Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems

The concept of globalization has been prosperous in the past decades while manufacturing as well as logistics have already become one of the most significant issues in the globalization era. However, while modern globalized firms are leveraging both global manufacturing resources as well as logistics systems for pursuing higher quality, lower cost as well as product differentiation, how to evaluate, selecting an appropriate global manufacturing strategy by considering issues from both aspects of global manufacturing as well as logistics has become one of the most critical and difficult issues. Moreover, how the chosen intertwined global manufacturing as well as logistics system is to be optimized so that the aspired level of the global manufacturing system can be achieved have few been addressed. Thus, this research aims to resolve the above mentioned global manufacturing and logistics strategy selection as well as system reconfiguration issue. A Decision Making Trial and Evaluation Laboratory (DEMATEL) technique based novel multiple criteria decision making (MCDM) method with Analytic Network Process (ANP), Grey Relational Analysis (GRA) as well as VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) will be proposed for selecting and re-configuring the aspired global manufacturing and logistics system. An empirical study based on the global manufacturing and logistics system design of a semiconductor company will be provided for verifying the effectiveness of this proposed methodology.     

Cell formation using fuzzy relational clustering algorithm

Cellular manufacturing is a useful way to improve overall manufacturing performance. Group technology is used to increase the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation is an important step in group technology. It is used in designing good cellular manufacturing systems. The key step in designing any cellular manufacturing system is the identification of part families and machine groups for the creation of cells that uses the similarities between parts in relation to the machines in their manufacture. There are two basic procedures for cell formation in group technology. One is part-family formation and the other is machine–cell formation. In this paper, we apply a fuzzy relational data clustering algorithm to form part families and machine groups. A real data study shows that the proposed approach performs well based on the grouping efficiency proposed by Chandrasekharan and Rajagopalan.     

Order selection optimization in hybrid make-to-order and make-to-stock markets

The order selection process for firms operating in markets characterized by standard products as well as strong product customization, is directly tied to the customer and to the associated production costs. This order selection process must be inter-functional with marketing and manufacturing in deciding which orders to accept, specially since the selection criteria used by marketing and manufacturing tend to differ. This paper develops a methodology/mathematical programming model for improving this coordination through an optimal selection of sales orders, such that the total financial contribution of selected orders is maximized. Based on an actual case study in the pigment manufacturing industry, the methodology provides for a user interface that addresses both the manufacturing and marketing department. More importantly, such a marketing/production coordination allows for improved performance in increasingly segmented (customized) markets, while continuing standard product lines as well.     

Manufacturing performance measurement and target setting: A data envelopment analysis approach

Manufacturing decision makers have to deal with a large number of reports and metrics for evaluating the performance of manufacturing systems. Since the metrics provide different and at times conflicting assessments, it is hard for the manufacturing decision makers to track and improve overall manufacturing system performance. This research presents a data envelopment analysis (DEA) based approach for performance measurement and target setting of manufacturing systems. The approach is applied to two different manufacturing environments. The performance peer groups identified using DEA are utilized to set performance targets and to guide performance improvement efforts. The DEA scores are checked against past process modifications that led to identified performance changes. Limitations of the DEA based approach are presented when considering measures that are influenced by factors outside of the control of the manufacturing decision makers. The potential of a DEA based generic performance measurement approach for manufacturing systems is provided.     

New bounds and constraint propagation techniques for the clique partitioning problem

This paper considers the problem of clustering the vertices of a complete edge-weighted graph. The objective is to maximize the sum of the edge weights within the clusters (also called cliques). This so-called Clique Partitioning Problem (CPP) is NP-complete, and has several real-life applications such as groupings in flexible manufacturing systems, in biology, in flight gate assignment, etc. Numerous heuristics and exact approaches as well as benchmark tests have been presented in the literature. Most exact methods use branch and bound with branching over edges. We present tighter upper bounds for each search tree node than those known from the literature, improve the constraint propagation techniques for fixing edges in each node, and present a new branching scheme. The theoretical improvements are reflected by computational tests with real-life data. Although a standard solver delivers best results on randomly generated data, the runtime of the proposed algorithm is very low when being applied to instances on object clustering.     

基于支持向量机的制造企业创新能力评价模型

在分析了制造企业创新能力评价指标体系的基础上进行企业调查,对收集到的不同类型制造企业的完整数据进行整理,因子分析整理后得到9个综合因子表述原数据,以减少数据处理及问题分析的复杂性.利用支持向量机作为分类器,并使用已有的企业数据作为训练样本,创建了基于支持向量机的制造企业创新能力评价模型.实验结果表明采用径向基函数和多项式函数作为核函数,此模型具有很好的分类性能,可作为制造企业创新能力的评价工具.     

Cane Railway Scheduling via Constraint Logic Programming: Labelling Order and Constraints in a Real-Life Application

In Australia, cane transport is the largest unit cost in the manufacturing of raw sugar, making up around 35% of the total manufacturing costs. Producing efficient schedules for the cane railways can result in significant cost savings. This paper presents a study using Constraint Logic Programming (CLP) to solve the cane transport scheduling problem. Tailored heuristic labelling order and constraints strategies are proposed and encouraging results of application to several test problems and one real-life case are presented. The preliminary results demonstrate that CLP can be used as an effective tool for solving the cane transport scheduling problem, with a potential decrease in development costs of the scheduling system. It can also be used as an efficient tool for rescheduling tasks which the existing cane transport scheduling system cannot perform well.     

外源投入型两阶段制造系统网络DEA效率分析研究

制造过程评价是改善制造系统效率的重要一环,传统的评价方法将每个制造系统决策单元视为黑箱来研究整体效率,忽略了中间产品转化信息及投入要素在各子过程中的配置信息。针对两阶段(第二阶段有外源性新投入)制造系统的效率评估问题,分别在固定规模报酬和可变规模报酬假设下,充分利用制造系统中间产品的转化及外源投入要素的配置信息,建立了制造系统网络DEA效率测度及分解模型,建模方法遵循客观评价原则,无需事先主观确定子效率和系统效率之间的组合关系。并将其应用于钢铁制造系统效率测度与分解,研究结果表明该方法能够挖掘决策单元内部子单元的效率情况,帮助决策者发现复杂制造过程非有效的根源,为复杂制造过程的整体效率测度及分解提供了有效的分析方法。     

Competition for cores in remanufacturing

We study competition between an original equipment manufacturer (OEM) and an independently operating remanufacturer (IO). Different from the existing literature, the OEM and IO compete not only for selling their products but also for collecting returned products (cores) through their acquisition prices. We consider a two-period model with manufacturing by the OEM in the first period, and manufacturing as well as remanufacturing in the second period. We find the optimal policies for both players by establishing a Nash equilibrium in the second period, and then determine the optimal manufacturing decision for the OEM in the first period. This leads to a number of managerial insights. One interesting result is that the acquisition price of the OEM only depends on its own cost structure, and not on the acquisition price of the IO. Further insights are obtained from a numerical investigation. We find that when the cost benefits of remanufacturing diminishes and the IO has more chance to collect the available cores, the OEM manufactures less in the first period as the market in the second period gets larger to protect its market share. Finally, we consider the case where consumers have lower willingness to pay for the remanufactured products and find that in that case remanufacturing becomes less profitable overall.     

Strategic capacity planning for light emitting diode (LED) supply chains across Taiwan and China

This paper describes a mixed-integer programming (MIP) model formulated for strategic capacity planning for light emitting diode (LED) makers of Taiwan, major companies in the global LED market. These firms have complex supply chains across Taiwan and China, and the region’s unique political and economic environment has created not only competitive advantages but also challenges in supply chain management: government regulations require that customer orders be accepted from Taiwan or China according to customer attributes; when conducting manufacturing, Taiwanese firms may need to transfer orders across national borders for reasons such as manufacturing technology (the required technology is available only at certain manufacturing facilities) or more efficient capacity utilization; and there are operations to be performed with specific processing requirements to follow, posing substantial challenges for planners. Motivated by the significance of these firms in the global market, we develop a MIP model with novel features to support their strategic capacity planning, covering demand and manufacturing-related decisions, including order acceptance and transfer, manufacturing starts, capacity expansion, and logistics. We illustrate the model’s performance using modified industry data in a numerical example; we also describe the potential impacts the model may create in industry applications.     

A new bounding mechanism for the CNC machine scheduling problems with controllable processing times

In this study, we determine the upper and lower bounds for the processing time of each job under controllable machining conditions. The proposed bounding scheme is used to find a set of discrete efficient points on the efficient frontier for a bi-criteria scheduling problem on a single CNC machine. We have two objectives; minimizing the manufacturing cost (comprised of machining and tooling costs) and minimizing makespan. The technological restrictions of the CNC machine along with the job specific parameters affect the machining conditions; such as cutting speed and feed rate, which in turn specify the processing times and tool lives. Since it is well known that scheduling problems are extremely sensitive to processing time data, system resources can be utilized much more efficiently by selecting processing times appropriately.     

Learning management knowledge for manufacturing systems in the early stages using time series data

Acquiring knowledge in manufacturing systems in the early stages always has a challenging task due to the lack of sufficient data. This makes it hard for the derived management model to reach a reliable and stable level. Li and Lin (2006) developed a useful method that can deal with the problem of knowledge acquisition based on a small data set. However, this method assumes all data are collected at the same time, since they treat the data set as a source (from one population) of a priori knowledge for learning. In fact, instead of being a random data set, these collected data can be time dependent, that is, they tend to be a sequence of observations, occurring at different times. The consideration of this dependence property in the data will benefit the knowledge acquisition in the early stages by expanding the learning model from an independent model to a dependent model. This research expanded the intervalized kernel density estimator (IKDE) presented in Li and Lin (2006) to a more general form to improve the learning model in the early stages. The general model, called GIKDE, joints the concepts of time series and stochastic processes in order to deal with both independent and dependent data sets. The Virtual Sample Generation process based on GIKDE was also developed to produce extra information for expediting the learning. Results obtained from the application of the model to a control charts data, using a back-propagation neural network as the learning tool, show that this unique approach is an effective method of knowledge acquisition for a manufacturing system in the early stages.     

Heuristics with guaranteed performance bounds for a manufacturing system with product recovery

We consider a manufacturing system with product recovery. The system manufactures a new product as well as remanufactures the product from old, returned items. The items remanufactured with the returned products are as good as new and satisfy the same demand as the new item. The demand rate for the new item and the return rate for the old item are deterministic and constant. The relevant costs are the holding costs for the new item and the returned item, and the fixed setup costs for both manufacturing and remanufacturing. The objective is to determine the lot sizes and production schedule for manufacturing and remanufacturing so as to minimize the long-run average cost per unit time. We first develop a lower bound among all classes of policies for the problem. We then show that the optimal integer ratio policy for the problem obtains a solution whose cost is at most 1.5% more than the lower bound.     

Machine-part cell formation using biclustering

Cellular manufacturing is the cornerstone of many modern flexible manufacturing techniques, taking advantage of the similarities between parts in order to decrease the complexity of the design and manufacturing life cycle. Part-Machine Grouping (PMG) problem is the key step in cellular manufacturing aiming at grouping parts with similar processing requirements or similar design features into part families and by grouping machines into cells associated to these families. The PMG problem is NP-complete and the different proposed techniques for solving it are based on heuristics. In this paper, a new approach for solving the PMG problem is proposed which is based on biclustering. Biclustering is a methodology where rows and columns of an input data matrix are clustered simultaneously. A bicluster is defined as a submatrix spanned by both a subset of rows and a subset of columns. Although biclustering has been almost exclusively applied to DNA microarray analysis, we present that biclustering can be successfully applied to the PMG problem. We also present empirical results to demonstrate the efficiency and accuracy of the proposed technique with respect to related ones for various formations of the problem.     

Economically study regarding modernizing of the final working of the car radiators through implementation of an articulated robot in a flexible manufacturing cell

In this paper the authors purpose the conception and modelling of a possible application in a circular flexible manufacturing cell. This flexible cell is composed from five manufacturing points for realizing the final technological itinerary of a type of radiator. This flexible manufacturing cell is served by an industrial articulated robot, which possess in his cinematic chain three degrees of freedom, type RRT. In virtue of real values of the functional parameters which are input data in the analysis of the flexible manufacturing cell, had been dignify the cost of the flexible manufacturing cell, determining the value of the included industrial robot in the system and the value of the entire flexible cell where the robot realize the manipulating process of the radiators. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Chapter 9 Moving frontier analysis: An application of Data Envelopment Analysis for competitive analysis of a high-technology manufacturing plant

Competitive analysis of plants is fundamental to enhancing the competitiveness of manufacturing firms. Moving Frontier Analysis (MFA), proposed in this paper, is a new application of Data Envelopment Analysis (DEA) to conduct competitive analysis of a high-technology manufacturing plant. The development and application of MFA was done in collaboration with the management of a US merchant semiconductor manufacturing firm. A wafer fabrication plant of the firm served as the research site for this study. MFA provides a means to determine (i) the gap between a plant's best performance and the best of competition, and (ii) whether or not it will be possible to close this performance gap, and if so, the time it will take to do so. From an implementation standpoint, competitive analysis using MFA can be conducted even if (i) accurate and detailed data on comparable plants of competitor companies are not available, and (ii) the operations of competitors' plants are dynamically changing. An application of MFA for conducting competitive analysis of the wafer fabrication plant is presented. This application illustrates how MFA makes it possible to estimate (i) the unit cost of production for the best of competitors' plants using aggregate operational information on industry best practices, and (ii) the time it will take for the plant's unit cost of production to catch up with the best of competition using the technologies in operation.     

Queueing theory in manufacturing systems analysis and design: A classification of models for production and transfer lines

Queueing network modeling of manufacturing systems has been addressed by a large number of researchers. The purpose of this paper is to provide a bibliography of material concerned with modeling of production and transfer lines using queueing networks. Both production and transfer lines have a product-flow layout and are used in mass manufacturing. We denote production lines as flow lines with asynchronous part transfer, while transfer lines have synchronous part transfer. As well as providing a bibliography of material, a contribution of this paper is also the systematic categorization of the queueing network models based on their assumptions. This, it is hoped, will be of use to researchers of queueing networks and also manufacturing system designers. A number of suggestions are also given for further research. The basic source for this work is the book by Papadopoulos, Heavey and Browne, with the addition of the newly published papers and books (from 1992 to early 1995).     

Agile infrastructure design for garment industry

The Information Economics and its platform can input the vigor into manufacturing industry, i.e., garment industry, help to increase its ability for good return, promote the level of technology and management of Garments from a labor-intensive industry, and move it to the accurate management. The platform is designed as Comprehensive Information Platform by our research team. This paper is a general report series of papers the design of Agile Infrastructure for Collaborative Manufacturing and Agile Supply Chain. The key enabled technologies in the platform includes agile infrastructure and its application, business resources planning in manufacturing system, the design of data access model, optimal strategy for production planning, the dynamic load balance scheduling model, the intrusion detection technology and intelligent communication strategy. All of them are elaborated in turn. The paper tries to introduce the research of Agile Infrastructure and its key technologies, showing how well they work in some traditional manual industries.     

Software modelling of manufacturing systems: A case for an object-oriented programming approach

With the recognition of the importance of Computer Integrated Systems (CIM) in improving manufacturing productivity, there is a pressing need for good software modelling approaches to support efficient design and control of manufacturing systems. Software design concepts based on Object-Oriented Programming (OOP) are emerging as powerful techniques for developing large scale software systems. This paper presents important features of object-oriented computing and the relevance of such an approach in modelling and developing software for manufacturing systems.     

Using recurrent neural networks with attention for detecting problematic slab shapes in steel rolling

The competitiveness in the manufacturing industry raises demands for using recent data analysis algorithms for manufacturing process development. Data-driven analysis enables extraction of novel knowledge from already existing sensors and data, which is necessary for advanced manufacturing process refinement involving aged machinery. Improved data analysis enables factories to stay competitive against newer factories, but without any hefty investment. In large manufacturing operations, the dependencies between data are highly complex and therefore very difficult to analyse manually. This paper applies a deep learning approach, using a recurrent neural network with long short term memory cells together with an attention mechanism to model the dependencies between the measured product shape, as measured before the most critical manufacturing operation, and the final product quality. Our approach predicts the ratio of flawed products already before the critical operation with an AUC-ROC score of 0.85, i.e., we can detect more than 80 % of all flawed products while having less than 25 % false positive predictions (false alarms). In contrast to previous deep learning approaches, our method shows how the recurrent neural network reasons about the input shape, using the attention mechanism to point out which parts of the product shape that have the highest influence on the predictions. Such information is crucial for both process developers, in order to understand and improve the process, and for process operators who can use the information to learn how to better trust the predictions and control the process.