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.     

Equipment replacement decisions and lean manufacturing

Traditional manufacturing systems are built on the principle of economies of scale. Here, the large fixed costs of production are depreciation-intensive because of huge capital investments made in high-volume operations. These fixed costs are spread over large production batch sizes in an effort to minimize the total unit costs of owning and operating the manufacturing system. As an alternative to “batch-and-queue,” high-volume, and inflexible operations, the principles of the Toyota Production System (TPS) and lean manufacturing have been widely adopted in recent years in the US [1, 2, 3 and 4]. In this paper, we illustrate an equipment replacement decision problem within the context of lean manufacturing implementation. In particular, we demonstrate how the value stream mapping (VSM) suite of tools can be used to map the current state of a production line and design a desired future state. Further, we provide a roadmap for how VSM can provide necessary information for analysis of equipment replacement decision problems encountered in lean manufacturing implementation.     

Development and evaluation of a Basestock-CONWIP pull production control strategy in balanced assembly systems

In this study, a new pull production control strategy called Basestock-Constant Work-in-Process (B-CONWIP) is proposed. It is used to control the flow of materials and information in balanced assembly production systems. This proposed control strategy uses one type of authorization cards called CONWIP card that limits the work-in-process (WIP) in the whole system. It has been applied in a single-product and a mixed-product assembly system balanced by two efficient Genetic algorithms introduced in literature. The performance of this control strategy is compared with another pull production control strategy called Basestock Kanban CONWIP (BK-CONWIP), which is a very promising production control strategy found in literature. The proposed strategy has two control parameters, CONWIP authorization cards and basestock levels while BK-CONWIP has three control parameters Kanban authorization cards, CONWIP authorization cards and basestock levels. The comparison is based on three performance measures average system WIP, percentage of satisfied customer demand (service level) and WIP variation between workstations. The performance of the proposed strategy B-CONWIP and BK-CONWIP is mainly similar in both types of assembly systems when mean demand rates are low with respect to mean service rates with the proposed strategy being easier to control and optimize. On the other hand, when mean demand rates are high with respect to mean service rates; B-CONWIP is preferable if service level is more important, while BK-CONWIP is preferable if WIP level is more important. Regarding WIP variation, it mainly depends on the efficiency of the balancing approach. The more efficient the balancing approach, the less WIP variation. Treating demand as lost instead of backordered results in decreased average system WIP and does not affect service levels in both PCSs. It is also shown that S-KDP is more flexible in dealing with situations of variable product mixes than d-KDP because control parameters can be used by any product which minimizes the effect of the unbalanced systems.     

A multi-agent based agile manufacturing planning and control system

In today’s manufacturing enterprise, the performance of customer service level (e.g., short ordering-to-delivery time, low price) is highly dependent on the effectiveness of its manufacturing planning and control system (MPCS). However, most of the current MPCS, employed the hierarchical planning approach, may have some drawbacks, such as structural rigidity, difficulty of designing a control system, and lack of flexibility. Currently, RFID (Radio Frequency Identification) technology has been applied to enhance the visibility, accountability, track ability and traceability of manufacturing system whenever the accurate and detailed manufacturing information (e.g., raw material, WIP, products in factory and products in the down streams) of products will be followed in real-time basis by RFID technique. In addition, a multi-agent approach may be applied in a distributed and autonomous system which allows negotiation-based decision making. Therefore, the objective of this research is to study the application of RFID technique and multi-agent system (MAS) in developing an agent-based agile manufacturing planning and control system (AMPCS) to respond to the dynamically changing manufacturing activities and exceptions.In AMPCS, RFID-based manufacturing control (R-MC) module plays the role of controlling the manufacturing system in which production items and manufacturing resources attached with RFID tag may actively feedback production status to and receive production and operations schedule from advanced manufacturing planning (AMP) module. In addition, a bidding process and algorithm is developed to generate operations schedule by using the characteristics of MAS. Performance analysis (PA) module is responsible not only for evaluating the scheduling results but also for evaluating the performance of production execution. The development of an AMPCS for an automated manufacturing cell demonstrates that the integration of RFID technique and MAS in developing an agile manufacturing planning and control system can really possess the characteristics of visibility, accountability, track ability, responsiveness, and flexibility in a distributed and dynamic manufacturing system.     

CONWIP based control of a lamp assembly production line

Efficient and effective production control systems are very important for manufacturing plants. CONWIP, one of these production control systems, has a high potential of becoming the best one available because it suits a variety of production environments and is easy to implement. In the following paper, we compare the single-loop and multi-loop CONWIP production control systems for an actual lamp assembly production line producing different kinds of products with discrete distribution processing time and demand. A model is formulated with respect to total cost and service level. A novel rule-based genetic algorithm (GA) approach is proposed for the multi-loop CONWIP system to find the optimum parameter setting. The results have shown that the single-loop CONWIP production control system is more efficient than the multi-loop system. It can greatly decrease the total cost and the WIP (Work-In-Process) with zero shortage probability.     

Hidden Markov model-based autonomous manufacturing task orchestration in smart shop floors

Smart manufacturing requires flexible production organization and management to handle the dynamic customer requirements rapidly and efficiently. In the context of smart manufacturing, work-in-progress (WIP), machines, and other physical resources in smart shop floors are endowed with intelligence, such as self-perception and self-decision-making. In this situation, the manufacturing task orchestration in such smart shop floors becomes autonomous, which is different from the traditional one that is centrally set and managed. The manufacturing tasks are accomplished with the help of autonomous communication between the WIP and the machines. This paper firstly clarifies the logic of autonomous manufacturing, in which the core idea is the autonomous communication and collaboration between the WIP and the machines during production. Furthermore, the autonomous manufacturing task orchestration (AMTO) problem is described. An improved hidden Markov model (HMM) is proposed to formulate the problem and generate an optimal AMTO solution for a certain process flow. A demonstrative case is implemented to verify the feasibility of the proposed model and method. The results show that HMM can give suggestions on AMTO and dynamically adjust the situation based on the real-time manufacturing data.     

Stability of real-time lot scheduling policies for an unreliablemachine

A single-machine multiproduct manufacturing system with random breakdowns and random repair times is considered. Under a weak capacity condition on the system it is shown that the total work-in-progress (WIP) is a recurrent stochastic process. By replacing the stochastic model by a deterministic one with preventive maintenance activities scheduled on a regular basis or by strengthening the capacity condition, the boundaries of WIP and expected WIP, respectively, over time can be shown     

On the workload and ‘phaseload’ allocation problems of short reliable production lines with finite buffers

Serial flow or production lines are modeled as tandem queueing networks and formulated as continuous-time Markov chains to investigate how to maximize throughput or minimize the average work-in-process (WIP) when the total service time and the total number of service phases among the stations are fixed (these are the workload and ‘phaseload’ allocation problems, respectively). This paper examines both the effect of the kind of service time distribution on the optimal workload allocation in order to maximize throughput or minimize the average WIP of perfectly reliable production lines.

The new approach of this work is the differentiation of the number of service phases of the service time distribution which is assumed to be of phase type at all stations of the flow line and the placement of storage space (buffers) between any two successive stations in order to examine the effect of these factors to the form of the optimal workload vector t and the optimal service phase vector ph.     

ErgoVSM: A Tool for Integrating Value Stream Mapping and Ergonomics in Manufacturing

Value stream mapping (VSM) is a lean tool aiming at waste reduction. Previous research suggests that the use of VSM may result in work intensification and thus an increased risk for the workers of developing work‐related musculoskeletal disorders (MSD). In the current study, VSM was developed to also consider physical exposure in the analyzed production system (ErgoVSM). As the VSM, ErgoVSM is based on a participatory approach. ErgoVSM was tested in a Swedish manufacturing company. The results suggest that ErgoVSM catalyzes change processes to include intervention proposals emphasizing ergonomics in addition to waste reduction. Thus, ErgoVSM appeared useful for the investigated target group of production engineers and experienced operators. The performance improvements suggested when using the ordinary VSM seemed not to be hampered by adding the ergonomics complement. However, the use of ErgoVSM was somewhat more time consuming than the use of VSM. In conclusion, ErgoVSM is suggested as a feasible tool to be used by production engineers and experienced operators for including ergonomics considerations in the rationalization process.     

Synchronized production and logistics via ubiquitous computing technology

The integration of manufacturing and logistics has drawn widespread research attentions in recent years. This paper focuses on the Synchronized Production and Logistics (SPL), which is operational level integration. SPL is defined as synchronizing the processing, moving and storing of raw material, WIP and finished product within one manufacturing unit by high level information sharing and joint scheduling to achieve synergic decision, execution and overall performance improvement. Through analysing the requirements and challenges in real life industry, the ubiquitous computing is adopted as an enabling technology and an Ubi-SPL (Synchronized Production and Logistics via Ubiquitous Technology) framework is proposed. This framework is consists of four layers, which creates a close decision-execution loop by linking the frontline real time data, user feedback and optimized decision together. A real life case study of applying Ubi-SPL solution in a chemical industry has been conducted. The implementation results show that the proposed Ubi-SPL solution can significantly improve the overall performance in both production and logistics service.     

Agile MPC system linking manufacturing and market strategies

Increasing complexity and interdependency in manufacturing enterprises require an agile manufacturing paradigm. This paper considers a dynamic control approach for linking manufacturing strategy with market strategy through a reconfigurable manufacturing planning and control (MPC) system to support agility in this context. A comprehensive MPC model capable of adopting different MPC strategies through distributed controllers of inventory, capacity, and WIP is presented. A hierarchical supervisory controller (referred to as decision logic unit, DLU) that intakes the high-level strategic market decisions and constraints together with feedback of the current manufacturing system state (WIP, production, and inventory levels) and optimally manages the distributed controllers is introduced. The DLU architecture with its three layers and their different functionalities is discussed showing how they link the higher management level to the operational level to satisfy the required demand. A case study for an automatic PCB assembly factory is implemented to demonstrate the applicability of the whole approach. In addition, a comparative cost analysis study is carried out to compare between the developed agile MPC system and classical-inventory- and capacity-based MPC policies in response to different demand patterns. Results showed that the developed agile MPC policy is as cost effective as the inventory-based MPC policy in demand patterns with steady trends, as cost effective as capacity-based MPC in turbulent demand patterns, and far superior than both classical MPC polices in mixed-demand patterns.     

Supply chain management with lean production and RFID application: A case study

This study applies lean production and radio frequency identification (RFID) technologies to improve the efficiency and effectiveness of supply chain management. In this study, a three-tier spare parts supply chain with inefficient transportation, storage and retrieval operations is investigated. Value Stream Mapping (VSM) is used to draw current state mapping and future state mapping (with lean production and RFID) with material, information, and time flows. Preliminary experiments showed that the total operation time can be saved by 81% from current stage to future stage with the integration of RFID and lean. Moreover, the saving in total operation time can be enhanced to 89% with cross docking. In addition, utilizing RFID technology, the cost of labors can be significantly reduced while maintaining current service capacity at the members in the studied supply chain. Return-on-investment (ROI) analysis shows that the proposed method is both effective and feasible.     

A machine tool matching method in cloud manufacturing using Markov Decision Process and cross-entropy

Cloud Manufacturing (CMfg) is a state-of-the-art manufacturing paradigm implementing the concept of service-oriented manufacturing. Machine tools are one kind of the critical manufacturing resources in Cloud Manufacturing, however machine tool matching is still immature owning to customization manufacturing service demands from users and various disturbing factors in production. This paper proposes a machine tool matching method for dealing with a single Cloud Manufacturing task with complex machine tool application demands. In this method, the demands of machine tools and themselves are described and evaluated based on a universal framework to obtain candidate resource groups satisfying local requirements of sub-demands. Then, a series of Markov Decision Processes (MDP) is established, which take the minimal service cost as optimal object to meet global requirements, and a cross-entropy based algorithm is used to solve the optimal object. Finally, simulation experiments are conducted to validate the usability and superiority in efficiency of the proposed method.     

Evaluation of multi-product lean manufacturing systems with setup and erratic demand

The consideration of demand variability in Multi-Product Lean Manufacturing Environment (MPLME) is an innovation in production system engineering. Manufacturing systems that fail to recognise demand variability generate high Work-In-Process (WIP) and low throughput in MPLME. In response to demand variability, organisations allocate large quantities of Production Authorisation Cards (PAC). A large proportion of PAC results in a high WIP level. However, the Shared Kanban Allocation Policy (S-KAP) allows the distribution of PAC among part-types, which minimises WIP in MPLME. Nevertheless, some existing lean manufacturing control strategies referred as Pull Production Control Strategies (PPCS) that have shown improved performance in single-product systems failed to operate S-KAP. The recently developed Basestock–Kanban-CONWIP (BK-CONWIP) strategy has the capability of minimising WIP while maintaining low backlog and volume flexibility. This paper investigates the effects of erratic demand on the performance of PPCS in MPLME. It is shown that S-KAP BK-CONWIP outperforms other PPCS. Finally, it is feasible to design quick-response PPCS for MPLME under erratic demand.     

Digital twin for cutting tool: Modeling,application and service strategy

Developments in the internet technology, internet of things, cloud computing, big data, and artificial intelligence in recent years have brought significant advancements in manufacturing, among which manufacturing digitalization contributes greatly to the ever-increasing productivity. A cutting tool driven by the digital twin can provide modern solution to these ever-growing needs of digitalization. In this paper, the digital twin driven-data flow framework at every state of the cutting tool life cycle is proposed in order to ensure the possibility for continuous improvement of process and tool. The detailed digital twin-driven services are discussed, and there are two service modes for manufacturers to meet customer needs. Furthermore, a virtual cutting tool test platform fused with physical tool wear data and virtue tool wear data, which provides a promising guidance for the development of intelligence manufacturing in future. The development prospects and challenges of data analysis, fusion, mining, and service are also discussed.     

Manufacturing intelligence to forecast and reduce semiconductor cycle time

Semiconductor manufacturing is one of the most complicated production processes with the challenges of dynamic job arrival, job re-circulation, shifting bottlenecks, and lengthy fabrication process. Owing to the lengthy wafer fabrication process, work in process (WIP) usually affects the cycle time and throughput in the semiconductor fabrication. As the applications of semiconductor have reached the era of consumer electronics, time to market has played an increasingly critical role in maintaining a competitive advantage for a semiconductor company. Many past studies have explored how to reduce the time of scheduling and dispatching in the production cycle. Focusing on real settings, this study aims to develop a manufacturing intelligence approach by integrating Gauss-Newton regression method and back-propagation neural network as basic model to forecast the cycle time of the production line, where WIP, capacity, utilization, average layers, and throughput are rendered as input factors for indentifying effective rules to control the levels of the corresponding factors as well as reduce the cycle time. Additionally, it develops an adaptive model for rapid response to change of production line status. To evaluate the validity of this approach, we conducted an empirical study on the demand change and production dynamics in a semiconductor foundry in Hsinchu Science Park. The approach proved to be successful in improving forecast accuracy and realigning the desired levels of throughput in production lines to reduce the cycle time.     

Practical simulation application: Evaluation of process control parameters in Twisted-Pair Cables manufacturing system

Intensive competition and rapid technology development of Twisted-Pair Cables (TPC) industry have left no room for competing manufacturers to harbour system inefficiencies. TPC are used in various communication and networks hardware applications; their manufacturing facilities face many challenges including various product configurations with different equipment settings, different product flows and Work in Process (WIP) space limitations. The quest for internal efficiency and external effectiveness forces companies to align their internal settings and resources with external requirements/orders, or in different words, significant factors must be set appropriately and identified prior to manufacturing processes. Integrated definition models (IDEF0, IDEF3) in conjunction with a simulation model and a design of experiments (DOE) have been developed to characterize the TPC production system, identify the significant process parameters and examine various production setting scenarios aiming to get the best product flow time.     

Fabulous MESs and C/Cs: an overview of semiconductor fab automation systems

This article presents an overview of the automation systems for a semiconductor fab. In particular, two key systems are presented: 1) a manufacturing execution systems (MES) that formulates manufacturing methods and procedures and 2) a cell controller (C/C) that serves as the automation link between the upper MES and the lower equipment within a production tunnel. At the end of the article, lot operation of one manufacturing step is detailed to highlight the interaction between MES and C/C in a fully automated semiconductor fab. The C/C, in contrast, are to respond to equipment requests for processing, to coordinate intrabay or interbay lot transportation, and to keep the MES the most updated WIP information within a production channel. This overview presents the most essential automation functions needed in general semiconductor fabs. Yet, in terms of future technology trends, reported work on specification, design methods, and implementation platforms has taken advantage of the latest development of computer technology in general, and object-oriented technology in particular.     

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.