Intelligent and integrated RFID (Ⅱ-RFID) system for improving traceability in manufacturing

In the wake of globalization,many modern manufacturing companies in Norway have come under intense pressure caused by increased competition,stricter government regulation,and customer demand for higher value at low cost in a short time.Manufacturing companies need traceability,which means a real-time view into thenproduction processes and operations.Radio frequency identification(RFID) technology enables manufacturing companies to gain instant traceability and visibility because it handles manufactured goods,materials and processes transparently.RFID has become an important driver in manufacturing and supply chain activities.However,there is still a challenge in effectively deploying RFID in manufacturing.This paper describes the importance for Norwegian manufacturing companies to implement RFID technology,and shows how the intelligent and integrated RFID(n-RFID) system,which has been developed in the Knowledge Discovery Laboratory of Norwegian University of Science and Technology,provides instant traceability and visibility into manufacturing processes.It supports the Norwegian manufacturing industries survive and thrive in global competition.The future research work will focus on the field of RFID data mining to support decision-making process in manufacturing.     

Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors

Physical Internet (PI, π) has been widely used for transforming and upgrading the logistics and supply chain management worldwide. This study extends the PI concept into manufacturing shop floors where typical logistics resources are converted into smart manufacturing objects (SMOs) using Internet of Things (IoT) and wireless technologies to create a RFID-enabled intelligent shop floor environment. In such PI-based environment, enormous RFID data could be captured and collected. This study introduces a Big Data Analytics for RFID logistics data by defining different behaviours of SMOs. Several findings are significant. It is observed that task weight is primarily considered in the logistics decision-making in this case. Additionally, the highest residence time occurs in a buffer with the value of 12.17 (unit of time) which is 40.57% of the total delivery time. That implies the high work-in-progress inventory level in this buffer. Key findings and observations are generated into managerial implications, which are useful for various users to make logistics decisions under PI-enabled intelligent shop floors.     

基于智能对象的SMT车间制造执行系统研究

为了实现电子产品制造过程中对SMT车间的实时、精细化管控,本文提出了一种基于智能对象的SMT车间制造执行系统。通过引入RFID技术与智能对象,给出了基于智能对象的SMT行业制造执行系统框架。在介绍RFID在车间的系统化部署的基础上,详细分析了SMT车间数据采集模式,并描述了贴装过程物料防错流程。最后以广州某电子企业为例,说明了所构建系统的可行性和有效性。     

Enhancing smart shop floor management with ubiquitous augmented reality

This paper describes a framework for implementing Smart manufacturing Shop floor systems based on the Ubiquitous Augmented Reality technology (SSUAR). The proposed system makes use of data sharing between shop floor resources and a sensor network in order to optimise the production schedules for carrying out projects. The optimisation is performed in real-time and the production scheduling responds to new projects as well as the changing status of resources, such as machines and workers. Ubiquitous augmented reality interface has been developed and utilised as a user interface for the shop floor workers to receive information, instructions and guidance from the experts and manufacturing systems, and to update the systems on task parameters, such as estimated completion times, progress and machine status. A review of related work, methodology and implementation of the proposed system, and a case study are presented in this paper. Using this architecture, real-time scheduling of tasks in the smart shop floor can be achieved. The case study demonstrated the ability of SSUAR to integrate task scheduling with two-way communication between the system and the users.     

Human resource optimisation through semantically enriched data

The industrial domain is experiencing a so-called fourth industrial revolution in which the evergrowing complexity of manufacturing information, the increasing amount of knowledge and the use of web-oriented techniques, represent three crucial factors that are accelerating the growth of complexity of industrial systems. On the other hand, continuous-evolving requirements in industrial environments, due to technology outbreaks and a new global marketplace, have led to an on-going evolution of human resource management through the creation and adoption of alternative business models. In the past decade, semantic models such as ontologies have been proven to be effective for many knowledge-intensive applications, since they provide formal models of domain knowledge that can be exploited in different ways. For all these reasons, an innovative human resource optimisation (HRO) engine is introduced, which employs semantically enhanced information and conditional random field (CRFs) probabilistic models with knowledge derived from industrial shop floor level, and proposes the right person for the right job in real-time shop floor operations towards optimising decisions on how to implement and schedule either repeatedly or non-occurring tasks. Industrial information data flow and semantic enrichment were ensured through the combined use of a common interface data exchange model (CIDEM) and ontologies, after which a feasibility study at a chemical plant presented interesting preliminary results.     

Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors

Smart manufacturing is the core idea of the fourth industrial evolution. For a smart manufacturing shop floor, real-time monitoring, simulation and prediction of manufacturing operations are vital to improve the production efficiency and flexibility. In this paper, the Cyber-Physical System (CPS) and Digital Twin technologies are introduced to build the interconnection and interoperability of a physical shop floor and corresponding cybershop floor. A Digital Twin-based Cyber-Physical Production System (DT-CPPS) is further established, and the configuring mechanism, operating mechanism and real-time data-driven operations control of DT-CPPS are discussed in detail. It is expected that DT-CPPS will provide the basis for shop floors to march towards smart manufacturing.     

酒品包装生产中动态缓冲管理机制

考虑存在资源约束的流线式酒品包装生产车间,为了减少企业生产成本,提出了一种以控制在制品(WIP)库存为主要参考指标的动态缓冲管理方法。本方法通过实时监控缓冲区的WIP库存,根据在监控窗口中缓冲区出现WIP库存由高于安全下限变化到低于安全下限的频率以及随后是否出现WIP库存耗尽的情况,对库存安全上下限进行动态调节进而实现对缓冲的动态管理。为了验证方法的有效性,安排了3组不同的仿真实验。实验结果表明：与传统的缓冲管理策略相比,基于动态缓冲管理策略的控制方法在WIP库存控制方面存在22%以上的优势,采用该方法可以有效的控制生产系统中的在制品库存。     

RFID-enabled product-service system for automotive part and accessory manufacturing alliances

Automotive part and accessory manufacturers (APAMs) at the lower tiers of automotive vertical supply chains have been responding to major initiatives taken by leading vehicle assemblers in adopting RFID (radio frequency identification) and ubiquitous computing technologies to alleviate their manufacturing systems. RFID-enabled real-time traceability and visibility facilitate and enhance the implementation of advanced strategies such as just-in-time (JIT) lean/responsive manufacturing and mass customisation (MC). This paper reports on findings gained from a series of industrial field and pilot studies conducted within collaborating companies. Being typically small and medium sized, APAMs are faced with business and technical challenges that are summarised by the so-called ‘three high problems’, namely high cost, high risk and high level of technical skills. This research takes a more cost-effective pragmatic approach to overcome the ‘three high problems’ by sharing out the problems among APAMs while taking a longer-term, expensive and lengthy, approach to absolutely reduce the problems. The sharing approach requires the establishment of an innovative service-oriented framework, abbreviated AUTOPS, based on the Product Service Systems (PSS) business model. RFID hardware devices are innovated into gateways as hardcore products to formulate a PSS. RFID-enabled real-time services are deployed at a common platform across members of an APAMs alliance. AUTOPS facilities are shared by APAM alliance members to reduce the start-up investment costs, reduce the level of required specialist skills, speed up installation processes and streamline maintenance services, and improve the reliability of the RFID gateway services.     

Choices in manufacturing strategy decision areas in batch production system – six case studies

The configuration of a production system can be described by the choices a firm makes in its manufacturing decision areas. Manufacturing strategy literature lacks empirical research in manufacturing decision areas. The current paper is an exploratory study using six case companies on alternative configurations that can exist in a batch production system. Choices made in decisions such as layout, shop floor control, etc., were found to be similar for all six companies that use batch process. However, there were a number of decisions that were found to be non-process specific and are influenced by product complexity, important competitive priorities, strategic orientation of manufacturing, top management decisions and the size of the company. The paper concludes with managerial implications and future research directions.     

虚拟车间动态重构模型的研究

阐述了敏捷制造模式下，车间的功能除了具有常规的生产管理和控制功能外，还具有实现单元动态重构的功能和通过网络对外合作的功能。在分析了虚拟车间动态重构的系统的关键技术的基础上，提出了基于多代理机的虚拟车间动态重构模型，讨论了Ａｇｅｎｔ之间的协商机制。     

Collaborative agents for production planning and scheduling (CAPPS): a challenge to develop a new software system architecture for manufacturing management in Japan

Considering the frequent changes in market needs, real-time production management will produce huge additional value for manufacturers. As scheduling of shop floor operations is a local and partial decision in enterprises, the distributed local scheduling problems should be integrated at the enterprise level. Since decisions at the shop floor are very valuable, the next generation of production management will be performed as a decentralized system. This paper proposes architecture for scheduling-intensive production management. In the architecture, referred to as collaborative agents for production planning and scheduling, system integration is only achieved by communication among agents, i.e. a system is composed of loose connections between agents. As an implementation method, the paper introduces a standard specification for the communications among agents. The specification is recommended by PSLX, the Japan-based consortium for production planning and scheduling. The paper also shows some emerging business models on scheduling-intensive production management.     

Work-in-process clearing in supply chain operations planning

In this study we provide insights into the effectiveness of the clearing function concept in a hierarchical planning context. The clearing function is a mathematical representation of the relation between the Work-In-Process (WIP) and the throughput of a production process. We use it in a deterministic order release planning model to anticipate the dynamics of the operational level, which is subject to uncertainties. A multi-period single-item order release and scheduling problem is considered in which the delivery schedule of the orders is determined through the planned lead times, and the capacity loading decisions are separated from the order release decisions in a way so as to plan for on-time deliveries. Early or late delivery of the orders, which are released and delivered in batches, has not been considered explicitly in previous studies on clearing functions, and it significantly affects the inventory costs. Both linear and non-linear clearing functions are tested using a simulation approach. The results indicate that modeling the clearing of WIP by a shop should be based on the short-term operational dynamics of the shop rather than on the long-term average shop behavior, since it improves the consistency between the operational planning and scheduling levels of the hierarchy. The presented methods and results provide valuable information on modeling production characteristics in aggregate production planning and scheduling models.     

Bid generation and evaluation for MANPro-based real time scheduling

This paper proposes a bid generation and evaluation scheme and an information model for real-time scheduling. It is well known that an agent-based control approach performs according to both the negotiation mechanism itself and the single agent internal strategic policies. Mobile agent-based negotiation process (MANPro), which has been proposed in our previous work, concerns the first issue of the negotiation mechanism. This paper concerns the second issue of the single agent internal strategic policies. It explains how bids are generated and evaluated and how agents obtain mutual agreement in MANPro. First, the generic bid generation and evaluation framework for MANPro-based negotiation is proposed. Then, the generic framework is applied to a real-time scheduling system for a distributed shop floor control system (SFCS).     

Performance evaluation of an auction-based manufacturing system using coloured Petri nets

The next generation of manufacturing systems is assumed to be intelligent enough to make decisions and automatically adjust to variations in production demand, shop-floor breakdowns etc. Auction-based manufacturing is a control strategy in which various intelligent entities in the manufacturing system bid themselves, accept bids and make selections among the bids available based on a heuristic. This paper deals with the simulation modelling and performance evaluation of a push-type auction (negotiation) based manufacturing system embedded in a pulltype production system using coloured Petri nets. Three different models of an auction-based manufacturing system have been discussed. This methodology helps in developing systems for real-time control, anticipation of deadlocks, and evaluation of various performance metrics like machine utilization, automated guided vehicle (AGV) utilization, waiting times, work in process (WIP) etc. Various decision-making rules were identified for the real-time control of auction-based manufacturing systems.     

Load smoothing with feedback in a bottleneck job shop

Most of the past research on job shop scheduling has assumed the shop environment where the load-smoothing function in the production planning and control system is ignored and consequently no visibility is provided to the shop. In practice, some kind of load-smoothing is used to smooth the work load level of the shop across the periods, by pulling jobs forward or pushing jobs back. In this study, three load-smoothing approaches with two levels of control for each approach are proposed and tested with two order review/release decisions in a bottleneck job shop. No smoothing becomes a benchmark. Also, the effectiveness of a feedback loop between load-smoothing and the shop floor is investigated. Experiments were conducted in a six-machine job shop simulation model. Results showed that the employment of load-smoothing is important, and pulling jobs forward in a valley period is better than pushing back jobs in a peak period. Controlling the release of jobs to the shop floor in the order review/release phase, given the amount of jobs to be processed during the planning period, is not effective. Also, the feedback system between the planning phase and shop floor to maintain the minimum shop load becomes much more important than simply controlling job release time.     

RFID-based multi-attribute logistics information processing and anomaly mining in production logistics

Timely collecting logistics information and finding anomalies of material supply plays a critical role in modern manufacturing systems. The problem is how to obtain multi-attribute logistics information of production logistics and build an effective approach for mining anomalies from the huge number of RFID data. The multi-attribute, randomness and various measure units of logistics states further aggravate the problem. In this paper, a novel RFID-based logistics information processing approach is proposed. Firstly, the state features of production logistics is discussed from multi-attribute perspectives including time, location, quantities, sequence and path, and a set of calculating models is set up to process RFID data for getting multi-attribute state data. Furthermore, in case of the randomness and various measure units of state data, a similarity model is presented to unify measure units of state data, and a clustering approach is proposed to divide the huge number of RFID data into different clusters with high close degree for finding out anomalies. Lastly, the experimental results show that the proposed approach can efficiently find out more than 90% of anomalies among production logistics.     

Designing data warehouses for equipment management system with genetic algorithms

Effective equipment management is becoming one of the key factors in keeping a competitive advantage in the dynamic business environment since equipment is an important asset for manufacturing companies. Nowadays, maintenance administration has become one of the most important tasks in equipment management, particularly in manufacturing industries. Equipment management system (EMS) aims at reducing maintenance cost and production loss caused by machine breakdown. In addition, EMS can assist equipment engineers to make the right maintenance decisions at the right time, and at the right shop floor. Traditional computerized maintenance management systems (CMMS) have helped equipment engineers to deal with maintenance operations, but they lack decision support capability. In this paper, we design a data warehouse (DW) for EMS to help equipment engineers make maintenance decisions with various equipment related dimensions to improve effectiveness. A set of cubes can be built from EMS DW for the purpose of decision-making. In order to achieve a reasonable query response time under the memory space limit, a mechanism of partial materialization based on genetic algorithms (GAs) is adopted to design data cubes in the EMS DW. From the computational results the proposed GA-based approach for cube design can be applied to effectively select the appropriate multi-dimensional views for equipment management.     

Negotiating price/delivery date in a stochastic manufacturing environment

We study a make-to-order manufacturing system consisting of several processing centers that are subject to failures and repairs. Our objective is to build a model that can be used as a tool for negotiating the delivery date and the price of a certain upcoming order. The model takes into account the congestion level of the shop floor at the time the order is placed. Based on the workload of the processing centers, the model splits the order into lots and assigns them to the processing centers so as to determine the order completion time associated with the minimum operating cost. The efficiency of the solution method for the model allows real-time decision-making while negotiating the price and delivery date of the order to be placed. Since the decisions are made based on a snapshot of the congestion level at the shop floor, using this model will reduce the conflict between the marketing and the production activities in manufacturing organizations.     

Effectivity date analysis and scheduling

The impact of design on logistics cannot be ignored, and design for logistics is a new concept similar to design for manufacturing or design for assembly. Engineering change is one of the scenarios that would require logistics support. Change control of a product data management (PDM) system is one of the major approaches for handling engineering changes today. According to principles of configuration management, during the change control workflow, there are three different dates: release date, effective date, and effectivity date utilised for controlling and managing change planning and scheduling. Effective date is the exact date that a released change takes effect to the shop floor workshop. Effectivity date is the expected date that decision makers plan for the change to take effect. In normal situations, multiple disciplines, such as design and development, purchasing, shop floor workshop, quality control, and so on, are involved in making a change decision on when a change is to become effective. In this paper, a linear programming effectivity decision model is proposed to concurrently support changes of design scheduling, and production planning and scheduling when an engineering change occurs. The proposed model succeeded in solving an integration problem of design scheduling, production planning and shop floor scheduling.