A case of implementing RFID-based real-time shop-floor material management for household electrical appliance manufacturers

Radio Frequency Identification (RFID) technologies provide automatic and accurate object data capturing capability and enable real-time object visibility and traceability. Potential benefits have been widely reported for improving manufacturing shop-floor management. However, reports on how such potentials come true in real-life shop-floor daily operations are very limited. As a result, skeptics overwhelm enthusiasm. This paper contributes to the re-vitalization of RFID efforts in manufacturing industries by presenting a real-life case study of applying RFID for managing material distribution in a complex assembly shop-floor at a large air conditioner manufacturer. The case study discusses how technical, social and organizational issues have been addressed throughout the project within the company. It is hoped that insights and lessons gained be generalized for future efforts across household electrical appliance manufacturers that share similar shop-floor.     

RFID-based wireless manufacturing for walking-worker assembly islands with fixed-position layouts

A walking-worker fixed-position flexible assembly line is a shop-floor where products are placed at work centres, the workers move from one work centre to another, and tools and components are brought to the work centre for assembly according to the process and production plan. Such assembly shop-floor configuration is not only suitable for producing large, bulky, heavy or fragile products, but also offers necessary flexibility and competitive operational efficiency for products of modest variety and production volumes. However, the shop-floor with a fixed-position assembly islands typically suffers from limited spaces at work centres and high dynamics of material and manpower flows in addition to common shop-floor problems. This paper presents an affordable solution to these problems by using wireless manufacturing (WM)–an emerging advanced manufacturing technology (AMT). WM relies substantially on wireless devices such as radio frequency identification(RFID) auto ID sensors and wireless information networks for the collection and synchronization of the real-time field data from manufacturing workshops. A simplified example is used to illustrate how to deploy WM technology for implementing the concept of Just-In-Time (JIT) manufacturing to reduce the shop-floor work-in-progress (WIP) inventories and smoothening their flows through real-time information visibility and traceability.     

智能制造系统基于数据驱动的车间实时调度

智能制造系统采用大量先进的信息技术,为车间实时调度提供技术基础.各类信息技术在生产制造过程中的广泛应用使得制造系统积累了大量与生产调度相关的数据,因此,通过利用历史生产调度数据和智能装备收集到的实时生产数据,建立基于数据驱动的生产实时调度方法成为新型制造环境下实现高效调度的新思路.针对智能制造环境下的混合流水车间实时调度问题,提出基于BP神经网络的数据驱动的实时调度方法,从历史近优的调度方案中提取用于调度知识挖掘的样本数据,通过BP神经网络训练学习获取生产系统状态与调度规则的映射关系网络,并将其应用于生产在线实时调度.数值实验表明,所提出的方法优于固定单一调度规则,在不同的调度性能指标下其效果均稳定且良好.     

Development of an agent-based system for manufacturing control and coordination with ontology and RFID technology

Integrating physical objects with the corresponding enterprise applications any time any where is the essential issue for a real-time enterprise. This study proposes a multi-agent system framework called agent-based manufacturing control and coordination (AMCC) system, a agent-based framework using ontology and RFID technology to monitor and control dynamic production flows and also to improve the traceability and visibility of mass customization manufacturing processes. The capabilities offered by multi-agent systems to respond to RFID events in real-time and a broad class of agent design and coordination issue regarding just in time (JIT) and just in sequence (JIS) manufacturing processes are also exploited in this study. To validate the proposed framework, case study of a bicycle manufacturing company is used to demonstrate how the proposed framework can benefit its JIT production. Finally, an example prototype system is implemented to demonstrate the concept of the proposed framework.     

Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan

The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics.     

Hybrid decomposition heuristics for solving large-scale scheduling problems in semiconductor wafer fabrication

Most shop-floor scheduling policies used in practice rely on dispatching, making use of only local information at individual workcenters. However, in semiconductor manufacturing environments, we have access to real-time shop-floor status information for the entire facility. In these complex facilities, there would appear to be significant potential for improved schedules by considering global shop information and using optimization-based heuristics. To this end, we propose a rolling horizon (RH) heuristic that decomposes the shop into smaller subproblems that can be solved sequentially over time using a workcenter-based decomposition heuristic. We develop test instances for evaluating our heuristic using a simulation model of an industrial facility. The results demonstrate that the proposed heuristic yields better schedules than the dispatching rules in the vast majority of test instances with reasonable computational effort.     

Multi-agent based real-time production scheduling method for radio frequency identification enabled ubiquitous shopfloor environment

The lack of timely feedback shopfloor information during manufacturing execution stage leads to significant difficulties in achieving real-time production scheduling. To address this problem, an overall architecture of multi-agent based real-time production scheduling is presented to close the loop of production planning and control. Several contributions are significant. Firstly, wireless devices such as radio frequency identification (RFID) are deployed into value-adding points in a ubiquitous shopfloor environment to form Machine Agent for the collection and processing of real-time shopfloor data. Secondly, Capability Evaluation Agent is designed to optimally assign the tasks to the involved machines at the process planning stage based on the real-time utilization ration of each machine. The third contribution is a Real-time Scheduling Agent for manufacturing tasks scheduling/re-scheduling strategy and methods according to the real-time feedback. Fourthly, a Process Monitor Agent model is designed for tracking and tracing the manufacturing execution based on a critical event structure. Finally, a case is used to demonstrate the proposed multi-agent based real-time production scheduling models and methods.     

A methodology for implementation of mobile robot in adaptive manufacturing environments

With the rapid development of technologies, many production systems and modes has been advanced with respect to manufacturing, management and information fields. The paper deals with the problem of the implementation of an autonomous industrial mobile robot in real-world industrial applications in which all these fields are considered, namely mobile robot technology, planning and scheduling and communication. A methodology for implementation consisting of: a mobile robot system design (Little Helper prototype), an appropriate industrial application (multiple-part feeding), an implementation concept for the industrial application (the Bartender Concept), a mathematical model and a genetic algorithm-based heuristic is proposed. Furthermore, in order for the mobile robot to work properly in a flexible (cloud-based) manufacturing environment, the communications and exchange of data between the mobile robot with other manufacturing systems and shop-floor operators are addressed in the methodology. The proposed methodology provides insight into how mobile robot technology and abilities contribute to cloud manufacturing systems. A real-world demonstration at an impeller production line in a factory and computational experiments are conducted to demonstrate the effectiveness of the proposed methodology.     

The relevance of Computer Supported Cooperative Work for advanced manufacturing

Computer Integrated Manufacturing (CIM) is faced with issues which are crucial to Computer Supported Cooperative Work (CSCW). However, despite the large amount of work on Enterprise Integration and its obvious links to the CSCW field, this domain is almost totally absent in the work of the CSCW community. Therefore, this paper is intended to contribute to the discussion on the relevance of CSCW in manufacturing and to combine new concepts for cooperative work with requirements for information system design in production. Production related models, e.g., an order-driven mode of coordination, can be augmented with mechanisms of interaction for coordination, collaboration and co-decision and the design of customer/supplier chains. It is suggested, as one example, to use workflow management technology in advanced manufacturing for shop-floor production planning and control (PPC). The proposed enterprise model and support technology is especially useful for small-batch production.     

基于蚁群——遗传混合算法的高级计划与排产系统应用研究

生产管理系统是制造业中最为复杂的系统之一,高级计划与排产(Advanced Planning and Scheduling以下简称APS)是其重点与难点。由于生产的复杂性与不确定性,当前企业的生产计划编制方法虽多但效果不尽人意,特别是中小企业的现行生产计划与排产系统尤是如此。本文以江门某塑胶丝花行业为背景,以生产计划编制为研究对象,以计划编制的优化为主要内容,结合蚁群算法、遗传算法及其混合应用等算法理论与多种数学及智能信息技术方法,对建立一个能有效提高企业生产计划编制水平的APS系统应用进行了研究。     

A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing

Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.     

The connotation of digital twin,and the construction and application method of shop-floor digital twin

Digital twin (DT) technology provides a novel, feasible, and clear implementation path for the realization of smart manufacturing and cyber-physical systems (CPS). Currently, DT is applied to all stages of the product lifecycle, including design, production, and service, although its application in the production stage is not yet extensive. Shop-floor digital twin (SDT) is a digital mapping model of the corresponding physical shop-floor. How to build and apply SDT has always been challenging when applying DT technology in the production phase. To address the existing problems, this paper first reviews the origin and evolution of DT, including its application status in the production stage. Then, an implementation framework for the construction and application of SDT is proposed. Three key implementation techniques are explained in detail: the five-dimensional modeling of SDT; DT-based 3D visual and real-time monitoring of shop-floor operating status; and prediction of shop-floor operating status based on SDT using Markov chain. A DT-based visual monitoring and prediction system (DT-VMPS) for shop-floor operating status is developed, and the feasibility and effectiveness of the proposed method are demonstrated through the use of an engineering case study. Finally, a summary of the contributions of the paper is given, and future research issues are discussed.     

基于遗传算法的制造单元重组技术研究

根据生产任务动态重组制造单元是缩小生产设备调度范围，提高机床利用率的一个有效途径，针对制造单元重组问题，提出了基于生物遗传规律的制造单元重组算法（GCRA），该算法真实地反映了动态逻辑单元重组的本质要求，具有模型简单，设计变量少，计算速度快，稳定性强等优点，最后给出了实例验证。     

Multi-objective supervisory flow control based on fuzzy interval arithmetic: Application for scheduling of manufacturing systems

Complex production systems can produce more than one part type under multiple and possibly conflicting objectives. This paper considers the design of the multiple objective real-time scheduling problem of a multiple-part-type production system. Based on fuzzy control theory and fuzzy arithmetic intervals, distributed and supervised continuous-flow control architecture has been proposed. The objective is to balance the production process by adjusting the continuous production rates of the machines on the basis of the average behaviour. The supervisory control aims to maintain the overall performance within acceptable limits. In the proposed approach, the problem of a real-time scheduling of jobs is considered at the shop-floor level. In this case, the actual dispatching times are determined from the continuous production rates through a discretisation procedure. To deal with conflicts between jobs at a shared machine, a decision for the actual part to be processed is taken using some criterions which represent a measure of the job’s priority. A case study demonstrates the efficiency of the proposed control approach.     

Negotiation-based routing in job shops via collaborative agents

In recent years, the application of agent-based systems in manufacturing has received a lot of attention in several areas, particularly scheduling and shop-floor control. This paper explores two methods of enhancing the negotiation process employed by an agent-based system to support performance improvements in real-time routing of job orders within job-shop environments. The first method takes advantage of an extended negotiation period to provide a more complete picture of the shop's conditions in order to enhance the validity of the decisions made by individual agents. The second approach explores the possibility of process model data to increase the accuracy of time estimates used in the negotiation process. The resulting performance of the two methods is tested using a simulated job shop.     

A proactive material handling method for CPS enabled shop-floor

Cyber physical system (CPS) enables companies to keep high traceability and controllability in manufacturing for better quality and improved productivity. However, several challenges including excessively long waiting time and a serious waste of energy still exist on the shop-floor where limited buffer exists for each machine (e.g., shop-floor that manufactures large-size products). The production logistics tasks are released after work-in-processes (WIPs) are processed, and the machines will be occupied before trolleys arrival when using passive material handling strategy. To address this issue, a proactive material handling method for CPS enabled shop-floor (CPS-PMH) is proposed. Firstly, the manufacturing resources (machines and trolleys) are made smart by applying CPS technologies so that they are able to sense, act, interact and behave within a smart environment. Secondly, a shop-floor digital twin model is created, aiming to reflect their status just like real-life objects, and key production performance indicators can be analysed timely. Then, a time-weighted multiple linear regression method (TWMLR) is proposed to forecast the remaining processing time of WIPs. A proactive material handling model is designed to allocate smart trolleys optimally. Finally, a case study from Southern China is used to validate the proposed method and results show that the proposed CPS-PMH can largely reduce the total non-value-added energy consumption of manufacturing resources and optimize the routes of smart trolleys.     

Design of a suitable production management system for a manufacturing company

This paper reports on the design of a suitable production management system (PMS) for a manufacturing company, located in Western Australia. The company was experiencing problems in scheduling and plant layout leading to further problems in materials flow, labour control, inventory and purchasing, material handling system, and production space.

Group technology (GT) was used to design a new plant layout. A G.T. algorithm was developed to minimise machine duplications. A Kanban system was designed to assist execution of scheduling based on a pull system. The design was tested by simulation using actual data collected on existing operations.     

An integrated MRP and JIT framework

A new production planning and scheduling framework is developed to address the multi-stage, production-inventory system problem by integrating Material Requirements Planning (MRP) and Just-in-Time (JIT) production. The objective is to find detailed shop-floor schedules, which specify the quantity of an operation to be processed, at what time, and by which machine, so as to minimize total production cost. The integrated system gets rid of the major problems existing in MRP and JIT. First of all, the proposed integrated system incorporates both the scheduling and capacity planning aspects, simultaneously. Secondly, the integrated system eliminates the need to specify planned lead time. Thirdly, the integrated system, unlike MRP, provides detailed shop-floor schedules. Lastly, the integrated system does not have to operate in the level schedule case as in JIT, so it can handle a very general production environment.     

CPS-enabled and knowledge-aided demand response strategy for sustainable manufacturing

The utilization of advanced industrial informatics, such as industrial internet of things and cyber-physical system (CPS), provides enhanced situation awareness and resource controllability, which are essential for flexible real-time production scheduling and control (SC). Regardless of the belief that applying these advanced technologies under electricity demand response can help alleviate electricity demand–supply mismatches and eventually improve manufacturing sustainability, significant barriers have to be overcome first. Particularly, most existing real-time SC strategies remain limited to short-term scheduling and are unsuitable for finding the optimal schedule under demand response scheme, where a long-term production scheduling is often required to determine the energy consumption shift from peak to off-peak hours. Moreover, SC strategies ensuring the desired production throughput under dynamic electricity pricing and uncertainties in manufacturing environment are largely lacking. In this research, a knowledge-aided real-time demand response strategy for CPS-enabled manufacturing systems is proposed to address the above challenges. A knowledge-aided analytical model is first applied to generate a long-term production schedule to aid the real-time control under demand response. In addition, a real-time optimization model is developed to reduce electricity costs for CPS-enabled manufacturing systems under uncertainties. The effectiveness of the proposed strategy is validated through the case study on a steel powder manufacturing system. The results indicate the exceptional performance of the proposed strategy as compared to other real-time SC strategies, leading to a reduction of electricity cost up to 35.6% without sacrificing the production throughput.