Impact of random defective rate on lot size focusing work-in-process inventory in manufacturing system

Literature has focused inventory models with intensive emphasis on imperfect production processes in recent past. However, the work-in-process-based inventory models have been ignored, relatively, in general and the impact of random defects in the form of reworkable and non-reworkable defect rate on lot size and total cost function in particular. This paper develops mathematical models for work-in-process-based inventory by incorporating the effect of random defects rate on lot size and expected total cost function. Our proposed models assume that defective products produced during the production process follow random distributions. Defective products, either in the form of reworkable or rejected production units, follow four types of distribution density functions: uniform, triangular, double triangular and beta distribution. Mathematical models are derived for optimum lot size based on minimization of expected total cost function through the analytical optimization approach. Numerical examples and detailed sensitivity analysis are carried to illustrate and compare the proposed models at different levels of distribution functions’ parameters.     

Subwavelength size determination by spatially modulated illumination virtual microscopy

A new approach for determining the sizes of individual, small fluorescent objects with diameters considerably below the optical resolution limit is described in which spatially modulated illumination (SMI) microscopy and 360-647-nm excitation wavelengths are used. The results of SMI virtual microscopy computer simulations indicate that, in this wavelength range, reliable measurements of sizes as small as approximately 20 nm are feasible if the low numbers of fluorescence photons that are usually detected from such small objects are taken into account. This method is based on the well-known fact that the modulation of the diffraction image in a SMI microscope is disturbed by the size of the object. Using appropriately calculated calibration functions, one can use this disturbance of the modulation to determine the size of the original object.     

Effect of learning and forgetting on economic lot size scheduling problem

The paper determines the sequence of set-ups and quantity to produce in each setup if two products are to be produced on a single facility, considering the effects of learning and forgetting. The cost expression which is to be minimized, consists of three factors: set-up cost, carrying cost and the cost of idle facility. A solution procedure is developed and the analysis is extended for the n-product system.     

Improvement of manufacturing processes with virtual reality-based CIP workshops

The Continuous Improvement Process (CIP) is a well-established method to improve manufacturing processes. A typical CIP workshop, carried out by a group of workers and engineers directly in production, often causes unwanted downtimes. Furthermore, improvements have to be realised without reliability testing. Virtual Reality (VR) provides a powerful means to support CIP workshops. This paper introduces the concept of a VR-based CIP workshop. User interfaces are proposed to integrate additional elements, e.g. findings of augmented reality, into VR. This allows meeting specific demands of different participants involved in a CIP workshop. The proposed concept is validated based on an industrial use case.     

Sequential stability of the constant cost dynamic lot size model-searching for monotonicity

The set of cost inputs for which an optimal solution of the dynamic lot size model remains valid is called stability region. The size of this region may be viewed as a measure of robustness of a solution. It is an expectation that the stability regions shrink with growing time horizons and that they are monotonous in this sense. In the present paper several sufficient conditions implying monotonicity will be studied. The conditions cover the existence of planning and forecast horizons and generalize the results of a previous paper in wich monotonicity results were presented for the case of ordinary planning horizons.     

Effect of rework,rejects and inspection on lot size with work-in-process inventory

The economic order quantity and economic production quantity models are the most commonly used inventory models in production environments for the calculation of optimum lot size. However, these models are based on the unrealistic assumption that every process produces good quality products every time. Moreover, the impact of inspection is neglected in all extended inventory control models involving work in process inventory. By taking both imperfect production and lot size inspection into consideration, this paper presents a more realistic approach for the modelling of optimum lot size and total cost with a focus on the work in process inventory. A mathematical model is derived for optimum lot size based on the minimisation of the average cost. Our approach incorporates the effect of rework, rejects and inspection on work in process inventory. The significant effect of imperfect production and inspection on optimum lot size is evaluated via numerical examples. In comparison to existing models, the proposed model is a more generalised and flexible form of inventory model for independent demands.     

Design and implementation of a virtual information system for agile manufacturing

In the new and emerging agile manufacturing paradigm, where multiple firms cooperate under flexible virtual enterprise structures, there exists a great need for a mechanism to manage and control information flow among collaborating partners. In response to this pressing need, this paper addresses the design and implementation of an agile manufacturing information system integrating manufacturing databases dispersed at various partner sites. We propose a framework in which: (1) information is modeled in a hierarchical fashion using object-oriented methodology (OOM); (2) information transactions are specified by the workflow hierarchy consisting of partner workflows; (3) information flow between partners is controlled by a set of distributed workflow managers (WM) interacting with partner knowledge bases, which reflect partner specific information control rules on internal data exchange, as well as inter-partner mutual protocols for joint partner communications; (4) the prototype system is accomplished using the World Wide Web based on a client-server architecture. The overall approach and system provides within a dynamic environment, where virtual partnerships are synthesized in response to specific business initiatives, a dynamic and flexible mechanism to support partner information exchange and to keep the dispersed information consistent.     

The impact of lot sizing and sequencing on manufacturing performance in a two-stage hybrid flow shop

This paper deals with the problem of production scheduling in a two-stage hybrid flow shop, which consists of one machine in the first stage, and multiple process lines in the second stage. In the first stage, materials are batch-processed, which are then fed into one of several process lines in the second stage, depending on the final product specifications. First, we formulated the total flow time or makespan, the capacity utilization and the maximum work-in-process level as measures of manufacturing performance. We analysed, through a series of numerical experiments, how these performance measures are affected by lot sizing, sequencing rules and the scheduling scenario. The following points were clarified. (1) The ratio rule yields the best performance with regard to the makespan, but the LPT rule yields the best performance with regard to the maximum work-in-process level, (2) The optimum lot size is also affected by such operation conditions as the unit processing time, the setup time, and the ratio of unit processing times in the two stages. (3) The starting point for making a schedule should be set in the bottleneck stage with regard to the makespan.     

The ergonomic design of workstations using virtual manufacturing and response surface methodology

The increasing use of computerized tools for virtual manufacturing in workstatin design has two main advantages over traditional methods first it enables the designer to examine a large number of design solutions; and second, simulation of the work task may be performedin order to obtain the values of various performance measures. In this paper a ne~ structural. methodology for the workstation design is presented. Factorial experiments and the response surface methodology are integrated 111 order to reduce the number of examined design solutions and obtain an estimate for the best design configuration With respect to multi-objective requirements.     

Analysis of operating policies for manufacturing cells

We present an analytical model for error-recovery problems of unitary production cells and develop a framework for economic justification. Each production cell has its own machine and functional characteristics (e.g. precision, speed, operating cost). Each alternative operating policy of error-recovery for a production cell leads to a different system throughput, scrap rate, and required quantities of parts. However, error recovery cannot be accomplished without in-process inspections. Thus, the central problem discussed in this paper is to maximize profits or throughput rate by selecting an appropriate production cell along with an optimal set of inspection and error recovery policies.     

Minimization of tool switches for a flexible manufacturing machine with slot assignment of different tool sizes

The problem addressed in this paper is the tool switching problem for an automated manufacturing environment, when each tool may occupy more than one slot of the tool magazine. A machine processes parts automatically by using a limited capacity tool magazine. Providing a tool that is needed for a certain processing operation is not in the magazine, a tool switch must occur before the job can be processed, a time/cost consuming operation. To solve this problem, one has to decide three types of decisions, namely, how to select the jobs' sequence (machine loading), which tools to switch before each processing operation (tool loading) and where to locate each tool in the magazine (slot loading). We present an integer programming formulation for the problem and suggest a heuristic procedure to obtain a solution. Our heuristic is partly a generalization of previously suggested approaches to the first two decision types, but it is mainly oriented towards answering the third decision type. The unified problem has not been addressed previously in the literature. We present a numerical study that demonstrates the efficiency of our procedure.     

Performance comparison of virtual cellular manufacturing with functional and cellular layouts in DRC settings

This study investigates the performance of virtual cellular manufacturing (VCM) systems, comparing them with functional layouts (FL) and traditional, physical cellular layout (CL), in a dual-resource-constrained (DRC) system context. VCM systems employ logical cells, retaining the process layouts of job shops. Part family-based scheduling rules are applied to exploit the benefits of group technology while retaining the flexibility and functional synergies of the job shop. Past studies of VCM have been based entirely on single-resource-constrained (SRC) systems, i.e. as purely machine-limited systems, assuming that resources such as labour and tooling do not restrict the output. However, given the fact that labour forms a second major constraining resource, and many of the advantages associated with cellular manufacturing are derived from labour flexibility, it becomes necessary to extend the research to DRC systems. In this study, we assume several levels of labour flexibility in all three systems, in addition to other relevant factors such as lot size, set-up reduction, and labour assignment rules. It is shown that VCM can outperform efficiently operated FL and CL in certain parameter ranges, as preliminary research has shown so far. However, it is shown that CL tends to outperform both VCM and FL in the parameter ranges customarily advocated for CL, namely, low lot sizes, adequate levels of set-up reduction, cross training of workers, and worker mobility within cells.     

Effect of lognormal particle size distributions on particle spreading in additive manufacturing

Additive manufacturing (AM) has attracted much attention worldwide in various applications due to its convenience and flexibility to rapidly fabricate products, which is a key advantage compared to the traditional subtractive manufacturing. This discrete element method (DEM) study focusses on the impact of particle polydispersity during the particle spreading process on parameters that affect the quality of the final product, like packing and bed surface roughness. The particle systems include four lognormal particle size distribution (PSD) widths, which are benchmarked against the monodisperse system with the same mean particle diameter. The results reveal that: (i) the solid volume fraction of the initial packed particle bed in the delivery chamber increases then plateaus as the PSD width increases; (ii) regardless of PSD width, the solid volume fraction of the particle bed increases with spreading layer height before compression, but decreases with layer height after compression; (iii) the bed surface roughness increases with PSD width or layer height both before and after the compression of the spreading layer; (iv) the extent of increase in solid volume fraction during compression is correlated with the extent of decrease in bed surface roughness; and (v) the broader PSDs exhibit larger fluctuations of solid volume fraction of the particle bed and bed surface roughness due to greater variability in the arrangement of particles of different sizes. The results here have important implications on the design and operation of particle-based AM systems.     

A genetic algorithm-based optimisation model for performance parameters of manufacturing tasks in constructing virtual enterprises

The performance parameters of the manufacturing tasks (PPMT) are considered as the key parameters in constructing virtual enterprises (VE). It is difficult to determine the optimal or near-optimal values of PPMT. In this paper, the optimisation process in VE is perceived as a bidirectional optimisation process which consists of the forward and reverse process. By analysing the process, a reverse optimisation model based on vector norm theory is proposed and the target function is defined as a formula of the sum of the weighting Euclidean distance between two PPMT vectors. The existence of optimisation solution for the problem is investigated. Then an adaptive genetic algorithm based on natural number encoding is developed to solve the problem. A practical example is implemented to verify the validity of the proposed model and approach. The discussed results show that the optimal or near-optimal PPMT is helpful for the candidate enterprises selection in the process of constructing a VE.     

Dynamic routing and the performance of automated manufacturing cells

There has been a considerable amount of research done on the “cell formation” problem, in which machining cells are designed to process a family of components. More recently, it has been suggested that machining cells should be designed so that they take advantage of the flexibility for processing parts that have alternate, or multiple machine routing possibilities. It is argued that such flexibility will improve machine utilization as well as other measures of cell performance and may reduce the need for centralized cell loading and scheduling algorithms. Unfortunately, if the cell is automated, routing flexibility requirements can create a complex control problem for the cell controller. In this paper we implement a cell controller designed to handle the requirements of the flexible routing of parts and compare the performance of the cell to the case in which each part has only one routing. We find that significant improvements occur when the cell design is capable of processing parts with flexible routings.     

Intelligent dynamic control of stochastic economic lot scheduling by agent-based reinforcement learning

This paper addresses a stochastic economic lot scheduling problem (SELSP) for a single machine make-to-stock production system in which the demands and the processing times for N types of products are random. The sequence-independent setup times and costs are explicitly considered and may have different values for various types of products. The SELSP is to decide when, what, and how much (the lot size) to produce so that the long-run average total cost, including setup, holding and backorder costs, is minimised. We develop a mathematical model and propose two reinforcement learning (RL) algorithms for real-time decision-making, in which a decision agent is assigned to the machine and improves the accuracy of its action-selection decisions via a ‘learning’ process. Specifically, one is a Q-learning algorithm for a semi-Markov decision process (QLS) and another is a Q-learning algorithm with a learning-improvement heuristic (QLIH) to further improve the performance of QLS. We compare the performance of QLS and QLIH with a benchmarking Brownian policy and the first-come-first-served policy. The numerical results show that QLIH outperforms QLS and both benchmarking policies.     

Estimating the distribution and variance of time to produce a fixed lot size given deterministic processing times and random downtimes

Managing production throughput variability is necessary to reduce system costs and improve throughput. Critical in this management is predicting how system changes, like reduced repair times, will change throughput variability. Many manufacturing plants produce products in fixed lot sizes to meet production targets which causes system variability to be manifested as variability in the time to produce the lot. We derive analytical approximations of the density function and variance of the time to produce a fixed lotsize on a single workstation with deterministic processing times and random downtimes. These expressions can be applied to larger production systems, by modelling the system as a single workstation with parameters that approximate the system's output. This result quantifies how workstation failures, repair times, and speed impact the density and variance of time to produce a fixed lotsize. The density function also generates a cycle time distribution when the lost size is one. This is useful in discrete event simulation.     

A cellular similarity coefficient algorithm for the design of manufacturing cells

The development and implementation of an integrated system for computer-aided process planning and cellular manufacturing design is described. A coding scheme is proposed for classifying parts according to the manufacturing processes required to produce them. The routeing data and the classification codes obtained from computer-aided process planning are then stored in a database. A heuristic algorithm has also been developed for designing manufacturing cells using the classification data stored in the process planning database. This algorithm is based on a new concept developed in this study called cellular similarity coefficient which considers the similarity between machine cells rather than individual machines, as in the case of other similarity coefficients.     

一种利用虚拟仪器建立的电压闪变监测方法

为了实现对电压闪变的监测,以减少其对人们工作、生活的影响,根据虚拟仪器的设计思想,按照国际电工委员会(IEC)制定的闪变仪的测试功能和设计规范,给出了IEC数字式闪变仪各个环节数字滤波器的传递函数和运算结构,在LabVIEW环境下建立了闪变仪的模型,根据标准调整了各个模块环节的参数,对电压数字信号进行了分析处理,得到了电压瞬时闪变视感度,将计算结果与标准值进行了比较验证,仿真实验证实了利用虚拟仪器监测闪变的可行性.