Design and development of a hybrid flexible manufacturing system : a thesis presented in fulfilment of the requirements for the degree of Master of Technology at Massey University

Volumes 1 and 2 merged.The ability of a manufacturing environment to be able to modify itself and to incorporate a wide variety of heterogeneous multi-vendor devices is becoming a matter of increasing importance in the modern manufacturing enterprise. Many companies in the past have been forced to procure devices which are compatible with existing systems but are not as suitable as other less compatible devices. The inability to be able to integrate new devices into an existing company has made such enterprises dependent on one vendor and has decreased their ability to be able to respond to changes in the market. It is said that typically 60% of orders received in a company are new orders. Therefore the ability of a company to be able to reconfigure itself and respond to such demands and reintegrate itself with new equipment requirements is of paramount importance. In the past much effort has been made towards the integration of shop floor devices in industry whereby such devices can communicate with each other so that certain tasks are able to be achieved in a single environment. Up until recently however much of this was carried out in a very much improvised fashion with no real structure existing within the factory. This meant that once the factory was set up it became a hard-wired entity and extensibility and modiflability were difficult indeed. When formalised Computer Integrated Manufacturing (CIM) system architectures were developed it was found that although they solved many existing shortcomings there were inherent problems associated with these as well. What became apparent was that a fresh approach was required that took the advantages of existing architectures and combined them into an new architecture that not only capitalised on these advantages but also nullified the weaknesses of the existing systems. This thesis outlines the design of a new FMS architecture and its implementation in a factory environment on a PC based system

Réduction du comportement myope dans le contrôle des FMS : une approche semi-hétérarchique basée sur la simulation-optimisation

Heterarchical-based control for flexible manufacturing systems (FMS) localizes control capabilities in decisional entities (DE), resulting in highly reactive and low complex control architectures. However, these architectures present myopic behavior since DEs have limited visibility of other DEs and their behavior, making difficult to ensure certain global performance. This dissertation focuses on reducing myopic behavior. At first, a definition and a typology of myopic behavior in FMS is proposed. In this thesis, myopic behavior is dealt explicitly so global performance can be improved. Thus, we propose a semi-heterarchical architecture in which a global decisional entity (GDE) deals with different kinds of myopic decisions using simulation-based optimization (SbOs). Different optimization techniques can be used so myopic decisions can be dealt individually, favoring GDE modularity. Then, the SbOs can adopt different roles, being possible to reduce myopic behavior in different ways. More, it is also possible to grant local decisional entities with different autonomy levels by applying different interaction modes. In order to balance reactivity and global performance, our approach accepts configurations in which some myopic behaviors are reduced and others are accepted. Our approach was instantiated to control the assembly cell at Valenciennes AIPPRIMECA center. Simulation results showed that the proposed architecture reduces myopic behavior whereby it strikes a balance between reactivity and global performance. The real implementation on the assembly cell verified the effectiveness of our approach under realistic dynamic scenarios, and promising results were obtained.Le contrôle hétérarchique des systèmes de production flexibles (FMS) préconise un contrôle peu complexe et hautement réactif supporté par des entités décisionnelles locales (DEs). En dépit d'avancées prometteuses, ces architectures présentent un comportement myope car les DEs ont une visibilité informationnelle limitée sue les autres DEs, ce qui rend difficile la garantie d'une performance globale minimum. Cette thèse se concentre sur les approches permettant de réduire cette myopie. D'abord, une définition et une typologie de cette myopie dans les FMS sont proposées. Ensuite, nous proposons de traiter explicitement le comportement myope avec une architecture semi-hétérarchique. Dans celle-ci, une entité décisionnelle globale (GDE) traite différents types de décisions myopes à l'aide des différentes techniques d'optimisation basée sur la simulation (SbO). De plus, les SbO peuvent adopter plusieurs rôles, permettant de réduire le comportement myope de plusieurs façons. Il est également possible d'avoir plusieurs niveaux d'autonomie en appliquant différents modes d'interaction. Ainsi, notre approche accepte des configurations dans lesquelles certains comportements myopes sont réduits et d'autres sont acceptés. Notre approche a été instanciée pour contrôler la cellule flexible AIP- PRIMECA de l'Université de Valenciennes. Les résultats des simulations ont montré que l'architecture proposée peut réduire les comportements myopes en établissant un équilibre entre la réactivité et la performance globale. Des expérimentations réelles ont été réalisées sur la cellule AIP-PRIMECA pour des scenarios dynamiques et des résultats prometteurs ont été obtenus

Pollux: a dynamic hybrid control architecture for flexible job shop systems

Nowadays, manufacturing control systems can respond more effectively to exigent market requirements and real-time demands. Indeed, they take advantage of changing their structural and behavioural arrangements to tailor the control solution from a diverse set of feasible configurations. However, the challenge of this approach is to determine efficient mechanisms that dynamically optimise the configuration between different architectures. This paper presents a dynamic hybrid control architecture that integrates a switching mechanism to control changes at both structural and behavioural level. The switching mechanism is based on a genetic algorithm and strives to find the most suitable operating mode of the architecture with regard to optimality and reactivity. The proposed approach was tested in a real flexible job shop to demonstrate the applicability and efficiency of including an optimisation algorithm in the switching process of a dynamic hybrid control architecture.This work was supported by the Colombian scholarship programme of department of science – COLCIENCIAS under grant ‘Convocatoria 568 – Doctorados en el exterior’ and the Pontificia Universidad Javeriana under grant ‘Programa de Formacion de posgrados del Profesor Javeriano’.info:eu-repo/semantics/publishedVersio

Governance mechanism in control architectures for Flexible Manufacturing Systems

Manufacturing systems, and specifically Flexible Manufacturing Systems (FMS), face the challenge of accomplish global optimal performance and reactiveness at dynamic manufacturing environments. For this reason, manufacturing control systems must incorporate mechanisms that support dynamic custom-build responses. This paper introduces a framework that includes a governance mechanism in control system architectures that dynamically steers the autonomy of decision-making between predictive and reactive approaches. Results from experiments led in simulation show that it is worth studying in depth a governance mechanism that tailors the structure and/or behaviour of a manufacturing control system and, at the same time, potentiate the reactivity required in manufacturing operations.info:eu-repo/semantics/publishedVersio

Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system

PublishedThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Nowadays, manufacturing organisations face increasing pressures from the frequent change in product type, continuous demand fluctuation and unexpected change in customer requirements. In order to survive in the turbulent environment, manufacturing organisations must become flexible and responsive to these dynamic changes in the business environment. This paper presents a hierarchical agent bidding mechanism that is particularly designed for Make-to-Order manufacturing system and attempts to enhance the operational flexibility of manufacturing system in dealing with dynamic changes in the business environment. The novelty of this mechanism is that it enables manufacturing resources to be self-organised cost-efficiently within structural constraints of manufacturing system for fulfilling customer orders. However, when orders cannot be fulfilled within the structural constraints of manufacturing systems, the mechanism can enable manufacturing resources to be regrouped flexibly across system boundaries but with minimum disturbances to existing system structure. Based on an example application to a manufacturing company, this paper demonstrates that the operational flexibility provided by this mechanism is able to help manufacturing system to respond demand fluctuation through balancing the capacity across the entire system. Meanwhile, this mechanism potentially enables manufacturing systems to deal with unexpected changes in product type. As long as the manufacturing system has the technicality required by a new product, this mechanism enables resources across the manufacturing system to be cost-efficiently and flexibly self-organised to fulfil the new product

Application of Reinforcement Learning to Multi-Agent Production Scheduling

Reinforcement learning (RL) has received attention in recent years from agent-based researchers because it can be applied to problems where autonomous agents learn to select proper actions for achieving their goals based on interactions with their environment. Each time an agent performs an action, the environment¡Šs response, as indicated by its new state, is used by the agent to reward or penalize its action. The agent¡Šs goal is to maximize the total amount of reward it receives over the long run. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored. The objective of this research is to develop a set of guidelines for applying the Q-learning algorithm to enable an individual agent to develop a decision making policy for use in agent-based production scheduling applications such as dispatching rule selection and job routing. For the dispatching rule selection problem, a single machine agent employs the Q-learning algorithm to develop a decision-making policy on selecting the appropriate dispatching rule from among three given dispatching rules. In the job routing problem, a simulated job shop system is used for examining the implementation of the Q-learning algorithm for use by job agents when making routing decisions in such an environment. Two factorial experiment designs for studying the settings used to apply Q-learning to the single machine dispatching rule selection problem and the job routing problem are carried out. This study not only investigates the main effects of this Q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of Q-learning to agent-based production scheduling

A switching mechanism framework for optimal coupling of predictive scheduling and reactive control in manufacturing hybrid control architectures

Nowadays, manufacturing systems are seeking control architectures that offer efficient production performance and reactivity to disruptive events. Dynamic hybrid control architectures are a promising approach as they are not only able to switch dynamically between hierarchical, heterarchical and semi-heterarchical structures, they can also switch the level of coupling between predictive scheduling and reactive control techniques. However, few approaches address an efficient switching process in terms of structure and coupling. This paper presents a switching mechanism framework in dynamic hybrid control architectures, which exploits the advantages of hierarchical manufacturing scheduling systems and heterarchical manufacturing execution systems, and also mitigates the respective reactivity and optimality drawbacks. The main feature in this framework is that it monitors the system dynamics online and shifts between different operating modes to attain the most suitable production control strategy. The experiments were carried out in an emulation of a real manufacturing system to illustrate the benefits of including a switching mechanism in simulated scenarios. The results show that the switching mechanism improves response to disruptions in a global performance indicator as it permits to select the best alternative from several operating modes.This article was supported by COLCIENCIAS Departamento Administrativo de Ciencia, Tecnología e Innovación 10.13039/100007637 [Grant Number Convocatoria 568 Doctorados en el exterior]; Pontificia Universidad Javeriana [Grant Number Programa de Formacion de posgrados].info:eu-repo/semantics/publishedVersio

Dynamic self-organization in holonic multi-agent manufacturing systems: The ADACOR evolution

Nowadays, systems are becoming increasingly complex, mainly due to an exponential increase in the number of entities and their interconnections. Examples of these complex systems can be found in manufacturing, smart-grids, traffic control, logistics, economics and biology, among others. Due to this complexity, particularly in manufacturing, a lack of responsiveness in coping with demand for higher quality products, the drastic reduction in product lifecycles and the increasing need for product customization are being observed. Traditional solutions, based on central monolithic control structures, are becoming obsolete as they are not suitable for reacting and adapting to these perturbations. The decentralization of the complexity problem through simple, intelligent and autonomous entities, such as those found in multi-agent systems, is seen as a suitable methodology for tackling this challenge in industrial scenarios. Additionally, the use of biologically inspired self-organization concepts has proved to be suitable for being embedded in these approaches enabling better performances to be achieved. According to these principals, several approaches have been proposed but none can be truly embedded and extract all the potential of self-organization mechanisms. This paper proposes an evolution to the ADACOR holonic control architecture inspired by biological and evolutionary theories. In particular, a two-dimension al self-organization mechanism was designed taking the behavioural and structural vectors into consideration, thus allowing truly evolutionary and reconfigurable systems to be achieved that can cope with emergent requirements. The approach proposed is validated with two simulation use cases.info:eu-repo/semantics/publishedVersio

Dynamic Switching Mechanism to Support Self-organization in ADACOR Holonic Control System

Evolvable control systems face the demands for modularity, decentralization, reconfigurabil-ity and responsiveness pointed out by the Industrie 4.0 initiative. In these systems, the self-organization model assumes a critical issue to ensure the correct evolution of the system structure into different operating configurations. ADACOR holonic manufacturing control architecture introduces an adaptive production control mechanism that balances between two states, combining the optimization provided by hierarchical structures with agility and responsiveness to condition changes offered by decentralized structures. This paper describes the switching mechanism that supports this dynamic balance and particularly the local and global driving forces for the self-organization model. The proposed model was experimentally tested in a small scale production system.info:eu-repo/semantics/publishedVersio