Simulation and optimisation of a specific flexible manufacturing system.

As current market competition evolves, most companies intend to increase their options for product customisation and accelerate their product upgrading. Correspondingly, manufacturers have to face the increasing size of product family, shortened product life cycle or rapid product/process change. Therefore, Flexible Manufacturing Systems (FMS) have been introduced that uses advanced machines and efficient transport systems to produce multiple products at the same time. However, an FMS can be complicated to manage because of the increased variability in products and processes. The research aims to develop manufacturing simulation and optimisation techniques for a FMS. This research will integrate Discrete Event Simulation (DES) and multi-objective optimisation approach to address the complexity and flexibility within an agile manufacturing environment. Due to the complexity of FMS, most current FMS optimisation research has engaged with FMS production problems separately without considering other inter-related problems in the same system such as dealing with operation sequence problem without considering Level of Flexibility (LoF), thus it is hard for the solution to provide a prospective impact for the whole system. There are very few real-world FMS implementations that are available to literatures, making it difficult to build and verify the models within a complete ecosystem. Consequently, most of the models in the research are oversimplified. Therefore, this research aims to develop a method to optimise FMS production considering the overall system, by having access to an FMS industrial implementation. This research contributes to knowledge in four main areas, namely, (1) the interactions of FMS production problems have been investigated, (2) a framework has been developed to integrate the simulation and optimisation for FMS to enable optimisation algorithms working with DES models effectively, (3) a comprehensive FMS simulation model has been built and validated on the industrial shop floor and (4) multi-objective optimisation has been applied to the FMS scheduling problem, considering interactions with other problems. Based on the results and limitations of this research, real-time simulation, mock-up FMS and improve computational efficiency are suggested for future work.PhD in Manufacturin

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

An optimization approach for predictive-reactive job shop scheduling of reconfigurable manufacturing systems

The manufacturing industry is now moving forward rapidly towards reconfigurability and reliability to meet the hard-topredict global business market, especially job-shop production. However, even if there is a properly planned schedule for production, and there is also a technique for scheduling in Reconfigurable Manufacturing System (RMS) but job-shop production will always come out with errors and disruption due to complex and uncertainty happening during the production process, hence fail to fulfil the due-date requirements. This study proposes a generic control strategy for piloting the implementation of a complex scheduling challenge in an RMS. This study is aimed to formulate an optimization-based algorithm with a simulation tool to reduce the throughput time of complex RMS, which can comply with complex product allocations and flexible routings of the system. The predictive-reactive strategy was investigated, in which Genetic Algorithm (GA) and dispatching rules were used for predictive scheduling and reactivity controls. The results showed that the proposed optimization-based algorithm had successfully reduced the throughput time of the system. In this case, the effectiveness and reliability of RMS are increased by combining the simulation with the optimization algorithm

Energy-Efficient Technologies for High-Performance Manufacturing Industries

Ph.DDOCTOR OF PHILOSOPH

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

The optimisation and integration of AGVs with the manufacturing process

In recent years, the manufacturing environment, driven by the growth of advanced technologies and the increasing demand for customised products, has becomes increasingly competitive. In this context, manufacturing systems are now required to be more automated, flexible and reconfigurable. Thus, Autonomous Guided Vehicle (AGV), as a key enabler of dynamic shop floor logistics, are being increasingly widely deployed into the manufacturing sector for the lineside materials supplying, work-in-progress transportation, and finished products collection. A large number of companies and institutions are researching on different AGV systems to integrate AGVs-based shop floor logistics with manufacturing equipment and processes. However, these AGV systems are typically equipped with various communication protocols and utilise ad-hoc communication methods. They lack a generic framework to integrate the AGV systems into the manufacturing systems with minimal engineering effort and system reconfiguration. Current scheduling optimisation methods for multiple AGVs in shop floor logistics now support effective task allocation, shortest route planning, and conflict-free supervision, allocating the delivery tasks based on the location and availability of AGVs. However, these current methods do not give enough consideration to real-time operational information during the manufacturing process and have difficulties in analysing the real-time delivery requests from manufacturing work stations. This not only reduces the efficiency and flexibility of the shop floor logistics, ii but also significantly impacts on the overall performance of manufacturing processes. This thesis presents a generic integration approach, called Smart AGV Management System (SAMS), to support the integration of AGVs with manufacturing processes. The proposed framework enables enhanced interoperability between AGVs-based shop floor logistics and the manufacturing process through a generic data-sharing platform. Moreover, a Digital Twin (DT)-based optimisation method is developed in SAMS that can simulate and analyse the real-time manufacturing process to schedule AGVs for optimising multiple objectives, including the utilisation of work stations, delivery Justin- time (JIT) performance, charging of AGVs and overall energy consumption. This approach is experimentally deployed and evaluated from various perspectives to identify its integration and optimisation capabilities during the reconfiguration and operational phases. The results show that the proposed integration framework can enable a more effective integration with manufacturing process compared to traditional integration methods. In addition, the results demonstrate that the proposed optimisation method can schedule and reschedule AGV-based shop floor logistics when facing a range of system disruptions

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Proposition d’une architecture holonique auto-organisée et évolutive pour le pilotage des systèmes de production

The manufacturing world is being deeply challenged with a set of ever demanding constraints where from one side, the costumers are requiring products to be more customizable, with higher quality at lower prices, and on other side, companies have to deal on a daily basis with internal disturbances that range from machine breakdown to worker absence and from demand fluctuation to frequent production changes. This dissertation proposes a manufacturing control architecture, following the holonic principles developed in the ADAptive holonic COntrol aRchitecture (ADACOR) and extending it taking inspiration in evolutionary theories and making use of self- organization mechanisms. The use of evolutionary theories enrich the proposed control architecture by allowing evolution in two distinct ways, responding accordingly to the type and degree of the disturbance that appears. The first component, named behavioural self- organization, allows each system’s entity to dynamically adapt its internal behaviour, addressing small disturbances. The second component, named structural self-organization, addresses bigger disturbances by allowing the system entities to re-arrange their rela- tionships, and consequently changing the system in a structural manner. The proposed self-organized holonic manufacturing control architecture was validated at a AIP-PRIMECA flexible manufacturing cell. The achieved experimental results have also shown an improvement of the key performance indicators over the hierarchical and heterarchical control architecture.Le monde des entreprises est profondément soumis à un ensemble de contraintes toujours plus exigeantes provenant d’une part des clients, exigeant des produits plus personnalisables, de qualité supérieure et à faible coût, et d’autre part des aléas internes auxentreprises, comprenant les pannes machines, les défaillances humaines, la fluctuation de la demande, les fréquentes variations de production. Cette thèse propose une architecture de contrôle de systèmes de production, basée sur les principes holoniques développées dans l’architecture ADACOR (ADAptive holonic COntrol aRchitecture), et l’étendant en s’inspirant des théories de l’évolution et en utilisant des mécanismes d’auto-organisation. L’utilisation des théories de l’évolution enrichit l’architecture de contrôle en permettant l’évolution de deux manières distinctes, en réponse au type et au degré de la perturbation apparue. Le premier mode d’adaptation, appelé auto-organisation comportementale, permet à chaque entité qui compose le système d’adapter dynamiquement leur comportement interne, gérant de cette façon de petites perturbations. Le second mode, nommé auto-organisation structurelle, traite de plus grandes perturbations, en permettant aux entités du système de ré-organiser leurs relations, et par conséquent modifier structurellement le système. L’architecture holonique auto-organisée de contrôle de systèmes de production proposée dans cette thèse a été validée sur une cellule de production flexible AIP-PRIMECA. Les résultats ont montré une amélioration des indicateurs clés de performance par rapport aux architectures de contrôle hiérarchiques et hétérarchiques

Flexible Job-shop Scheduling Problem with Sequencing Flexibility: Mathematical Models and Solution Algorithms

Marketing strategists usually advocate increased product variety to attend better market demand. Furthermore, companies increasingly acquire more advanced manufacturing systems to take care of the increased product mix. Manufacturing resources with different capabilities give a competitive advantage to the industry. Proper management of the current productions resources is crucial for a thriving industry. Flexible job shop scheduling problem (FJSP) is an extension of the classical Job-shop scheduling problem (JSP) where operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled FJSP with sequencing flexibility (FJSPS), is studied in this work. The extension considers precedence between the operations in the form of a directed acyclic graph instead of sequential order. In this work, a mixed integer programming (MILP) formulation is presented. A single objective formulation to minimize the weighted tardiness for the FJSP with sequencing flexibility is proposed. A different objective to minimize makespan is also considered. Due to the NP-hardness of the problem, a novel hybrid bacterial foraging optimization algorithm (HBFOA) is developed to tackle the FJSP with sequencing flexibility. It is inspired by the behaviour of the E. coli bacteria. It mimics the process to seek for food. The HBFOA is enhanced with simulated annealing (SA). The HBFOA has been packaged in the form of a decision support system (DSS). A case study of a small and medium-sized enterprise (SME) manufacturing industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments with instances provided by the literature are considered. The results demonstrate that the HBFOA outperformed the classical dispatching rules and the best integer solution of MILP when minimizing the weighted tardiness and offered comparable results for the makespan instances. In this dissertation, another critical aspect has been studied. In the industry, skilled workers usually are able to operate a specific set of machines. Hence, managers need to decide the best operation assignments to machines and workers. However, they need also to balance the workload between workers while accomplishing the due dates. In this research, a multi-objective mathematical model that minimizes makespan, maximal worker workload and weighted tardiness is developed. This model is entitled dual-resource FJSP with sequencing flexibility (DRFJSPS). It covers both the machine assignment and also the worker selection. Due to the intractability of the DRFJSPS, an elitist non-dominated sorting genetic algorithm (NSGA-II) is developed to solve this problem efficiently. The algorithm provides a set of Pareto-optimal solutions that the decision makers can use to evaluate the trade-offs of the conflicting objectives. New instances are introduced to demonstrate the applicability of the model and algorithm. A multi-random-start local search algorithm has been developed to assess the effectiveness of the adapted NSGA-II. The comparison of the solutions demonstrates that the modified NSGA-II provides a non-dominated efficient set in a reasonable time. Finally, a situation where there are multiple process plans available for a specific job is considered. This scenario is useful to be able to react to the current status of the shop where unpredictable circumstances (machine breakdown, current product mix, due dates, demand, etc.) can be accurately tackled. The determination of the process plan also depends on its cost. For that, a balance between cost, and the accomplishment of due dates is required. A multi-objective mathematical model that minimizes makespan, total processing cost and weighted tardiness are proposed to determine the sequence and the process plan to be used. This model is entitled flexible job-shop scheduling problem with sequencing and process plan flexibility (FJSP-2F). New instances are generated to show the applicability of the model