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Cloud manufacturing (CMfg) provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing services on-demand. The service composition with optimal overall quality of service (QoS) is of great significance since it affects the efficiency of resource sharing and optimal allocation in CMfg systems. However, some limitations still exist in manufacturing service composition methods especially in the complicated cloud environment. This owes to two aspects: (1) current service composition approaches assume that the QoS of services is fixed and not influenced by other services, the existence of the service correlation context is not considered adequately, thus resulting in no accordance with practical applications; (2) with the growing number of candidate services in cloud resource pools, the traditional methods might be inefficient for addressing large scale service composition problems. To overcome such drawbacks, this study proposes a new hybrid teaching-learning-based optimization (HTLBO) algorithm for optimal service composition with the consideration of service correlations. Experiments are conducted to verify the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Hybrid manufacturing and remanufacturing systems have become a topic of considerable interest in the advanced manufacturing industry due in part to the profit and cost saving by reusing remaufacturable parts in the end-of-use products. In this paper, we investigate a production planning problem in such a hybrid system with the integration of resource capacity planning that is shared by both manufacturing and remanufacturing processes. Due to the uncertain nature in practice, both new and remanufactured product demands are stochastic. Taking a scenario-based approach to express the stochastic demands according to the historical data, we formulate the stochastic aggregate production planning problem as a mixed integer linear programming (MILP) model. Based on the Lagrangian relaxation (LR) technique, the MILP model is decomposed into four sets of sub-problems. For these sub-problems, four heuristic procedures are developed, respectively. Then, a LR based heuristic for the main problem is proposed and further tested on a large set of problem instances. The results show that the algorithm generates solutions very close to optimums in an acceptable time. At last, the impact of demands uncertainty on the solution is analyzed by the sensitivity analysis on a number of scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we review and illustrate applications, reported in the literature or used in our group, of 27 Al- 27 Al double-quantum single-quantum (DQ-SQ) MAS NMR experiments for the structural characterization of Al-containing microporous solids, namely zeolites, aluminophosphates and metal-organic frameworks. Information regarding the periodic frameworks or the localization of the various aluminum species in the materials are obtained from the analysis of the two-dimensional NMR spectra, which allows getting local structural details sometimes inaccessible from other characterization technique. An application of 27 Al- 27 Al of the DQ-SQ experiment for the detection of aluminum pairing in zeolite is shown. [ABSTRACT FROM AUTHOR]

Managing company in the lean way presumes no breakdowns nor reserves in the whole delivery chain. However, achieving such low indicators is impossible. That is why in some production plants it is extremely important to focus on preventive actions which can limit damages. This article depicts the method of designating the critical damages in accordance with lean maintenance method. The article consists of two parts which make for an integral body. Part one depicts the characteristic of a realistic object, it also contains productions capabilities analysis of certain areas within the production structure. Part two depicts the probabilistic model of shaping maximal time loss basing on emptying and filling interoperational buffers. [ABSTRACT FROM AUTHOR]

Recently, due to the significance of Industry 4.0, the manufacturing industry is developing globally. Conventionally, the manufacturing industry generates a large volume of data that is often related to process, line and products. In this paper, we analyzed causes of defective products in the manufacturing process using the decision tree technique, that is a well-known technique used in data mining. We used data collected from the domestic manufacturing industry that includes Manufacturing Execution System (MES), Point of Production (POP), equipment data accumulated directly in equipment, in-process/external air-conditioning sensors and static electricity. We propose to implement a model using C4.5 decision tree algorithm. Specifically, the proposed decision tree model is modeled based on components of a specific part. We propose to identify the state of products, where the defect occurred and compare it with the generated decision tree model to determine the cause of the defect. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Estudos Urbanos e Regionais is the property of Associacao Nacional de Pos-Graduacao e Pesquisa em Planejamento Urbano e Regional and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Personnel scheduling closes the missing link between personnel and production planning in manufacturing companies. Personnel scheduling has a significant impact on the development of employee competencies and the achievement of production goals. Nevertheless, in industrial practice the performance of the employee is not considered in production planning. According to the 5th Global Productivity Study of the Proudfoot Consulting 37% of working time is wasted, which is mainly due to a lack of planning and control. A major reason is the difficult measurability of employee competence and performance. In this paper, a method that describes the influence of employees on the processing and set-up time and its implementation at a manufacturer of thread parts is shown. For this, production data is statistically evaluated to predict the employee's influence. This information will be used by an algorithm for personnel scheduling. Thus, the highest possible competence development can be achieved in accordance with the utilization of the production. [ABSTRACT FROM AUTHOR]

Today’s turbulent global environment requires agility and flexibility of manufacturing companies to stay competitive. Thus, employees have to monitor their performance continuously and react quickly to turbulences which demands real-time information provisioning across all hierarchy levels. However, existing manufacturing IT systems, for example, manufacturing execution systems (MES), do hardly address information needs of individual employees on the shop floor. Besides, they do not exploit advanced analytics to generate novel insights for process optimisation. To address these issues, the operational process dashboard for manufacturing (OPDM) is presented, a mobile data-mining-based dashboard for workers and supervisors on the shop floor. It enables proactive optimisation by providing analytical information anywhere and anytime in the factory. In this paper, first, user groups and conceptual dashboard services are defined. Then, IT design issues of a mobile shop floor application on top of the advanced manufacturing analytics platform are investigated in order to realise the OPDM. This comprises the evaluation of different types of mobile devices, the development of an appropriate context model and the investigation of security issues. Finally, an evaluation in an automotive industry case is presented using a prototype in order to demonstrate the benefits of the OPDM for data-driven process improvement and agility in manufacturing. [ABSTRACT FROM PUBLISHER]

The study aims to present architecture and technology solution for collaborative manufacturing execution system oriented to discrete manufacturing enterprises. Under the networked manufacturing environment, the enterprises are facing challenges including the increasing manufacturing process complexity, the shorter product delivery, the higher customer satisfaction requirements, and so on. To deal with these challenges, the manufacturing enterprises must implement information management, while collaborative manufacturing execution system is one of the key technologies to realize informationization. Thus, in this study, the authors analyze production characteristics, typical production flow, and collaborative management requirements of multi-workshop production in the discrete manufacturing enterprise and propose an architecture of collaborative manufacturing execution system oriented to discrete manufacturing enterprises. The main functions of this system and key technologies to realize the system is also analyzed. Finally, a prototype system is developed and a case study is executed in Henan Pingyuan Optics Electronics Co., LTD in OPTO-Electronic Group to demonstrate the implementation and potentials of the system. [ABSTRACT FROM AUTHOR]

Foreign matter defect introduced during lithium-ion battery manufacturing process is one of the main reasons for battery thermal runaway. Therefore, reliable detection of the foreign matter defect is needed for safe and long-term operation of lithium-ion batteries. It is favored to detect the defective battery during the battery manufacturing process before the battery is put into use. In this study, the defects are implanted into batteries on a real battery pilot manufacturing line. Data of defective batteries and thousands of normal batteries are collected for data analyses and algorithm development. Feature selection is conducted with feature importance analysis using the random forest method and out-of-bag error calculation. Local outlier factor method is used for defect detection with the selected features as input. The proposed defect detection algorithm achieves high detection rate and low false alarm rate which has the potential to be deployed on the manufacturing execution system to further enhance screening ability of defective batteries and improve battery safety. • The first known application of the data-driven algorithms to solve the foreign matter defect detection problem. • Experiments are conducted with implanted foreign matter defect on battery pilot manufacturing line. • The proposed method achieves high precision, accuracy and recall compared with traditional HiPot test. [ABSTRACT FROM AUTHOR]

Energy efficiency improvement in the industrial sector is critical in achieving carbon neutrality by 2050. A discrete-event-based dynamic energy simulation method is suggested to enhance the efficiency of energy-intensive manufacturing processes. The statistical characteristics of an actual manufacturing site were reflected in the simulation using data from the factory energy management system (FEMS) and manufacturing execution system (MES). The proposed method integrates discrete event simulation (DES) and transient thermal analysis. The dynamic behavior resulting from the interaction between sub-processes is simulated in DES. The energy conversion and detailed heat loss occurring at the facilities were modeled in the transient thermal analysis. The concept of process loss was suggested for the analysis to account for the energy that could have been not wasted at the factory level. The simulation results provided three key performance indicators (KPIs) representing the energy usage, production, and product quality of a manufacturing site. Investigation of these KPIs under various operating conditions found that the specific energy consumption could be reduced by 12.6% with a better operating strategy. We expect that the integrated simulation method can provide a practical solution for improving the energy efficiency of manufacturing sites equipped with a FEMS. • The dynamic behavior in energy usage of a manufacturing process is simulated. • Input modeling for the simulation is done using MES and EMS data of a real foundry. • The temperature history of the metal charge is predicted by the transient thermal model. • Process loss concept is suggested to consider factory-level energy efficiency. • SEC reduces by 12.6% while maintaining product quality through better operation. [ABSTRACT FROM AUTHOR]

Computer implementation of mathematical models, algorithms, and computer programs is given for resolving a set of production problems in the field of blast furnace production introduced into Magnitogorsk Metallurgical Combine. Requirements are considered for the structure and architecture of a computer system for support of solutions adopted for an MES-level Blast Furnace Technologist workstation. A short description is provided for the main model sub-systems, and also assumptions made during mathematical modeling. Use of the systems makes it possible for engineering and production personnel to perform operational analysis of a blast furnace workshop situation, to resolve a number of production problems for controlling blast furnace heat, gas-dynamic, and slag regimes, and also to calculate the optimum blast furnace charge composition, which finally provides an improvement in blast furnace production technical and economic operating indices. [ABSTRACT FROM AUTHOR]

As a type of process plant, a refinery is characterized by the interaction of discrete events and continuous processes. To schedule crude oil operations in a refinery, it is necessary to define and schedule the jobs simultaneously such that heuristics and meta-heuristics cannot be directly applied. It is very challenging to schedule crude oil operations. To solve this problem, it is decomposed into two subproblems hierarchically. At the upper level, a refining schedule is found, while at the lower level a detailed schedule is obtained to realize the refining schedule. Given a refining schedule at the upper level, this paper studies the detailed scheduling problem at the lower level. Based on a control-theoretic perspective, the problem is innovatively transformed to a problem of assigning charging tanks to distillers such that meta-heuristic methods can be applied. Then, a genetic algorithm (GA) approach is developed to solve the problem. In realizing the proposed GA, based on a set of existence conditions of a feasible schedule, methods are presented to guarantee that each chromosome corresponds to a feasible schedule. An industrial case study is used to show the application of the proposed method. It shows that the method works well and is applicable to real-life problems. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Industrial chemical plant diagnosis is the task of analyzing process data to sufficiently pinpoint the causes of abnormal events as fast and as accurately as possible so corrective action can be taken in a timely manner. The need to identify failures explicitly and support human centered decision making becomes pronounced for enterprises. Qualitative diagnostic models offer robustness in capturing diagnostic behaviors when there is little or no data on fault conditions. This article develops, analyzes, and demonstrates a qualitative diagnostic methodology called Causal Link Assessment (CLA). CLA avoids the drawbacks of other methodologies while leveraging several new concepts that include dynamic pattern generation, single time step modeling with multitime step interpretation, and discretized, low granularity dynamic modeling. CLA is demonstrated for an existing ethylene production facility. Model building, robustness, reusability, unaccounted for faults and failures and alignment with emerging Smart Manufacturing infrastructure concepts are discussed. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3420-3431, 2016 [ABSTRACT FROM AUTHOR]

The output performance of the manufacturing system has a direct impact on the mechanical product quality. For guaranteeing product quality and production cost, many firms try to research the crucial issues on reliability of the manufacturing system with small sample data, to evaluate whether the manufacturing system is capable or not. The existing reliability methods depend on a known probability distribution or vast test data. However, the population performances of complex systems become uncertain as processing time; namely, their probability distributions are unknown, if the existing methods are still taken into account; it is ineffective. This paper proposes a novel evaluation method based on poor information to settle the problems of reliability of the running state of a manufacturing system under the condition of small sample sizes with a known or unknown probability distribution. Via grey bootstrap method, maximum entropy principle, and Poisson process, the experimental investigation on reliability evaluation for the running state of the manufacturing system shows that, under the best confidence level P=0.95, if the reliability degree of achieving running quality is r>0.65, the intersection area between the inspection data and the intrinsic data is A(T)>0.3 and the variation probability of the inspection data is PB(T)≤0.7, and the running state of the manufacturing system is reliable; otherwise, it is not reliable. And the sensitivity analysis regarding the size of the samples can show that the size of the samples has no effect on the evaluation results obtained by the evaluation method. The evaluation method proposed provides the scientific decision and suggestion for judging the running state of the manufacturing system reasonably, which is efficient, profitable, and organized. [ABSTRACT FROM AUTHOR]

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper addresses an emergency scheduling for manufacturing resources (ESMR) problem and a method based on event-driven rolling optimization according to the production task priority is developed for solving it. A mathematical model aiming to maximum resource scheduling satisfaction, minimize tardiness penalties and minimize crash cost is proposed firstly. Then, the priority of the tasks in rolling window is analysed based on production system vulnerability. An adaptive multi-objective dynamic resource scheduling algorithm is proposed as a solution of the model above. Analytic results show that it is not only able to effectively reduce the complexity of ESMR, but also to reveal influence of task importance on ESMR. With the analysis, it can provide way of measuring the overall emergency impacts on the systems, and allow the decision-makers to respond to unexpected events that what the best way of resources scheduling is. [ABSTRACT FROM AUTHOR]

Cloud manufacturing brings new opportunities for manufacturing enterprises, but in the meantime it also brings new challenges to the design and implementation of manufacturing execution system (MES). To solve the issues of "job shop scheduling" in single and small batch MES,firstly we design a closed-loop architecture that is from static scheduling to real-time monitoring and active perception of manufacturing execution, then to intelligent response of abnormal events, and finally, to dynamic scheduling. Then for solving three sub issues, i. e. real-time acquisition of exception information and discovery of abnormal events, intelligent processing of abnormal events and servitization of job shop scheduling optimization algorithms, we analyze them and provide technical solutions. Finally, taking Harbin Electrical Machinery Plant as a case,and combining IEC/ISO 62264 standard,big data analysis and mining method,and the cloud computing method consisting of virtualization, servitization and SOA together,we develop an integrated job shop scheduling optimization system of single and small batch MES,and the aforementioned theory and method are validated. [ABSTRACT FROM AUTHOR]

The execution of construction projects such as a highway construction or the elevation of a new bridge is a complex, highly equipment-intensive process and are subject to many different uncertainties. This is very similar to the manufacturing execution level in production systems where predefined productions plans and schedules cannot be completely implemented due to unexpected internal and external changes and disturbances. Following this analogy, the paper proposes the application of a discrete-event simulation based method which was already applied in the decision-support for manufacturing control to develop the decision-support in the execution of a construction project where the effects of the deviation from the short-term schedule can be easily and quickly analyzed. [ABSTRACT FROM AUTHOR]

Manufacturing execution systems (MES) have recently been introduced to monitor various manufacturing objects (MOs) in dynamic shop floors; they can leverage the efficiency of information flow across functional layers for planning and control. However, current MES practices using traditional indoor positioning algorithms face several difficulties in tracking MOs for wireless manufacturing: (1) inefficient wireless data acquisition in the shop-floor environment, (2) lack of a reliable and accurate real-time signal processing method for handling massive signal data, and (3) the positions of reference objects cannot be treated as in a static environment when unknown manufacturing orders arrive in a streaming manner. This paper proposes to handle the first challenge by adopting RFID technology that can constantly capture the wireless signals sent from tags mounted on various MOs. The second difficulty can be solved by applying the online sequential extreme learning machine (OS-ELM) that inherits the elegant properties of ELM in terms of extremely fast learning speed and high generalization performance. The OS-ELM based positioning method also addresses the third issue in which an online localization model has been constructed in a streaming manner. The proposed method can greatly reduce the training time without costly retraining of the previously trained data together with the newly arrived data. With the novel OS-ELM based RFID positioning framework, the MOs are upgraded to smart manufacturing objects (SMOs), and the processes are enhanced with real-time signal processing and intelligent tracking capabilities. The experimental results verify that the proposed positioning method is superior to other state-of-the-art algorithms in terms of accuracy, efficiency, and robustness. [ABSTRACT FROM AUTHOR]

The paper is dedicated to the description of problems that occur during the functional modelling of MES (manufacturing execution system) class information systems. It was determined that the most significant problem is the lack of the universality of information systems. According to preliminary studies, the universality is achieved by using a process approach during design. Thus, it requires the improvement of the design process. To solve this issue, specifics were discussed to apply different approaches to the design of information systems at the lowest level. It helped to make a conclusion about the correct choice for the design approach. During functional modelling, the emphasis was on networked information systems with complete or partial virtualization. For this task, a model of the information system was built according to features of service-oriented architecture. At the stage of selecting CASE-tools, the accent was on the use of WEB-oriented tools. These steps led to the unification of the design and implementation of MES class information systems, and it will increase the universality of the developed information systems. [ABSTRACT FROM AUTHOR]