorithms for the GFFL problem. The overall structure of these algorithms is similar, consisting of machine allocation and sequencing phases. The algorithms have been integrated into an interactive production scheduling system for electronics assembly. Sample cases are used to illustrate the operation of the system in practice.     

A note on special optimal batching structures to minimize total weighted completion time

A batch is a subset of jobs which must be processed jointly in either serial or parallel form. The algorithmic aspects of the batching scheduling problems have been extensively studied in the literature. This paper presents necessary and sufficient conditions of the existence of optimal batch sequences for the single machine, batching, total weighted completion time scheduling problems on two batching ways: (1) all jobs form one batch; (2) each batch contains a single job. This kind of conditions can help us to recognize some special optimal schedules quickly. Research supported by NSFC (10671183).     

混合离散差分进化算法在单机批处理调度中的应用

本文研究单机批处理调度问题,批处理机有批次容量限制,批处理时间由每个批次所含作业中的最长作业处理时间决定。每个作业具有不同的大小、处理时间、提前拖期惩罚权重,所有作业具有公共交货期,且交货期无限晚。目标函数为最小化所有作业的加权提前拖期惩罚之和。该问题已被证明为NP难题,本研究找到了其最优解具有的一些性质,在此基础上利用它们提出了一种动态规划(DP)与差分进化(DE)算法相结合的混合离散差分进化(HDDE)算法来求解该问题,通过与传统的遗传算法、模拟退火算法和迭代贪婪算法进行对比,HDDE算法显示了更加强大的全局搜索能力。