Digital Machines,Space, and Time: Towards a Behavioral Perspective of Flexible Manufacturing

Recently, the diffusion of digital machines has further enhanced firms’ manufacturing flexibility, but also opened questions on potential challenges and implications in the production process. To respond to these timely issues, this study adopts a behavioral perspective and comparatively explores how four different types of digital machines—characterized by increasing degrees of manufacturing flexibility—affect the perception and use of space and time for routines within the production plant. To this end, 45 digital manufacturing machines, sampled across 14 firms in the British and Italian motorsport industry, were qualitatively observed and compared. A model emerges where four key mechanisms reshape (1) the interactive space around the machine, (2) the innovation activities performed in the machine space, (3) the time within activities involving the machine, and (4) the time perception. Such mechanisms mediate the relationship between manufacturing flexibility and firm performance. Further, data show how increasing digitalization in the manufacturing process enhances the establishment of new routines as flexible machines get introduced in the production. Finally, theoretical and practical implications related to fostering a behavioral perspective in innovation and operations management studies are discussed.     

Machine repairing models for production systems

In this paper we consider manufacturing systems of m identical unreliable machines producing one type of product. The operating time of each machine is exponentially distributed. The repairing process of a machine requires more than one phase. In each phase, the repair time is exponentially distributed and more than one operator may be required for fixing a broken machine. Here we consider two models of manufacturing systems. In the first model, there are r operators assigned in one server to repair a broken machine. The repairing rate in each phase depends on the number of operators there. This is a generalized model discussed in Buzacott and Shanthikumar [11]. We then consider a two-phase repairing model. Two groups of operators are assigned in the two phases. Each operator can handle one broken machine in each phase individually. This model is a generalization of Eben–Chaime's model [8]. Average profits are derived for both models and can be optimized by suitable allocation of the number of machines and operators in the systems.     

U-shaped production lines: A review of theory and practice

Most writers describe the U-shaped production line as the special type of cellular manufacturing used in just-in-time (JIT) production systems. JIT is defined to be an umbrella term for a number of techniques whose purpose is to improve product quality and cost by eliminating all waste in the production system. The U-line arranges machines around a U-shaped line in the order in which production operations are performed. Operators work inside the U-line. One operator supervises both the entrance and the exit of the line. Machine-work is separated from operator-work so that machines work independently as much as possible. Standard operation charts specify exactly how all work is done. U-lines may be simple or complex. U-lines are rebalanced periodically when production requirements change. The U-line satisfies the flow manufacturing principle. This requires operators to be multi-skilled to operate several different machines or processes. It also requires operators to work standing up and walking. When setup times are negligible, U-lines are operated as mixed-model lines where each station is able to produce any product in any cycle. When setup times are larger, multiple U-lines are formed and dedicated to different products. 114 US and Japanese U-lines are examined in this study. The average U-line has 10.2 machines and 3.4 operators. About one-quarter of all U-lines are manned by one operator and so run in chase mode. The reported benefits are impressive. Productivity improved by an average of 76%. WIP dropped by 86%. Leadtime shrunk by 75%. Defective rates dropped by 83%.     

A new approach to job shop scheduling problems with due date constraints considering operation subcontracts

We consider a kind of job shop scheduling problems with due-date constraints, where temporal relaxation of machine capacity constraint is possible through subcontracts. In practice, this kind of problem is frequently found in manufacturing industries where outsourcing of manufacturing operation is possible through subcontract. We present a heuristic algorithm that addresses the problem by solving a series of smaller subproblems to optimality. For the sake of efficiency, the algorithm repeatedly executes in two steps—(1) improving the sequence of operations and (2) picking out the operations to be subcontracted—on bottleneck machines. Experiments are conducted for example problems, and the result of the experiment confirms the viability of the suggested algorithm.     

特殊工况下盾构机出井吊装施工技术研究

为了解决在特殊情况下进行管片外径为8.8 m的盾构机接收、拆解和吊装作业问题,结合施工现场实际情况,对特殊工况下盾构机吊装出井技术措施进行研究。首先对吊装过程中最不利因素进行分析,其次提出了在结构未完全完成工况下盾构机吊装解决方案,同时进行了有、无措施的吊装作业对现有不完整井结构变形影响的数值模拟分析,并分析了在有措施的工况下吊车支腿地基承载力及基坑侧压力,最终提出了在接收井结构不完整情况下盾构机接收、拆解、吊装出井及施工监测的技术措施。结果表明：实际工程应用中,所提盾构机出井吊装施工技术措施可行可靠;有限元分析结果与实际监测变形规律基本一致,变形可控。研究结果丰富了特殊工况下盾构机吊装作业的施工技术,可为类似情况下的隧道工程吊装作业提供借鉴。     

石油钻机状态模糊综合评价

运用模糊数学的方法,根据石油钻机各个组成系统在整套钻机中所起的作用大小,确定各个系统的权重,并再进一步细化,确定各个组成系统的关键部件和零件的权重。依据检测数据分析和专家经验,对钻机等级进行综合评判和分级。     

Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm

This paper addresses the problem of finding robust and stable solutions for the flexible job shop scheduling problem with random machine breakdowns. A number of bi-objective measures combining the robustness and stability of the predicted schedule are defined and compared while using the same rescheduling method. Consequently, a two-stage Hybrid Genetic Algorithm (HGA) is proposed to generate the predictive schedule. The first stage optimizes the primary objective, minimizing makespan in this work, where all the data is considered to be deterministic with no expected disruptions. The second stage optimizes the bi-objective function and integrates machines assignments and operations sequencing with the expected machine breakdown in the decoding space. An experimental study and Analysis of Variance (ANOVA) is conducted to study the effect of different proposed measures on the performance of the obtained results. Results indicate that different measures have different significant effects on the relative performance of the proposed method. Furthermore, the effectiveness of the current proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods.     

A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry

This paper studies a solar cell industry scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage could be determined. However, the challenge in solar cell manufacturing is the number of machines can be dynamically adjusted to complete the job within the shortest possible time. Therefore, the paper addresses a multi-stage HFS scheduling problem with characteristics of parallel processing, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. The objective is to schedule the job production sequence, number of sublots, and dynamically allocate sublots to parallel machines such that the makespan time is minimized. The problem is formulated as a mixed integer linear programming (MILP) model. A hybrid approach based on the variable neighborhood search and particle swarm optimization (VNPSO) is developed to obtain the near-optimal solution. Preliminary computational study indicates that the developed VNPSO not only provides good quality solutions within a reasonable amount of time but also outperforms the classic branch and bound method and the current industry heuristic practiced by the case company.     

Multiple part-type scheduling in flexible robotic cells

This paper considers the scheduling problem arising in two-machine manufacturing cells which repeatedly produce a set of multiple part-types, and where transportation of the parts between the machines is performed by a robot. The cycle time of the cell depends on the robot move sequence as well as the processing times of the parts on the machines. For highly flexible CNC machines, the processing times can be adjusted. To this end, this study tries to find the robot move sequence as well as the processing times of the parts on each machine that jointly minimize the cycle time. The problem of determining the best cycle in a 2-machine cell is first modeled as a traveling salesman problem. Then, an efficient 2-stage heuristic algorithm is constructed and compared with the most common heuristic approach of longest processing time (LPT).     

A mathematical programming approach to evaluating alternative machine clusters in cellular manufacturing

Cellular manufacturing systems achieve the economies of scope and scale approaching that of flexible and high-volume production when the machine/part clusters are totally independent of each other. However, most real systems contain bottleneck machines and exceptional parts (exceptional elements) that reduce these economies. Many grouping methods have been proposed for creating the initial machine/part cells where the presence of exceptional elements may greatly affect their performance. Furthermore, multiple alternative solutions are often possible for a given grouping algorithm. In this paper, the previous work dealing with exceptional elements is reviewed. A mathematical programming model used for comprehensively dealing with exceptional elements is investigated. The effect of alternative initial machine/part clusters on the total cost is evaluated. It is demonstrated that the mathematical programming model can provide useful information in making trade-off decisions when exceptional elements are present.     

Fuzzy multi-criteria decision model for evaluating reconfigurable machines

The design and configuration of manufacturing equipment require crucial decision considering optimum capacity and functionality. The equipment selection problem might be involved with choosing between large-capacity machines versus a greater number of machines with smaller capacities, and/or dedicated facilities versus multi-product facilities. This paper investigates reconfigurable machining system characteristics in order to identify the crucial factors influencing the machine selection and the machine (re)configuration. Furthermore, changeover cost and changeover time while switching from one product to the other are taken into account. In particular, a fuzzy analytical hierarchical process (FAHP) model is proposed to integrate the decisive factors for the equipment selection process under uncertainty. The expected values of the normalised fuzzy sets are determined to identify the preference values of the alternative machines. The fuzzy multi-criteria model is analysed within the fuzzy domains of the operational characteristics along with economic, quality and performance criteria. The proposed model is examined using monitoring sensitivity analysis through a case study. As a result, the alternative machines are prioritised with consideration of the inconsistency ratios. The relative performances of the alternative equipment in view of interactions of process reconfigurability and cost, and capacity and functionality are graphically illustrated.     

A genetic algorithm based heuristic for two machine no-wait flowshop scheduling problems with class setup times that minimizes maximum lateness

Machine scheduling problem has been extensively studied by researchers for many decades in view of its numerous applications on solving practical problems. Due to the complexity of this class of scheduling problems, various approximation solution approaches have been presented in the literature. In this paper, we present a genetic algorithm (GA) based heuristic approach to solve the problem of two machine no-wait flowshop scheduling problems that the setup time on the machines is class dependent, and the objective is to minimize the maximum lateness of the jobs processed. This class of machine scheduling problems has many practical applications in manufacturing industry, such as metal refinery operations, food processing industry and chemical products production processes, in which no interruption between subsequent processes is allowed and the products can be grouped into families. Extensive computation experiments have been conducted to evaluate the effectiveness of the proposed algorithm. Results show the proposed methodology is suitable to be adopted for the development of an efficient scheduling plan for this class of problems in real life application.     

数控线切割编程中3B程序与CNC程序的转换及应用

数控线切割编程技术在线切割加工中起着主导作用。目前国内外采用的程序格式是CNC国际通用G代码,该程序操作简单、方便、快捷、准确。早期国产数控线切割机床的程序编制采用3B(4B)程序。这些机床的市场占有率仍很大,若更换为有G代码编程的控制柜其费用相当于原机床的售价。本文提出了一种简单的方法,可以将3B(4B)程序直接转换为CNC(ISO代码)程序,而使CNC编程的机床与原3B(4B)编程的国产机床实现数据共享。     

气体浓度监测系统的设计

介绍了1种微机控制气体浓度监测报警系统。该系统由从机与主机构成(主机由PC机实现,从机以单片机AT89C51为核心,一般情况下从机与主机联成系统,但也能单独工作),可实现多点多路气体浓度的采集、显示、存储、查询、打印等功能。     

基于AHP的EPC管道工程项目风险识别与分析

运用风险分解结构(RBS)方法,将EPC总承包管道工程项目划分为若干工作阶段,仔细识别与分析每一工作阶段项目可能面临的风险,并将识别出的各阶段具体风险对应到"人员、机具设备、原材料、工艺方法、环境"五要素中,转化成每类要素面临的风险。EPC管道工程项目的风险结构模型包括准则层和指标层。采用层次分析法(AHP)对风险度进行排序,可得出每类要素风险度的高低,进而在风险管理过程中有针对性地进行防范、控制或转移。本文分析结果表明,EPC管道工程项目面临的主要风险依次为人员风险、施工工艺风险以及机器设备风险。     

Production planning and inventory allocation of a single-product assemble-to-order system with failure-prone machines

We consider the optimal production and inventory allocation of a single-product assemble-to-order system with multiple demand classes and lost sales. Each component is replenished by a dedicated machine that is subjected to unpredictable breakdowns. We find that the machine state not only influences the production and allocation decisions on its own component but also influences the decisions on the other components. Specifically, the optimal component production policy is a base-stock policy with the base-stock level non-decreasing in the inventory levels of the other components and the states of the other machines. The optimal component allocation policy is a rationing policy with the rationing level non-increasing in the inventory levels of the other components, the states of the other machines, and its own machine state. We use an exponential distribution to approximate the distribution of the total processing times and propose two heuristic policies to address the production and allocation decisions. The importance of taking machine failures into consideration is revealed through computational experiments.     

Scheduling with processing set restrictions: PTAS results for several variants

We consider multiprocessor scheduling to minimize makespan. Each job has a given processing time and in addition, a subset of machines associated with it, also called its processing set. Each job has to be assigned to one machine in its set, thus contributing to the load of this machine. We study two variants of this problem on identical machines, the case of nested processing sets, and the case of tree-hierarchical processing sets. In addition, we consider uniformly related machines with a special case of inclusive processing sets, which has a clear motivation. We design polynomial time approximation schemes for these three variants. The first case resolves one of the open problems stated in the survey by Leung and Li (2008).     

开棉机的性能比较

开棉机属于纺纱设备中的开清棉机械之一,主要对纤维进行开松、除杂作用,对两种打击形式即自由打击与握持打击进行了比较,并具体分析比较了轴流式开棉机与豪猪式开棉机,分析了两种轴流式开棉机的使用情况。     

硫化机微机自动控制系统及应用

利用 ＳＴＤ总线工业控制机和 ＰＬＣ可编程序控制器对两台进口（８００Ｔ、３５０Ｔ）两台国产（５００Ｔ、２５０Ｔ）的不同吨位的四台电加热平板硫化机进行控制，实现了橡胶硫化过程的自动化，提高了产品合格率。