Designing Holonic manufacturing systems

This paper discusses the design of Holonic manufacturing systems (HMS), with emphasis on manufacturing control. First, it discusses the concept of a Holonic system. Second, it presents the HMS reference architecture for manufacturing control. Third, it addresses the overall design problem, i.e. designing both the holonic control system and the underlying manufacturing system. Finally, the paper addresses the design and development of the control software itself.     

Development of holonic manufacturing execution systems

Rapid changes of market demands and pressures of competition require manufacturers to maintain highly flexible manufacturing systems to cope with a complex manufacturing environment. To meet these requirements, this work adopts the concepts of holon and holarchy to design manufacturing systems. Holon and holarchy are derived from the studies of social organizations and living organisms and possess the properties of intelligence, autonomy, cooperation, reconfigurability, and extensibility. Moreover, advanced manufacturing systems also require the properties of security certification and failure recovery. Based on the requirements of these properties, a systematic approach is proposed to develop a holonic manufacturing execution system (HMES) for the semiconductor industry. This systematic approach starts with a system analysis by collecting domain requirements and analyzing domain knowledge. The HMES Holarchy is designed by the procedure of constructing an abstract object model based on domain knowledge, partitioning application domain into functional holons, identifying generic parts among functional holons, developing the Generic Holon, defining holarchy messages and the holarchy framework of HMES, and finally designing functional holons based on the Generic Holon. It is believed that this proposed systematic approach provides a novel and efficient way to design HMES.     

A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems

Production planning is concerned with finding a release plan of jobs into a manufacturing system so that its actual outputs over time match the customer demand with the least cost. For a given release plan, the system outputs, work in process inventory (WIP) levels and job completions, are non-stationary bivariate time series that interact with time series representing customer demand, resulting in the fulfillment/non-fulfillment of demand and the holding cost of both WIP and finished-goods inventory. The relationship between a release plan and its resulting performance metrics (typically, mean/variance of the total cost and the fill rate) has proven difficult to quantify. This work develops a metamodel-based Monte Carlo simulation (MCS) method to accurately capture the dynamic, stochastic behavior of a manufacturing system, and to allow real-time evaluation of a release plan's performance metrics. This evaluation capability is then embedded in a multi-objective optimization framework to search for near-optimal release plans. The proposed method has been applied to a scaled-down semiconductor fabrication system to demonstrate the quality of the metamodel-based MCS evaluation and the results of plan optimization.     

Agent-based modeling and simulation of an autonomic manufacturing execution system

Production management systems must constantly deal with unplanned disruptive events and disturbances such as arrivals of rush orders, raw material shortage/delays or equipment breakdowns along with a multitude of interactions in the supply chain which constantly demand on-line task rescheduling and order execution control. For responsiveness and agility at the shop-floor, a distributed design for manufacturing execution systems is proposed based on autonomic units that fill the gap between production planning and shop-floor control. An interaction mechanism designed around the concept of order and resource agents implementing the monitor-analyze-plan-execution loop is described. Generative simulation modeling of an autonomic manufacturing execution system (@MES) is proposed in order to evaluate emerging behaviors and macroscopic dynamics in a multiproduct batch plant. Results obtained for an industrial case study using a simulation model of the proposed @MES are presented. The usefulness of agent-based modeling and simulation as a tool for distributed MESs design and to verify performance, stability and disturbance rejection capability of an interaction mechanism is highlighted.     

A simulation optimization approach in production planning of failure prone manufacturing systems

In this paper, the implementation of a new method to control the production rate of manufacturing systems, based on the combination of stochastic optimal control theory, discrete event simulation, experimental design and response surface methodology is outlined. The system under study consists of several parallel machines, multiple-product manufacturing system. Machines are subject to failures and repairs and their capacity process is assumed to be a finite state Markov chain throughout the analytical control model. The problem is to choose the production rates so as to minimize the expected discounted cost of inventory/backlog over an infinite horizon. We first show that, for constant demand rates and exponential failure and repair times distributions of the machines, the hedging point policy is optimal. The structure of the hedging point policy is then parameterized by factors representing the thresholds of involved products. With such a policy, simulation experiments are combined to experimental design and response surface methodology to estimate the optimal control policy. We obtain that the hedging point policy is also applicable to a wide variety of complex problems including non-exponential failure and repair times distributions and random demand rates. Analytical solutions may not be easily obtained for such complex situations.     

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.     

Holonic supply chain: A study from family-based manufacturing perspective

In the contemporary business environment, to adhere to the need of the customers, caused the shift from mass production to mass-customization. This necessitates the supply chain (SC) to be effective flexible. The purpose of this paper is to seek flexibility through adoption of family-based dispatching rules under the influence of inventory system implemented at downstream echelons of an industrial supply chain network. We compared the family-based dispatching rules in existing literature under the purview of inventory system and information sharing within a supply chain network. The dispatching rules are compared for Average Flow Time performance, which is averaged over the three product families. The performance is measured using extensive discrete event simulation process. Given the various inventory related operational factors at downstream echelons, the present paper highlights the importance of strategically adopting appropriate family-based dispatching rule at the manufacturing end. In the environment of mass customization, it becomes imperative to adopt the family-based dispatching rule from the system wide SC perspective. This warrants the application of intra as well as inter-echelon information coordination. The holonic paradigm emerges in this research stream, amidst the holistic approach and the vital systemic approach. The present research shows its novelty in triplet. Firstly, it provides leverage to manager to strategically adopting a dispatching rule from the inventory system perspective. Secondly, the findings provide direction for the attenuation of adverse impact accruing from demand amplification (bullwhip effect) in the form of inventory levels by appropriately adopting family-based dispatching rule. Thirdly, the information environment is conceptualized under the paradigm of Koestler’s holonic theory.     

An intelligent simulation environment for manufacturing systems

The manufacturing field is an area where the application of simulation is an essential tool for validating methods and architectures before applying them on the factory floor. Despite the fact that there are a great number of simulation tools, most of them do not take into account the specific requirements of the “new manufacturing era” such as distributed organization, interoperability, cooperation, scalability, fault tolerance and agility. On the other hand, Multiagent System technology has demonstrated its utility in manufacturing system modeling and implementation. Agenthood features such as proactivity, reactivity, and sociability may also be useful for associating them with the specific simulation needs of the new changing requirements for manufacturing systems. In this paper, an Agent-supported Simulation Environment for intelligent manufacturing systems is presented. The different roles that are played by the agents of the simulation environment are defined taking into account the specific dynamic features in manufacturing simulation and the requirements of the new manufacturing era. Moreover, the interaction and cooperation scenarios among these agents are specified to facilitate manufacturing simulation in an appropriate and flexible way. A detailed evaluation study, with regards to the new manufacturing era requirements, demonstrates the advantages of the proposed approach over current state-of-the-art proposals.     

A survey of flexible manufacturing systems

In this report, the different flexible manufacturing systems used for machining and assembling in the following countries are reviewed: the United States, Japan, the Federal Republic of Germany, the German Democratic Republic, Italy, Great Britain, Sweden, Norway, France, Czechoslovakia and Hungary.Three categories of systems discussed are (1) flexible modules and units, (2) flexible transfer lines, and (3) “unaligned” flexible systems. Within each category are several sub-groups, divided up mainly according to the conveyor system and operating mode. This classification shows the effective French position in this field.     

Cyber coordinated simulation for distributed multi-stage additive manufacturing systems

Additive Manufacturing (AM) processes have been increasingly used to manufacture energy storage products with dedicated material preparation and post-processing stages to enhance product properties. Most researchers focus on selecting materials and improving processes, yet the system modeling and management has not been investigated so far. This paper extends the conventional single-stage AM processes to multi-STage distRibutEd AM (STREAM) systems. In STREAM, a batch of material produced at the pre-processing stage is jointly consumed by distributed AM printers, and then the printed parts are collected for the post-processing stage. Modeling and managing such complex systems have been challenging. We propose a novel framework for “cyber-coordinated simulation” to manage the hierarchical information in STREAM. This is important because simulation can be used to infuse data into predictive analytics, thus providing guidance for the optimization and control of STREAM operations. The proposed framework is hierarchical in nature, where the single-stage, multi-stage, and distributed productions are modeled through the integration of different simulators. We demonstrate the proposed framework with simulation data from Freeze Nano Printing (FNP) AM for the fabrication of energy storage products.     

Adaptive smart card-based pull control systems in context-aware manufacturing systems: Training a neural network through multi-objective simulation optimization

Nowadays, context aware manufacturing systems offer interesting capabilities to improve the performance of pull controlled production systems. Smart Kanbans can be used instead of physical cards, and the information become available about the production context, collected for example through sensors and RFID. Such information can be exploited by intelligent pull control strategies so as to dynamically adapt the number of cards. This is particularly useful for production systems that are subjected to unpredictable changes in the customers’ demand, and need to react quickly to preserve a high level of performance. For this reason, we aim, in this article, at proposing an intelligent system, which can communicate with the information system, whose purpose is to autonomously decide or to help managers in adding or removing cards. In this respect, we propose an approach that uses a neural network which is trained offline, directly from simulation, to decide when it is relevant to change the number of cards, and at what production stage. The learning process, based on multi-objective simulation optimization, aims at reducing the production costs as well as the number of changes to avoid nervousness. The use of stochastic simulation, allows various types of complex problems, related to manufacturing systems, to be addressed and fluctuating demand phenomena to be taken into account. The relevance of our approach is illustrated using six published adaptive ConWIP and Kanban systems. Comparisons with adaptive Kanban and ConWIP systems show that the neural network can automatically learn very relevant knowledge. Good results are obtained in terms of performance, with fewer changes in the number of cards. Several possible future research directions are pointed out.     

Integration of neurocomputing and system simulation for modeling continuous improvement systems in manufacturing

The use of a neural network embedded in a larger general-purpose simulation system (GPSS) simulation used to model continuous improvement systems (CIS) policies in a factory setting is described. The neural network is used to accelerate the identification of an effective CIS policy by providing a more realistic simulation framework. The interface between general simulation theory and neural network simulation is examined. Neural networks, when embedded in larger general-purpose simulations, are found to offer the potential for improving on the capabilities of those simulations, in particular manufacturing simulations for continuous improvement of production processes.     

Assessing ease of reconfiguration of conventional and Holonic manufacturing systems: Approach and case study

This paper focuses on comparing the ease of reconfiguration of Holonic and conventional manufacturing systems (HMS and CMS). By using the recently proposed guideline for assessing ease of reconfiguration [Covanich, W., McFarlane, D., Farid, A.M., 2008. Guidelines for Evaluating the Ease of Reconfiguration of Manufacturing Systems. Daejeon, South Korea. INDIN08], the paper evaluates the effort required to introduce a new machine into both conventional and Holonic manufacturing systems. Examining particularly the ease of reconfiguration, it is found that HMS with conventional hardware may have little advantage over CMS, while HMS with ideal hardware appears to have significant advantage over CMS.     

Dynamic Drum-Buffer-Rope approach for production planning and control in capacitated flow-shop manufacturing systems

Drum-Buffer-Rope-based production planning and control (PPC) approaches provide production managers with effective tools to manage production disruptions and improve operational performance. The corner stone of these approaches is the proper selection of time-buffers which are considered as exogenously defined constant. However, the majority of real-world manufacturing systems are characterized by the dynamic change of demand and by stochastic production times. This fact calls for a dynamic approach in supporting the decision making on time-buffer policies. To this end, we study a capacitated, single-product, three-operation, flow-shop manufacturing system. We propose a dynamic time-buffer control mechanism for short/medium-term PPC with adaptive response to demand changes and robustness to sudden disturbances in both internal and external shop environment. By integrating the control mechanism into the flow-shop system, we develop a system dynamics model to support the decision-making on time-buffer policies. Using the model, we study the effect of policies on shop performance by means of analysis of variance. Extensive numerical investigation reveals the insensitivity of time-buffer policies to key factors related to demand, demand due date and operational characteristics such as protective capacity and production times.     

A synergistic approach to manufacturing systems control using machine learning and simulation

This paper describes a synergistic approach that is applicable to a wide variety of system control problems. The approach utilizes a machine learning technique, goal-directed conceptual aggregation (GDCA), to facilitate dynamic decision-making. The application domain employed is Flexible Manufacturing System (FMS) scheduling and control. Simulation is used for the dual purpose of providing a realistic depiction of FMSs, and serves as an engine for demonstrating the viability of a synergistic system involving incremental learning. The paper briefly describes prior approaches to FMS scheduling and control, and machine learning. It outlines the GDCA approach, provides a generalized architecture for dynamic control problems, and describes the implementation of the system as applied to FMS scheduling and control. The paper concludes with a discussion of the general applicability of this approach.     

Systematic analysis of centralized online reputation systems

Centralized online reputation systems have been widely adopted by Internet companies to help users build trust, reduce information asymmetry and filter information. Research in this area to date has focused on analyzing the effectiveness of single-type systems, while less attention has been paid to the comparison of different systems. This paper proposes an analysis model that can classify and measure different reputation systems in the same context. The model divides reputation systems into five underlying components: input, processing, output, feedback loop and storage. A series of benchmark criteria is then defined based on the characteristics of each component. The model comprehensively analyzes most characteristics of centralized reputation systems and it takes both performance and costs of systems into consideration.     

Using scheduling in flexible manufacturing systems

In this paper the concept of recursive Allen temporal algebra is fully described, along with its relationship to the smart scheduler program and the concept of smart scheduling in general. Flexible manufacturing systems (FMS) are described, and their relationship with smart scheduling is explored. A case study that shows the utility of SSP in an FMS environment is presented in detail. Conclusions about the concept of smart scheduling are presented in closing.     

Fuzzy discrete event simulation: A new tool for rapid analysis of production systems under vague information

Information availability is often a critical point for the application of classical quantitative techniques for production system analysis. In several production problems, indeed, managers have considered the possibility to incorporate expert estimates of production data into a model as a substitute for hard data as very interesting. Furthermore, the use of qualitative representations to model physical systems is attractive because it allows to capture the inherent ambiguity characterizing real systems. Fuzzy set theory allows to gain such attractive options, since it provides tools to process vague information.This paper concerns the new and interesting topic of fuzzy discrete event simulation. In particular, the problem of processing fuzzy data within a discrete event simulation process is discussed and new methods, able to limit time paradox problems, are proposed. Furthermore, the paper addresses the comparison among fuzzy and classical simulation, pointing out peculiarities, potentialities and application fields of such a new tool; finally, the research highlights the necessity to develop proper and specific fuzzy simulation packages by demonstrating that, even under specific and simplifying hypotheses about process uncertainties, fuzzy simulation analysis results cannot be obtained through classical simulation packages.     

ManufAg: a multi-agent-system framework for production control of complex manufacturing systems

In this paper, we present a framework for the implementation of multi-agent-systems for production control of complex manufacturing systems. We present the results of a requirement analysis for production control systems for complex manufacturing systems; then we describe the framework design criteria. Our framework supports the inclusion of distributed hierarchical decision-making schemes into the production control. Furthermore, in order to increase the coordination abilities of multi-agent-systems, we follow the decision-making and staff agent architecture suggested in the PROSA reference architecture. We indicate the usage of the framework for designing and implementing an agent-based production control system for semiconductor manufacturing processes in a case study.     

Production planning and worker training in dynamic manufacturing systems

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.