Agility and production flow layouts: An analytical decision analysis

The term ‘agile manufacturing’ has referred to operational aspects of a manufacturing company concerning their ability to produce customized products at mass production prices and with short lead times. A core issue faced within agile manufacturing is the need for appropriate and supporting production and operations systems. Many design dimensions of agility and agile manufacturing exist. To help attain this goal for integrating the many design dimensions, operations infrastructure and capacity must be carefully planned to manage production flow, and thus production layout planning takes on an increasingly important role. Given the importance of these dimensions in response to agility, this paper seeks to make a contribution by providing insights into a decision aid for evaluating production flow layouts that support and enhance the agile manufacture of products. Layout design has a significant impact on the performance of a manufacturing or service industry system and has been an active research area for many decades. Strategic evaluation of production layouts requires consideration of both qualitative and quantitative factors (managerial, organizational, and technical). This paper makes use of the Analytical Network Process (ANP) which captures interdependencies among different criteria, sub-criteria and dimensions, an evident characteristic of production flow layouts in complex agile manufacturing environments. An application case study exemplifying the practical usefulness of this type of model describes how management, after implementation of the model, made a mid-course correction related to the production layout initially selected.     

Equipment replacement decisions and lean manufacturing

Traditional manufacturing systems are built on the principle of economies of scale. Here, the large fixed costs of production are depreciation-intensive because of huge capital investments made in high-volume operations. These fixed costs are spread over large production batch sizes in an effort to minimize the total unit costs of owning and operating the manufacturing system. As an alternative to “batch-and-queue,” high-volume, and inflexible operations, the principles of the Toyota Production System (TPS) and lean manufacturing have been widely adopted in recent years in the US [1, 2, 3 and 4]. In this paper, we illustrate an equipment replacement decision problem within the context of lean manufacturing implementation. In particular, we demonstrate how the value stream mapping (VSM) suite of tools can be used to map the current state of a production line and design a desired future state. Further, we provide a roadmap for how VSM can provide necessary information for analysis of equipment replacement decision problems encountered in lean manufacturing implementation.     

Integrated maintenance and operations decision making with imperfect degradation state observations

In highly flexible and integrated manufacturing systems, such as semiconductor fabs, strong interactions between the equipment condition, operations executed on the various machines and the outgoing product quality necessitate integrated decision making in the domains of maintenance scheduling and production operations. Furthermore, in highly complex manufacturing equipment, the underlying condition is not directly observable and can only be inferred probabilistically from the available sensor readings. In order to deal with interactions between maintenance and production operations in Flexible Manufacturing Systems (FMSs) in which equipment conditions are not perfectly observable, we propose in this paper a decision-making method based on a Partially Observable Markov Decision Processes (POMDP's), yielding an integrated policy in the realms of maintenance scheduling and production sequencing. Optimization was pursued using a metaheuristic method that used the results of discrete-event simulations of the underlying manufacturing system. The new approach is demonstrated in simulations of a generic semiconductor manufacturing cluster tool. The results showed that, regardless of uncertainties in the knowledge of actual equipment conditions, jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.     

A : A tool for identifying and repairing un-machinable shapes in designs

Providing early feedback on the manufacturability of a part design can greatly improve the quality of the product while reducing the time and cost of production. However, the necessary manufacturing knowledge is not always available. Computer tools that can provide this manufacturing knowledge by analyzing a design suggesting changes to improve its manufacturability would be a valuable asset to a designer. To this end, we present an approach to automatically generate redesign suggestions to improve the manufacturability of machined parts. Novel aspects of this approach include the ability to identify un-machinable shapes in a part and transform them into machinable features and to automatically identify the possible shape transformations based on properties of the machining equipment. This increases the scope of redesign generation tools by allowing them to be applied to parts that are not already machinable. We have developed a system called automated redesign for machined parts ( ) that assists users in repairing parts that contain un-machinable shapes.     

BiNem® manufacturing process for mass production

Abstract— As the need for high‐resolution ultra‐low‐power bistable displays grows, it is important to rapidly implement the mass‐production manufacturing of BiNem® LCDs. The cost‐effective approach for BiNem® manufacturing is based on using STN manufacturing process technologies since BiNem® and STN displays have a similar internal structure. The key differences between BiNem® and STN displays from a manufacturing point of view will be discussed. We show that industrial STN manufacturing equipment can be used to produce BiNem® LCDs at competitive costs. Reflective e‐book display modules with VGA resolution are produced in the pilot‐production series.     

An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design

In respond to new market requirements and competitive positioning of manufacturing companies selecting optimal machines that are consistent with manufacturing goals is of crucial importance. As it involves multiple conflicting criteria and inherent ambiguity and vagueness, election of a suitable machine can be regarded as a fuzzy multi-criteria decision making problem. In this study, for the first time in the literature, an integrated approach consisting of fuzzy simple multiattribute rating technique (SMART) approach and fuzzy weighted axiomatic design (FWAD) approach is proposed to determining the optimal continuous fluid bed tea dryer for a privately owned tea plant operating in Turkey. The weights of the evaluation criteria are calculated via fuzzy SMART and then FWAD is utilized to rank competing machine alternatives in terms of their overall performance. In the FWAD application phase, five experts have determined functional requirements (FRs) and have rated alternatives. Therefore, individual fuzzy opinions were required to be aggregated in order to set up a group consensus. A group decision analysis, referred to as the least squares distance method is used to aggregating the ratings of FRs and alternatives. It is concluded that the proposed hybrid methodology is a robust decision support tool for ranking machine alternatives under fuzzy environment and furthermore, it can be exploited for other fuzzy decision making problems, as well.     

Sequencing material handling equipment in production facilities

In this article we consider the problem of sequencing material handling equipment in manufacturing systems, subject to constraints that restrict the start and end time of each production activity, according to pre‐specified daily resource operational schedules and process planning information. The underlying decision problem is modeled using an integer programming formulation similar to vehicle routing with time windows (VRPTW). To take advantage of standard approaches for the VRPTW, we develop a transformation schema that allows a one‐to‐one mapping between the manufacturing problem and VRPTW. An efficient heuristic to solve the resulting transformed problem is proposed. The method, which is a penalty‐based sequential insertion heuristic, allows routes to be constructed by exploiting the tradeoff between material handling and resource starvation costs. The steps of the method are illustrated via a comprehensive example and results on benchmark problems are reported.     

基于维护计划的卷烟生产线设备运维管理系统

介绍了一个基于维护计划的卷烟生产线设备运维管理系统,分别从系统功能模型、计划业务流程等角度描述了系统的设计思想。并阐述了系统涉及的设备润滑计划制定、派工、运行维护数据分析、系统开发与应用等关键技术。开发的系统具有一定的可集成性、可扩展性和可重构性,与移动通讯技术、数据采集技术相结合,集成在制造执行系统中,可以实现卷烟生产企业制丝、卷包生产线的实时设备运行与维护保养管理。     

Fuzzy quality function deployment: determining the distributions of effort dedicated to technical change

A model is presented that develops the Quality Function Deployment (QFD) House of Quality tool into a fuzzy‐set based multi‐criteria decision‐making process to determine the distributions of effort directed toward technical changes. When customers are polled for desired product‐specific attributes, the responses are typically defined by linguistic variables that represent a fuzzy set distribution. Fuzzy sets also define customer perceptions of the product attributes and technical expert opinions about product design criteria relative to marketplace competitors. Therefore, for each technical criterion, the following factors have an effect on the decision to implement change(s): (1) fuzzy sets providing evidence of customer need for attributes; (2) motivation to change to satisfy desired customer attributes; (3) motivation for technical criterion change; and (4) the strength of the relationships between the attributes and the technical criterion. Priority rankings are linked to the distributions of effort to apply in fulfilling continuous product improvement.     

A predictive maintenance system for multi-granularity faults based on AdaBelief-BP neural network and fuzzy decision making

Predictive maintenance of production equipment is a prerequisite to ensure safe and reliable manufacturing operations. To avoid unexpected shutdown and even casualties caused by faults during production, it is paramount to design an effective predictive maintenance decision system for production equipment. Most of the related research works concentrate on early warn of specific faults but neglect the differentiations of the fault severity. To address the issue, this paper presents an intelligent predictive maintenance system for multi-granularity faults of production equipment based on the AdaBelief-BP (back propagation) neural network and the fuzzy decision making. The characteristics of the system presented in this paper include: (1) The proposed system implements a two-stage framework, integrating the functions of fault type prediction and fault degree prediction, which can provide comprehensive fault information throughout production lifecycles; (2) On the maintenance solution identification stage, fuzzy logic-based decision making is carried out to determine appropriate maintenance solutions based on the practical vague boundary of fault severity. In the system, the design of the AdaBelief-BP neural network can achieve a higher convergence rate and a better generalization capability as well. Meanwhile, to the best of our knowledge, in this research, it is the first time to use the migration of the fuzzy membership degree as the indicator of the changing condition of fault severity to facilitate more accurate maintenance solution identification. To verify the effectiveness of the system, comparison experiments with some popular algorithms are conducted. Benchmarking results show that the developed system can achieve higher prediction accuracy as well as higher efficiency than the comparative methods.     

A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control

The demands for mass individualization and networked collaborative manufacturing are increasing, bringing significant challenges to effectively organizing idle distributed manufacturing resources. To improve production efficiency and applicability in the distributed manufacturing environment, this paper proposes a multi-agent and cloud-edge orchestration framework for production control. A multi-agent system is established both at the cloud and the edge to achieve the operation mechanism of cloud-edge orchestration. By leveraging Digital Twin (DT) technology and Industrial Internet of Things (IIoT), real-time status data of the distributed manufacturing resources are collected and processed to perform the decision-making and manufacturing execution by the corresponding agent with permission. Based on the generated data of distributed shop floors and factories, the cloud production line model is established to support the optimal configuration of the distributed idle manufacturing resources by applying a systematic evaluation method and digital twin technology, which reflects the actual manufacturing scenario of the whole production process. In addition, a rescheduling decision prediction model for distributed control adjustment on the cloud is developed, which is driven by Convolutional Neural Network (CNN) combined with Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanism. A self-adaptive strategy that makes the real-time exceptions results available on the cloud production line for holistic rescheduling decisions is brought to make the distributed manufacturing resources intelligent enough to address the influences of different degrees of exceptions at the edge. The applicability and efficiency of the proposed framework are verified through a design case.     

Integration of fuzzy logic, genetic algorithms and neural networks in collaborative parametric design

The solution to a design problem is extracted through the exploitation of the design knowledge in the context of a space of solution alternatives. The design process incorporates a series of decision making and knowledge management issues, which should be often addressed through collaboration among diverse stakeholders. The alternative solutions must usually be shaped under different formalisms and evaluated against commonly accepted objective criteria.The current paper presents an approach that integrates soft-computing techniques in order to facilitate the computer-aided collaboration among designers. CopDeSC (Collaborative parametric Design with Soft-Computing) is the name of the system developed in order to implement this approach whose key features are: (a) the collaborative structuring of design parameter hierarchies, (b) the modeling of the design objectives through fuzzy preferences stated by the designers on certain design parameters, (c) the deployment of genetic algorithms for locating the optimum solution and (d) the utilization of records of elite solutions that are submitted in a neuro-fuzzy approximation in order to produce a simplified problem formulation, suitable for addressing redesign tasks in significantly less computational time.The efficiency of CopDeSC is evaluated in an example case of the parametric design of oscillating conveyor that has been conducted by a group of designers.     

A multigranular hierarchical linguistic model for design evaluation based on safety and cost analysis

Before implementing a design of a large engineering system different design proposals are evaluated. The information used by experts to evaluate different options may be vague and/or incomplete. Although different probabilistic tools and techniques have been used to deal with these kinds of problems, it seems better to use the fuzzy linguistic approach to model vagueness and the Dempster‐Shafter theory of evidence for modeling incompleteness and ignorance. In the evaluation of alternative designs, different criteria can be considered. In this article an evaluation process is developed in terms of Safety and Cost analysis. Both criteria involve uncertainty, vagueness, and ignorance due to their nature. Therefore, we propose an evaluation process defined in a linguistic framework where both criteria will be conducted in different utility spaces, i.e., in a multigranular linguistic domain. Once the evaluation framework has been defined, we present an evaluation process based on a Multi‐Expert Multi‐Criteria decision model that will be able to deal with multigranular linguistic information without loss of information in order to evaluate different design options for an engineering system in a precise manner. Accordingly, we propose the use of a multigranular linguistic model based on the Linguistic Hierarchies presented by Herrera and Martínez (“A model based on linguistic 2‐tuples for dealing with multigranularity hierarchical linguistic contexts in multi‐expert decision‐making.” IEEE Trans Syst Man Cybern B 2001;31(2):227–234). © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1161–1194, 2005.     

Optimal divestment time in supply chain redesign under oligopoly: evidence from shale oil production plants

This paper investigates the optimal timing for the closure of production plants in the context of supply chain redesign. We consider producers operating under oligopoly and analyze their optimal decisions with the aid of real options. We contextualize this study in the energy supply chain, where revenue is heavily influenced by the Organization of the Petroleum Exporting Countries (OPEC). It is optimal to divest from a production if certain values pass below the liquidation cost. The optimal time to divest is, then, the optimal time to exercise a perpetual American put option on the total value of the project, where the strike price is the liquidation value and expenses. The OPEC oligpolistic power is modeled with the aid of a hidden market in a Hidden Markov Model, since the state of the Markov chain is not directly visible to the production manager. The reported results can be used by supply chain managers in decision making on production network redesign in the oligopolistic environment considering present value of plants and cash flows in the supply chain. The advantage of the modeling approach proposed is its analytical tractability, especially for project valuation and real‐option problems.     

Real world applications of a fuzzy decision model based on relationships between goals for finance services and sequencing for the assembly of cars

Real world applications of a decision model of relationships between goals based on fuzzy relations are presented. In contrast to other approaches the relationships between decision goals or criteria for each decision situation are represented and calculated explicitly. The application fields are decision making for financial services and optimization of production sequences in car manufacturing.     

Group decision making in manufacturing systems: An approach using spatial preference information and indifference zone

Decision making is an essential activity in manufacturing systems when designing production lines, scheduling, etc. Many decision making problems are characterized by multiple conflicting criteria and a large number of alternatives. For these complex decision making problems, it is rational to involve a group of decision makers (DM) for considering different aspects of the problem. This paper proposes an approach for supporting the decision making group to reduce disagreement in the group and obtain a common solution. The proposed approach allows the DMs to specify a region of acceptance, known as indifference zone, in the objective space as preference inputs. This makes the proposed approach applicable to problems with a large number of alternatives. The use of indifference zone concept captures the uncertain nature of preference articulation. Moreover, the indifference zone is shown beneficial in reducing the difficulty of reaching a group common solution. The properties of the proposed method are investigated analytically and with numerical experiments. Finally, the usefulness of the proposed method is shown by tackling a real-world packaging line configuration problem with a large alternative set.     

A hybrid procedure for extracting rules of production performance in the automobile parts industry

In the manufacturing section, due to limitations of specific resources (e.g., time, people, and equipment), key determinants such as process capacity, human resources supply, and equipment availability may be in uncertain or out-of-control environments, followed by decreasing production performance. Traditionally, earlier studies of related issues of production performance usually used statistical methods for handling these problems. However, these methods become more complex when relationships in the input/output dataset are nonlinear. Furthermore, statistical techniques rely on the restrictive assumption on linear separability, multivariate normality and independence of the predictive variables; unfortunately, many of the common models of production performance violate these assumptions. To remedy these existing shortcomings, the study proposes a hybrid procedure that focuses on the opinions of experts, discretization of decision attributes, and application of well-known artificial intelligent (AI) approaches, such as decision trees (DT), artificial neural networks (ANN), and DT+ANN techniques, for objectively classifying production performance to solve real-world problems that are faced by the automobile parts industry. Two practically collected datasets are employed to verify the proposed hybrid procedure. The experimental results reveal that the proposed hybrid procedure is a good alternative to classify production performance from an intelligent manufacturing perspective objectively. Moreover, the output that is created by the DT C4.5 algorithm is a set of comprehensible and meaningful rules applied readily in knowledge-based performance-evaluating systems for manufacturing managers and HR division managers. The study findings and implications are of value to academicians and practitioners.     

Routine design with information content and fuzzy quality function deployment

Design can be classified into four basic categories: creative design, innovative design, redesign, and routine design. This paper describes a method for performing routine design by utilizing information content and fuzzy quality function deployment. An attempt has been made to associate with each critical characteristic of a product a value representing the information content, which is a measure of probability that a system can produce the parts as specified by the designer, using a specific manufacturing technology for making the parts. Once the information content of each design alternative is computed, the system will select an alternative with the minimum amount of information content. The proposed method provides us with a means for solving the critical design evaluation and validation problem.     

Establishing information requirements for supervisory controllers in a flexible manufacturing system using GTA

In this article we consider the technological change that has occurred in complex manufacturing systems within the past two decades and the implications it has had on the role of human operators in manufacturing systems control. Our examination ranges from the traditional production line manned by skilled machinists to flexible manufacturing systems (FMS) under supervisory control. On the basis of this study, we raise the question as to whether new advanced manufacturing technology interfaces are supportive of human operators in their responsibilities to manufacturing systems. We address this problem by analyzing supervisory controller information requirements for intervening in complex process control tasks as part of FMS operation. This analysis was conducted using a cognitive engineering research methodology, which has not previously been applied, in the domain of manufacturing. The method of GTA was applied to supervisory control of an FMS and produced detailed information requirements, which facilitated the formulation of general design guidelines for FMS interface design. The guidelines are aimed at supporting human operator process strategy development and decision making. © 2000 John Wiley & Sons, Inc.