RFID Data Reliability Optimiser Based on Two Dimensions Bloom Filter

Radio frequency identification (RFID) is a flexible deployment technology that has been adopted in many applications especially in supply chain management. RFID system used radio waves to perform wireless interaction to detect and read data from the tagged object. However, RFID data streams contain a lot of false positive and duplicate readings. Both types of readings need to be removes to ensure reliability of information produced from the data streams. In this paper, a single approach, which based on Bloom filter was proposed to remove both dirty data from the RFID data streams. The noise and duplicate data filtering algorithm was constructed based on bloom filter. There are two bloom filters in one algorithm where each filter holds function either to remove noise data and to recognize data as correct reading from duplicate data reading. Experimental results show that our proposed approach outperformed other existing approaches in terms of data reliability

From Open CNC systems to Cyber-Physical machine tools: a case study

The aim of next-generation Computer Numerical Control (CNC) is shifting from an open architecture, which has better flexibility, adaptability, versatility and expansibility, to a cyber-physical model, which offers real-time monitoring and control of the machining processes. This paper introduces a real case study to demonstrate such tendency from Open CNC systems to Cyber-Physical Machine Tools (CPMT) based on a low-power embedded platform. Firstly, a new open CNC architecture is presented, which is able to achieve high-precision, high-efficiency, and low-power consumption. Secondly, the open CNC architecture is extended to a CPMT by using Wireless Sensor Networks (WSN), where WSN is utilized to enable monitor and control the machining processes, and the integrated development platform is termed as CPMT. Finally, a case of health monitoring system for CPMT is designed and its system testing is carried out

An Efficient Constructive Heuristic to Balance Trade-Offs Between Makespan and Flowtime in Permutation Flow Shop Scheduling

Balancing trade-offs between production cost and holding cost is critical for production and operations management. Utilization of a production line affects production cost, which relates to makespan, and work-in-process (WIP) inventories in a production line affect holding cost, which relate to flowtime. There are trade-offs between two objectives, to minimize makespan and to minimize flowtime. Without addressing trade-off balancing issues in flow shop scheduling, WIP inventories are still high in manufacturing, generating unnecessary holding cost. However, utilization is coupled with WIP inventories. Low WIP inventory levels might lower utilization and generate high production cost. Most existing constructive heuristics focus only on single-objective optimization. In the current literature, the NEH heuristic proposed by Nawaz, Enscore, and Ham (1983) is the best constructive heuristic to minimize makespan, and the LR heuristic proposed by Liu and Reeves (2001) is the best to minimize flowtime. In this paper, we propose a current and future deviation (CFD) heuristic to balance trade-offs between makespan and flowtime minimizations. Based on 5400 randomly generated instances, 120 instances in Taillard’s benchmarks, and one-year historical records of operating room scheduling from University of Kentucky HealthCare (UKHC), our CFD heuristic outperforms the NEH and LR heuristics on trade-off balancing, and achieves the most stable performances from the perspective of statistical process control (SPC)

Blockchain-based life cycle assessment: An implementation framework and system architecture

Life cycle assessment (LCA) is widely used for assessing the environmental impacts of a product or service. Collecting reliable data is a major challenge in LCA due to the complexities involved in the tracking and quantifying inputs and outputs at multiple supply chain stages. Blockchain technology offers an ideal solution to overcome the challenge in sustainable supply chain management. Its use in combination with internet-of-things (IoT) and big data analytics and visualization can help organizations achieve operational excellence in conducting LCA for improving supply chain sustainability. This research develops a framework to guide the implementation of Blockchain-based LCA. It proposes a system architecture that integrates the use of Blockchain, IoT, and big data analytics and visualization. The proposed implementation framework and system architecture were validated by practitioners who were experienced with Blockchain applications. The research also analyzes system implementation costs and discusses potential issues and solutions, as well as managerial and policy implications

RFID data reliability optimizer based on two dimensions bloom filter

Radio Frequency Identification (RFID) is a flexible deployment technology that has been adopted in many applications especially in supply chain management. It provides several features such as to monitor, to identify and to track specific item hidden in a large group of objects in a short range of time. RFID system uses radio waves to perform wireless interaction to detect and read data from the tagged object. However, RFID data streams contain a lot of false positive and duplicate readings. Both types of readings need to be removed to ensure reliability of information produced from the data streams. A small occurrence of false positive can change the whole information, while duplicate readings unnecessarily occupied storage and processing resources. Many approaches have been proposed to remove false positive and duplicate readings, but they are done separately. These readings exist in the same data stream and must be removed using a single mechanism only. In this thesis, an efficient approach based on Bloom filters was proposed to remove both noisy and duplicate data from the RFID data streams. The noise and duplicate filter algorithm was constructed based on bloom filter. There are two bloom filters in one algorithm where each filter holds function either to remove noise data and to recognize data as correct reading from duplicate data reading. In order to test the algorithm, synthetic data was generated by using Poisson distribution. The simulation results show that our proposed approach outperformed other existing approaches in terms of data reliability

A Knowledge Enriched Computational Model to Support Lifecycle Activities of Computational Models in Smart Manufacturing

Due to the needs in supporting lifecycle activities of computational models in Smart Manufacturing (SM), a Knowledge Enriched Computational Model (KECM) is proposed in this dissertation to capture and integrate domain knowledge with standardized computational models. The KECM captures domain knowledge into information model(s), physics-based model(s), and rationales. To support model development in a distributed environment, the KECM can be used as the medium for formal information sharing between model developers. A case study has been developed to demonstrate the utilization of the KECM in supporting the construction of a Bayesian Network model. To support the deployment of computational models in SM systems, the KECM can be used for data integration between computational models and SM systems. A case study has been developed to show the deployment of a Constraint Programming optimization model into a Business To Manufacturing Markup Language (B2MML) -based system. In another situation where multiple computational models need to be deployed, the KECM can be used to support the combination of computational models. A case study has been developed to show the combination of an Agent-based model and a Decision Tree model using the KECM. To support model retrieval, a semantics-based method is suggested in this dissertation. As an example, a dispatching rule model retrieval problem has been addressed with a semantics-based approach. The semantics-based approach has been verified and it demonstrates good capability in using the KECM to retrieve computational models

A model for evaluation of organizational and technological concepts in themanufacturing sector from the perspective of Industry 4.0

Истраживање у докторској дисертацији односи се на примену организационих и технолошких концепата у прерађивачком сектору, са акцентом на њихов допринос савременим трендовима производње, дефинисаним у оквиру Индустрије 4.0. Основни циљ истраживања је да се развије концептуални модел за евалуацију организационих и технолошких концепата у прерађивачком сектору, а након тога да се модел емпиријски примени са циљем утврђивања специфичних концепата који, посматрано из перспективе савремених производних трендова, имају највећи значај за компаније. За сврхе овог истраживања коришћене су методе за вишекритеријумско одлучивање. Развијени модел може се користити у будућим истраживањима, док се резултати могу користити за стратешку оријентацију компанија које су заинтересоване да се прилагоде и да послују у складу са савременим трендовима производње.Istraživanje u doktorskoj disertaciji odnosi se na primenu organizacionih i tehnoloških koncepata u prerađivačkom sektoru, sa akcentom na njihov doprinos savremenim trendovima proizvodnje, definisanim u okviru Industrije 4.0. Osnovni cilj istraživanja je da se razvije konceptualni model za evaluaciju organizacionih i tehnoloških koncepata u prerađivačkom sektoru, a nakon toga da se model empirijski primeni sa ciljem utvrđivanja specifičnih koncepata koji, posmatrano iz perspektive savremenih proizvodnih trendova, imaju najveći značaj za kompanije. Za svrhe ovog istraživanja korišćene su metode za višekriterijumsko odlučivanje. Razvijeni model može se koristiti u budućim istraživanjima, dok se rezultati mogu koristiti za stratešku orijentaciju kompanija koje su zainteresovane da se prilagode i da posluju u skladu sa savremenim trendovima proizvodnje.This research is related to the use of organizational and technological concepts in manufacturing companies, highlighting their contribution to the production principles of Industry 4.0. The main purpose of this research is to develop a model for evaluation of organizational and technological concepts in the manufacturing sector from the perspective of Industry 4.0. Consequently, the model was used to determine which organizational and technological concepts are contributing the most to the production principles of Industry 4.0. The developed model could be used in future research, while the results presented in this research could serve for strategic orientation of manufacturers