多AGV和机器集成的多目标柔性作业车间调度研究

为解决智能制造环境中具有多时间和多AGV约束的柔性作业车间调度问题,构建了以最小化最大完工时间、最小化总延期、最小化设备总负荷为目标的机器/AGV双约束多目标调度模型,模型中综合考虑加工时间、工件到达时间、交货期等多时间因素,进行了多AGV和机器集成调度。为求解该模型,设计了新的AGV调度规则和改进的NSGA-算法,算法中提出了基于工序的扩展染色体编码方式和基于AGV分配的贪婪式解码策略,同时设计了不同参数控制的多种群二元锦标赛选择和分段交叉变异策略以及基于Pareto级的去重精英保留策略,以促进个体协同优化搜索。通过实例实验,分析了不同AGV数量任务分配方案下的模型有效性,对4个案例的仿真测试和同类算法比较解也验证了改进NSGA-算法求解该模型的有效性。     

考虑机器速度的低碳柔性作业车间调度问题研究

根据具有低碳需求的制造企业的实际情况,建立了考虑机器速度的低碳柔性作业车间调度问题模型,该模型考虑机器加工速度,增加了工件的装夹和卸载时间及机器在不同状态下的碳排放参数,使问题更具现实性。为了实现低碳生产的目标,本文在实现最大完工时间最小的前提下提出了一种非关键工序调整法,通过对非关键工序的调整,降低机器的总碳排放量,提高机器利用率。最后通过求解实际案例,实验结果证实模型的可行性和非关键工序调整法的有效性。     

模糊环境下多目标差异作业单机批调度问题研究

针对现实生产制造系统中存在的时间参数模糊化问题,采用梯形模糊数表征时间参数,给出一种具有模糊交货期和模糊加工时间,以最小化提前/拖期惩罚、制造跨度以及加工费用为目标的多目标差异作业单机批调度问题模型.在对该问题进行求解方面,针对基本粒子群算法容易陷入局部最优的问题,引入混沌局部搜索策略,给出了一种基于混沌优化技术的混合粒子群算法.仿真实验验证了所提出算法的可行性和有效性.     

基于迭代动态规划的聚合物驱注入策略求解

聚合物驱最优注入策略问题是一类分布参数系统的最优控制问题,针对聚合物驱注入方案的特点,运用迭代动态规划方法求解获得了该问题的分段常量控制.迭代动态规划对分布参数系统的多段决策问题从时间和空间进行了离散,由有限差分方法得到离散动态规划模型的递推方程.利用该方法,对一聚合物驱实验模型的最优注入策略问题进行了仿真求解,结果表明该方法与基于梯度方法相比对初值的依赖性小,有效的获得了全局的优化策略.     

串行生产线中机器维修工人的任务分配问题研究

在串行生产线中, 机器会发生故障而且故障间隔时间随机, 因此需要维修工人及时维修, 使得故障的机器恢复加工能力, 否则就可能导致系统吞吐率降低. 如何在满足系统吞吐率的前提下, 使用尽可能少的维修工人来完成机器的维修任务, 本文称这样一个全新的问题为串行生产线中机器维修工人的任务分配问题. 针对该问题, 本文首先建立了问题的优化模型, 并将该优化问题转换为多个判定问题进行求解; 然后, 通过合理地定义机器的维修工作量, 使得判定问题可以类比为并行机调度问题; 最后, 采用了一种基于最长处理时间优先算法(Longest processing time, LPT)和回溯策略的启发式算法, 搜索最优的维修工人任务分配方式. 实验结果表明, 该方法能有效求解维修工人的任务分配问题.     

不可靠制造系统的一种控制策略的参数最优化

本文研究生产单类工件的两台串级机器的系统，机器是不可靠的。系统采用Ｓｕｒｐｌｕｓ Ｃｏｎｔｒｏｌ策略，该策略以每台机器的一个阈值作为控制参数。基于扰动分析方法，我们提出了一种简单的算法用于估计系统费用函数对于控制参数的梯度，并证明了估计的无偏性，仿真例子验证了算法的有效性。     

快速计算布尔方程组特解的一个方法

本文提出了快速计算布尔方程组特解的一个新方法——通路求解法。该方法只用到通路的奇偶参数和“异或”运算,因此求解过程既直观又简单。在计算机上实现时,不仅模拟算法的程序系统简单,而且产生测试的效率较高,同时还较多的节省机器的存储空间。     

差分进化算法求解带批处理机的机器人制造单元调度问题

机器人制造单元是智能制造系统的主要载体,研究机器人制造单元的生产调度问题对于提高智能制造系统的生产效率有着重要作用.对此,研究带批处理机的混合流水线机器人制造单元调度问题.首先,针对机器人制造单元与批处理机的生产特性,建立数学优化模型;其次,设计差分进化算法对其进行求解,提出染色体组编码的概念,求解该问题的染色体组由两个染色体构成,第1条染色体确定工件在每个工序选择的机器,第2条染色体确定加工顺序以及机器人的搬运顺序;然后,设计差分变异、交叉以及选择操作;最后,进行数值实验,结果证明,针对带批处理机的机器人制造单元调度问题,差分进化算法能缩短完工时间,得到更好的解.     

基于多目标多约束的生产制造业的计划排产问题研究

保证交期是生产制造业的生产制造的核心竞争点之一,在这种情况下,制造系统都要求最小化生产完成时间。最小化生产完成时间基本上涉及两个目标,即最小化机器闲置时间和最小化订单提前率/延迟率。必须通过考虑所有约束条件(例如优先级、可用机器、机器转换、机器设置、机器容量、大量库存等)来实现这两个目标的最小化。这需要通过高级计划和排程(APS)来实现。该文提出了一种具有迭代搜索的遗传(GA)算法来找到APS问题的最优解,应用于解决生产制造业多目标多约束的计划排产过程问题。该算法在满足所有约束的情况下最小化机器闲置时间和订单提前率/延迟率,从而最小化生产完成时间,确保产品交期。该文使用Python开发了APS混合整数编程模型,该模型使用迭代搜索技术来提高系统效率并在短时间内产生最佳解决方案。     

基于改进离散人工蜂群算法的同类机调度优化

针对一类最小化最大完工时间的同类机调度问题,考虑到机器的加工效率和产品的交付时间,引入同类机调度问题的数学模型,提出一种改进的离散型人工蜂群算法（IDABC）求解该问题。首先,引入种群初始化策略,得到均匀分布的种群,并获得待优参数的生成策略,加快种群的收敛;其次,借鉴差分进化算法的变异算子和模拟退火算法的思想,改进雇佣蜂和跟随蜂的局部搜索策略,并利用最优解的优质信息改进侦察蜂,增加种群多样性、防止算法陷入局部最优;最后,分析算法的性能和参数,并将改进的算法应用于同类机调度问题,在15个算例上的实验结果表明,与混合离散人工蜂群（HDABC）算法相比,IDABC的求解精度和稳定性分别平均提高了4.1%和26.9%,且具有更好的收敛性,表明在实际场景中IDABC可以有效求解同类机调度问题。     

Optimization of the finite production rate model with scrap,rework and stochastic machine breakdown

This study employs mathematical modeling along with a recursive searching algorithm to determine the optimal run time for an imperfect finite production rate model with scrap, rework, and stochastic machine breakdown. In real-life manufacturing systems, generation of defective items and machine breakdown are inevitable. The objective of this paper is to address these issues and to be able to derive the optimal production run time. It is assumed that the proposed manufacturing system produces defective items randomly, a portion of them is considered to be scrap, and the other portion can be repaired through rework. Further, the proposed system is subject to random breakdown and when it occurs, the abort/resume (AR) policy is adopted. Under such an inventory control policy, the production of the interrupted lot will be resumed immediately when machine is fixed and restored. Mathematical modeling along with a recursive searching algorithm is used for deriving the replenishment policy for such a realistic production system.     

Multi-product economic lot scheduling problem with manufacturing and remanufacturing using a basic period policy

In this research we study the multi-product Economic Lot Scheduling Problem (ELSP) with manufacturing and remanufacturing opportunities. Manufacturing and remanufacturing operations are performed on the same production line. Both manufactured and remanufactured products have the same quality thus they fulfil the same demand stream. Tang and Teunter (2006) firstly studied this type of Economic Lot Scheduling Problem with Returns (ELSPR) and presented a complex algorithm for the optimal solution. More recently Teunter, Tang, and Kaparis (2009) proposed several heuristics to deal with the same problem using more computational efficient approaches. However, both studies have limited the attention to the common cycle policy with the assumption that a single (re)manufacturing lot is used for each item in each cycle. Relaxing the constraint of common cycle time and a single (re)manufacturing lot for each item in each cycle, we propose a simple, easy to implement algorithm, based on Segerstedt (1999), to solve the model using a basic period policy. Several numerical examples show the applicability of the algorithm and the cost savings.     

A lab-scale manufacturing system environment to investigate data-driven production control approaches

Controlling production and release of material into a manufacturing system effectively can lower work-in-progress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a data-driven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the data-driven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.     

制造网格及其资源配置算法

为实现资源共享和协同工作,制造业对公共技术及服务平台的需求赵来越强烈。以快速制造为应用背景,将网络技术运用到制造业领域,提出了制造网络（Manufacturing Grid,MG）的概念。同时,在分析和研究制造网络资源管理结点的功能和特点时,指出资源的配置是该结点的一个瓶颈点和核心,提出以TQCS为目标准则,以选择最佳资源结点为目标的基于AHP的网络结点选择方法,并详细描述了该方法的实现步骤。     

Mathematical modeling for solving manufacturing run time problem with defective rate and random machine breakdown

This paper employs mathematical modeling for solving manufacturing run time problem with random defective rate and stochastic machine breakdown. In real life manufacturing systems, generation of nonconforming items and unexpected breakdown of production equipment are inevitable. For the purpose of addressing these practical issues, this paper studies a system that may produce defective items randomly and it is also subject to a random equipment failure. A no resumption inventory control policy is adopted when breakdown occurs. Under such a policy, the interrupted lot is aborted and malfunction machine is immediately under repair. A new lot will be started only when all on-hand inventory are depleted. Modeling and numerical analyses are used to establish the solution procedure for such a problem. As a result, the optimal manufacturing run time that minimizes the long-run average production–inventory cost is derived. A numerical example is provided to show how the solution procedure works as well as the usages of research results.     

An optimal batch size for a JIT manufacturing system

This paper addresses the problem of a manufacturing system that procures raw materials from suppliers in a lot and processes them to convert to finished products. It proposes an ordering policy for raw materials to meet the requirements of a production facility. In turn, this facility must deliver finished products demanded by outside buyers at fixed interval points in time. In this paper, first we estimate production batch sizes for a JIT delivery system and then we incorporate a JIT raw material supply system. A simple algorithm is developed to compute the batch sizes for both manufacturing and raw material purchasing policies. Computational experiences of the problem are also briefly discussed.     

Robust planning in optimization for production system subject to random machine breakdown and failure in rework

This study is concerned with robust planning in optimization, specifically in determining the optimal run time for production system that is subject to random breakdowns under abort/resume (AR) control policy and failure in rework. In most real-life production processes, generation of defective items and breakdowns of manufacturing equipment are inevitable. In this study, random defective rate is assumed and all manufactured items are screened. The perfect quality, reworkable and scrap items are identified and separated; failure-in-rework is assumed. The system is also subject to random machine breakdown; and when it occurs, the AR policy is adopted. Under such policy, the production of the interrupted lot will be immediately resumed when the machine is restored. Mathematical modeling and derivation of the production-inventory cost functions for both systems with/without breakdowns are presented. The renewal reward theorem is used to cope with the variable cycle length when integrating cost functions. The long-run average cost per unit time is obtained. Theorems on convexity and on bounds of production run time are proposed and proved. A recursive searching algorithm is developed for locating the optimal run time that minimizes the expected production-inventory costs. A numerical example with sensitivity analysis is provided to give insight into the optimal operational control of such an unreliable system.     

Time-synchronizing control of self-organizing shop floors for networked manufacturing

In networked manufacturing systems, shop floors that are geographically dispersed can coordinate autonomously to complete the fabrication and assembly of products. It is a type of self-organizing production process, in which the scheduling of those shop floors must be synchronized, in terms of time and quantity. In this paper, we propose a time-synchronizing control policy for self-organizing shop floors based on the (max, +) system theory, prove the convergence of the synchronization algorithm, and verify the effectiveness of the algorithm by numerical experiments. Besides, a method of implementing the synchronizing control system based on the radio frequency identification (RFID) technologies is also proposed briefly.     

The robustness of scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers

In this paper, a continuous time Markov chain model is introduced to study multi-product manufacturing systems with sequence-dependent setup times and finite buffers under seven scheduling policies, i.e., cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing times), longest queue, longest processing time, and longest overall time. In manufacturing environments, optimal solution may not be applicable due to uncertainty and variation in system parameters. Therefore, in this paper, in addition to comparing the system throughput under different policies, we introduce the notion of robustness of scheduling policies. Specifically, a policy that can deliver good and stable performance resilient to variations in system parameters (such as buffer sizes, processing rates, and setup times) is viewed as a “robust” policy. Numerical studies indicate that the cyclic and longest queue policies exhibit robustness in subject to parameter changes. This could provide production engineers a guideline in operation management.