RFID-enabled real-time manufacturing information tracking infrastructure for extended enterprises

In extended enterprises, real-time manufacturing information tracking plays an important role and aims to provide the right information to the right person at the right time in the right format to achieve optimal production management among the involved enterprises. However, many enterprises are caused by lack of timely, accurate and consistent manufacturing data. The laggard information transfer flow and the unmatched information transfer method bring extended enterprises much more uncertainty and unknowingness. This paper proposes a RFID-enabled real-time manufacturing information tracking infrastructure (RTMITI) to address the real-time manufacturing data capturing and manufacturing information processing methods for extended enterprises. Following the proposed infrastructure, the traditional manufacturing resources such as employees, machines and materials are equipped with RFID devices (Readers and Tags) to build the real-time data capturing environment. In addition, a series of manufacturing information processing methods are established to calculate and track the real-time manufacturing information such as real-time manufacturing cost, progress, WIP (Work-in-progress) inventory etc. in parts/assemblies/products at machines/shop floors/enterprises/ extended enterprises levels. Finally, a case study is given to demonstrate the developed framework and corresponding methodologies.     

Robust manufacturing system design using multi objective genetic algorithms,Petri nets and Bayesian uncertainty representation

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.     

A distributed time-driven simulation method for enabling real-time manufacturing shop floor control

This paper proposes a distributed simulation approach for scheduling discrete-events in manufacturing shop floors. The proposed approach employs a time-driven method to simulate occurrence of discrete-events using distributed entities that replicate physical entities in the manufacturing shop floor. In specific, the proposed approach iteratively controls the timing of discrete-events occurrence using a control theoretic model. In this approach, changing the speed of the simulation clock, termed time-scaling factor, can accelerate or decelerate the simulation speed resulting in simpler synchronizations of discrete-events and faster simulation than standard distributed discrete-event simulations according to the capability of the communication networks. Computational experiments are conducted to test the performance of the proposed system with different values of the time-scaling factor, and the relationship between the system performance and the time-scaling factor is investigated through analysis of the system model. Results obtained from the computational experiments show significant successes in speeding up discrete-event simulations in such a way that the proposed approach can be used for the control of manufacturing shop floors, providing real-time decision supports.     

Flow shops with WIP and value added costs

A new deterministic flow shop problem is studied where the objective is to minimize the total WIP (work-in-process) cost. Based on a value added model, the unit time WIP cost increases as a job passes through various stages in the production process. The recognition version is unary NP-Complete even for two machines. Several simple and intuitive heuristics are presented. For each heuristic, we determine asymptotically attainable upper bounds on the relative error. Finally, the heuristics are empirically evaluated.     

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.     

Multi-attribute negotiation mechanism for manufacturing service allocation in smart manufacturing

Smart manufacturing is undergoing rapid development along with many disruptive technologies, such as Internet of Things, cyber-physical system and cloud computing. A myriad of heterogeneous manufacturing services can be dynamically perceived, connected and interoperated to satisfy various customized demands. In smart manufacturing, the market equilibrium is variable over time due to changes in demand and supply. Thus, efficient manufacturing service allocation (MSA) is critical to implementation of smart manufacturing. This paper considers the MSA problem under market dynamics with maximization of utility of customers and service providers. Many conventional methods generally allocate manufacturing services to the customers by multi-objective optimization without considering the impact of interactions between customers and service providers. This paper presents a multi-attribute negotiation mechanism to address the MSA problem under time constraints relying on autonomous agents. The proposed negotiation mechanism is composed of two models: an atomic manufacturing service negotiation model and a composite manufacturing service coordination. The former model is based on automated negotiation to seek an atomic manufacturing service over multiple attributes for an individual subtask. The latter model incorporates the global distribution and surplus redistribution to coordinate and control multiple atomic manufacturing service negotiations for the whole manufacturing task. Numerical studies are employed to verify the effectiveness of the multi-attribute negotiation mechanism in solving the MSA problem. The results show that the proposed negotiation mechanism can address the MSA problem and surplus redistribution can effectively improve the success rate of negotiations.     

Time-synchronizing control of self-organizing shop floors for networked manufacturing

In networked manufacturing systems, shop floors that are geographically dispersed can coordinate autonomously to complete the fabrication and assembly of products. It is a type of self-organizing production process, in which the scheduling of those shop floors must be synchronized, in terms of time and quantity. In this paper, we propose a time-synchronizing control policy for self-organizing shop floors based on the (max, +) system theory, prove the convergence of the synchronization algorithm, and verify the effectiveness of the algorithm by numerical experiments. Besides, a method of implementing the synchronizing control system based on the radio frequency identification (RFID) technologies is also proposed briefly.     

An IIoT-driven and AI-enabled framework for smart manufacturing system based on three-terminal collaborative platform

Smart manufacturing has great potential in the development of network collaboration, mass personalised customisation, sustainability and flexibility. Customised production can better meet the dynamic user needs, and network collaboration can significantly improve production efficiency. Industrial internet of things (IIoT) and artificial intelligence (AI) have penetrated the manufacturing environment, improving production efficiency and facilitating customised and collaborative production. However, these technologies are isolated and dispersed in the applications of machine design and manufacturing processes. It is a challenge to integrate AI and IIoT technologies based on the platform, to develop autonomous connect manufacturing machines (ACMMs), matching with smart manufacturing and to facilitate the smart manufacturing services (SMSs) from the overall product life cycle. This paper firstly proposes a three-terminal collaborative platform (TTCP) consisting of cloud servers, embedded controllers and mobile terminals to integrate AI and IIoT technologies for the ACMM design. Then, based on the ACMMs, a framework for SMS to generate more IIoT-driven and AI-enabled services is presented. Finally, as an illustrative case, a more autonomous engraving machine and a smart manufacturing scenario are designed through the above-mentioned method. This case implements basic engraving functions along with AI-enabled automatic detection of broken tool service for collaborative production, remote human-machine interface service for customised production and network collaboration, and energy consumption analysis service for production optimisation. The systematic method proposed can provide some inspirations for the manufacturing industry to generate SMSs and facilitate the optimisation production and customised and collaborative production.     

Service perspective based production control system for smart job shop under industry 4.0

Industry 4.0 describes a smart job shop as follows: it can meet individual customer requirements even if the requirements are changed at the last minute; its production control system (PCS) can rapidly respond to unexpected disruptions in production, and smart workpieces in the smart job shop can communicate with workstations to tell them what to do next. Present PCSs issue production instruction (PI) to workstation in a relatively long period such as a day, a week, even a month. And the PI is usually at process level, which means it is not sufficient to maintain smooth production flow at the operational level. Therefore, the existing PCSs cannot meet the requirements of Industry 4.0. On account of this, this article proposes a smart workpiece enabled production instruction service system for smart job shop under Industry 4.0. The PI service system in smart job shop consists of three parts such as PI sets generation, PI sets execution and PI sets update. In PI sets generation, the PI is viewed as a service requirement from the smart workpiece for the workstation, and then a PI service model is established to integrate machining actions with different kinds of manufacturing resources, processing place and processing time. Based on that, a method of converting the Gantt chart to PI sets is presented. In PI sets execution, a PI service unit is proposed for real-time issuing PIs to the radio-frequency identification (RFID) tags of smart workpieces. In PI sets update, the update of PI sets including unexecuted processes PI sets and current processes PI sets is discussed in detail. Finally, a small-scale smart job shop is taken as an example to illustrate the feasibility of the PI service system.     

差分进化算法求解带批处理机的机器人制造单元调度问题

机器人制造单元是智能制造系统的主要载体,研究机器人制造单元的生产调度问题对于提高智能制造系统的生产效率有着重要作用.对此,研究带批处理机的混合流水线机器人制造单元调度问题.首先,针对机器人制造单元与批处理机的生产特性,建立数学优化模型;其次,设计差分进化算法对其进行求解,提出染色体组编码的概念,求解该问题的染色体组由两个染色体构成,第1条染色体确定工件在每个工序选择的机器,第2条染色体确定加工顺序以及机器人的搬运顺序;然后,设计差分变异、交叉以及选择操作;最后,进行数值实验,结果证明,针对带批处理机的机器人制造单元调度问题,差分进化算法能缩短完工时间,得到更好的解.     

A simulated annealing algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times

One of the scheduling problems with various applications in industries is hybrid flow shop. In hybrid flow shop, a series of n jobs are processed at a series of g workshops with several parallel machines in each workshop. To simplify the model construction in most research on hybrid flow shop scheduling problems, the setup times of operations have been ignored, combined with their corresponding processing times, or considered non sequence-dependent. However, in most real industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacturing, hybrid flow shop problems have sequence-dependent setup times (SDST). In this research, the problem of SDST hybrid flow shop scheduling with parallel identical machines to minimize the makespan is studied. A novel simulated annealing (NSA) algorithm is developed to produce a reasonable manufacturing schedule within an acceptable computational time. In this study, the proposed NSA uses a well combination of two moving operators for generating new solutions. The obtained results are compared with those computed by Random Key Genetic Algorithm (RKGA) and Immune Algorithm (IA) which are proposed previously. The results show that NSA outperforms both RKGA and IA.     

一种柔性路径下的跨单元调度方法

针对单元制造系统(Cellular manufacturing system, CMS)中需要多个单元协作完成的特殊工件, 提出柔性路径下跨作业(Job shop)单元的特殊工件调度方法---基于信息素的方法(Pheromone-based approach, PBA).基于多Agent对单元制造系统建立模型, 提出了冗余单元的概念,建立了多Agent之间的协商机制.同时通过建立Agent联盟, 减少通信量的同时增强系统的鲁棒性和调度优化的全局性.实验结果表明,与常见的组合调度规则相比, 本文提出的方法在5种性能指标上具有显著优势.     

考虑工件移动时间的柔性作业车间调度问题研究

分析生产车间的实际生产状况,建立了考虑工件移动时间的柔性作业车间调度问题模型,该模型考虑了以往柔性作业车间调度问题模型所没有考虑的工件在加工机器间的移动时间,使柔性作业车间调度问题更贴近实际生产,让调度理论更具现实性。通过对已有的改进遗传算法的遗传操作进行重构,设计出有效求解考虑工件移动时间的柔性作业车间调度问题的改进遗传算法。最后对实际案例进行求解,得到调度甘特图和析取图,通过对甘特图和析取图的分析验证了所建考虑工件移动时间的柔性作业车间调度问题模型的可行性和有效性。     

Solving facilities location problem in the presence of alternative processing routes using a genetic algorithm

Facilities location problem deals with the optimization of location of manufacturing facilities like machines, departments, etc. in the shop floor. This problem greatly affects performance of a manufacturing system. It is assumed in this paper that there are multiple products to be produced on several machines. Alternative processing routes are considered for each product and the problem is to determine the processing route of each product and the location of each machine to minimize the total distance traveled by the materials within the shop floor. This paper presents a mixed-integer non-linear mathematical programming formulation to find optimal solution of this problem. A technique is used to linearize the formulated non-linear model. However, due to the NP-hardness of this problem, even the linearized model cannot be optimally solved by the conventional mathematical programming methods in a reasonable time. Therefore, a genetic algorithm is proposed to solve the linearized model. The effectiveness of the GA approach is evaluated with numerical examples. The results show that the proposed GA is both effective and efficient in solving the attempted problem.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

考虑机器速度的低碳柔性作业车间调度问题研究

根据具有低碳需求的制造企业的实际情况,建立了考虑机器速度的低碳柔性作业车间调度问题模型,该模型考虑机器加工速度,增加了工件的装夹和卸载时间及机器在不同状态下的碳排放参数,使问题更具现实性。为了实现低碳生产的目标,本文在实现最大完工时间最小的前提下提出了一种非关键工序调整法,通过对非关键工序的调整,降低机器的总碳排放量,提高机器利用率。最后通过求解实际案例,实验结果证实模型的可行性和非关键工序调整法的有效性。     

Joint optimization of job scheduling and maintenance planning for a two-machine flow shop considering job-dependent operating condition

Two-machine flow shops are widely adopted in manufacturing systems. To minimize the makespan of a sequence of jobs, joint optimization of job scheduling and preventive maintenance (PM) planning has been extensively studied for such systems. In practice, the operating condition (OC) of the two machines usually varies from one job to another because of different processing covariates, which directly affects the machines’ failure rates, PM plans, and expected job completion times. This fact is common in many real systems, but it is often overlooked in the related literature. In this study, we propose a joint decision-making strategy for a two-machine flow shop with resumable jobs. The objective is to minimize the expected makespan by taking into account job-dependent OC. We consider two situations. In the first situation, where the failure rate of a machine under a fixed OC is constant, a hybrid processing time model is proposed to obtain the optimal job sequence based on the Johnson's law. For the second situation, where the failure rate of a machine is time-varying, the job sequence and PM plan are jointly optimized. An enumeration method is adopted to find the optimal job sequence and PM plan for a small-scale problem, and a genetic algorithm-based method is proposed to solve a large-scale problem. Numerical examples are provided to demonstrate the necessity of considering the effect of job-dependent OC and the effectiveness of the proposed method in handing such joint decision-making problems in manufacturing systems.     

基于数字化人的生产现场信息化研究

针对目前车间生产现场数字化制造所存在的信息化盲区问题,利用移动通讯技术的优势,提出了数字化人的概念,给出了理论模型,探讨了数字化人-人和人-机的协同关系及数字化人实现的关键技术,最后建立了基于数字化人的车间生产现场的信息化系统结构,给出了运行实例。     

Augmented Lagrangian coordination for energy-optimal allocation of smart manufacturing services

The rapid development of information and communication technologies has triggered the proposition and implementation of smart manufacturing paradigms. In this regard, efficient allocation of smart manufacturing services (SMSs) can provide a sustainable manner for promoting cleaner production. Currently, centralized optimization methods have been widely used to complete the optimal allocation of SMSs. However, personalized manufacturing tasks usually belong to diverse production domains. The centralized optimization methods could hardly include related production knowledge of all manufacturing tasks in an individual decision model. Consequently, it is difficult to provide satisfactory SMSs for meeting customer's requirements. In addition, energy consumption is rarely considered in the SMS allocation process which is unfavorable for performing sustainable manufacturing. To address these challenges, augmented Lagrangian coordination (ALC), a novel distributed optimization method is proposed to deal with the energy-optimal SMS allocation problem in this paper. The energy-optimal SMS allocation model is constructed and decomposed into several loose-coupled and distributed elements. Two variants of the ALC method are implemented to formulate the proposed problem and obtain final SMS allocation results. A case study is employed to verify the superiority of the proposed method in dealing with energy-optimal SMS allocation problems by comparing with the centralized optimization method at last.     

Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: A case study based on the apparel industry

An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products. In this paper, a universal mathematical model of the JSS problem for apparel assembly process is constructed. The objective of this model is to minimize the total penalties of earliness and tardiness by deciding when to start each order’s production and how to assign the operations to machines (operators). A genetic optimization process is then presented to solve this model, in which a new chromosome representation, a heuristic initialization process and modified crossover and mutation operators are proposed. Three experiments using industrial data are illustrated to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the JSS problem in a mixed- and multi-product assembly environment.