159 research outputs found
Production Scheduling
Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume
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Simulation and optimization techniques applied in semiconductor assembly and test operations
The importance of back-end operations in semiconductor manufacturing has been growing steadily in the face of higher customer expectations and stronger competition in the industry. In order to achieve low cycle times, high throughput, and high utilization while improving due-date performance, more effective tools are needed to support machine setup and lot dispatching decisions. In previous work, the problem of maximizing the weighted throughput of lots undergoing assembly and test (AT), while ensuring that critical lots are given priority, was investigated and a greedy randomized adaptive search procedure (GRASP) developed to find solutions. Optimization techniques have long been used for scheduling manufacturing operations on a daily basis. Solutions provide a prescription for machine setups and job processing over a finite the planning horizon. In contrast, simulation provides more detail but in a normative sense. It tells you how the system will evolve in real time for a given demand, a given set of resources and rules for using them. A simulation model can also accommodate changeovers, initial setups and multi-pass requirements easily. The first part of the research is to show how the results of an optimization model can be integrated with the decisions made within a simulation model. The problem addressed is defined in terms of four hierarchical objectives: minimize the weighted sum of key device shortages, maximize weighted throughput, minimize the number of machines used, and minimize makespan for a given set of lots in queue, and a set of resources that includes machines and tooling. The facility can be viewed as a reentrant flow shop. The basic simulation was written in AutoSched AP (ASAP) and then enhanced with the help of customization features available in the software. Several new dispatch rules were developed. Rule_First_setup is able to initialize the simulation with the setups obtained with the GRASP. Rule_All_setups enables a machine to select the setup provided by the optimization solution whenever a decision is about to be made on which setup to choose subsequent to the initial setup. Rule_Hotlot was also proposed to prioritize the processing of the hot lots that contain key devices. The objective of the second part of the research is to design and implement heuristics within the simulation model to schedule back-end operations in a semiconductor AT facility. Rule_Setupnum lets the machines determine which key device to process according to a machine setup frequency table constructed from the GRASP solution. GRASP_asap embeds a more robust selection features of GRASP in the ASAP model through customization. This allows ASAP to explore a larger portion of the feasible region at each decision point by randomizing machine setups using adaptive probability distributions that are a function of solution quality. Rule_Greedy, which is a simplification of GRASP_asap, always picks the setup for a particular machine that gives the greatest marginal improvement in the objective function among all candidates. The purpose of the third part of the research is to statistically validate the relative effectiveness of our top six dispatch rules by comparing their performance on 30 real and randomly generated data sets. Using both GRASP and our ASAP discrete event simulation model, we have (1) identified the general order of dispatch rule performance, (2) investigated the impact of having setups installed on machines at time zero on rule performance, (3) determined the conditions under which restricting the maximum number of changeover affects the rule performance, and (4) studied the factors that might simultaneously affect rule performance with the help of a common random numbers experimental design. In the analysis, the first two objectives, weighted key device shortages and weighted throughput, are used to measure outcomes.Operations Research and Industrial Engineerin
Adaptive Order Dispatching based on Reinforcement Learning: Application in a Complex Job Shop in the Semiconductor Industry
Heutige Produktionssysteme tendieren durch die Marktanforderungen getrieben zu immer kleineren LosgröĂen, höherer Produktvielfalt und gröĂerer KomplexitĂ€t der Materialflusssysteme. Diese Entwicklungen stellen bestehende Produktionssteuerungsmethoden in Frage. Im Zuge der Digitalisierung bieten datenbasierte Algorithmen des maschinellen Lernens einen alternativen Ansatz zur Optimierung von ProduktionsablĂ€ufen. Aktuelle Forschungsergebnisse zeigen eine hohe LeistungsfĂ€higkeit von Verfahren des Reinforcement Learning (RL) in einem breiten Anwendungsspektrum. Im Bereich der Produktionssteuerung haben sich jedoch bisher nur wenige Autoren damit befasst. Eine umfassende Untersuchung verschiedener RL-AnsĂ€tze sowie eine Anwendung in der Praxis wurden noch nicht durchgefĂŒhrt. Unter den Aufgaben der Produktionsplanung und -steuerung gewĂ€hrleistet die Auftragssteuerung (order dispatching) eine hohe LeistungsfĂ€higkeit und FlexibilitĂ€t der ProduktionsablĂ€ufe, um eine hohe KapazitĂ€tsauslastung und kurze Durchlaufzeiten zu erreichen. Motiviert durch komplexe Werkstattfertigungssysteme, wie sie in der Halbleiterindustrie zu finden sind, schlieĂt diese Arbeit die ForschungslĂŒcke und befasst sich mit der Anwendung von RL fĂŒr eine adaptive Auftragssteuerung. Die Einbeziehung realer Systemdaten ermöglicht eine genauere Erfassung des Systemverhaltens als statische Heuristiken oder mathematische Optimierungsverfahren. ZusĂ€tzlich wird der manuelle Aufwand reduziert, indem auf die InferenzfĂ€higkeiten des RL zurĂŒckgegriffen wird.
Die vorgestellte Methodik fokussiert die Modellierung und Implementierung von RL-Agenten als Dispatching-Entscheidungseinheit. Bekannte Herausforderungen der RL-Modellierung in Bezug auf Zustand, Aktion und Belohnungsfunktion werden untersucht. Die Modellierungsalternativen werden auf der Grundlage von zwei realen Produktionsszenarien eines Halbleiterherstellers analysiert. Die Ergebnisse zeigen, dass RL-Agenten adaptive Steuerungsstrategien erlernen können und bestehende regelbasierte Benchmarkheuristiken ĂŒbertreffen. Die Erweiterung der ZustandsreprĂ€sentation verbessert die Leistung deutlich, wenn ein Zusammenhang mit den Belohnungszielen besteht. Die Belohnung kann so gestaltet werden, dass sie die Optimierung mehrerer ZielgröĂen ermöglicht. SchlieĂlich erreichen spezifische RL-Agenten-Konfigurationen nicht nur eine hohe Leistung in einem Szenario, sondern weisen eine Robustheit bei sich Ă€ndernden Systemeigenschaften auf.
Damit stellt die Forschungsarbeit einen wesentlichen Beitrag in Richtung selbstoptimierender und autonomer Produktionssysteme dar. Produktionsingenieure mĂŒssen das Potenzial datenbasierter, lernender Verfahren bewerten, um in Bezug auf FlexibilitĂ€t wettbewerbsfĂ€hig zu bleiben und gleichzeitig den Aufwand fĂŒr den Entwurf, den Betrieb und die Ăberwachung von Produktionssteuerungssystemen in einem vernĂŒnftigen Gleichgewicht zu halten
Intelligent shop scheduling for semiconductor manufacturing
Semiconductor market sales have expanded massively to more than 200 billion dollars annually accompanied by increased pressure on the manufacturers to provide higher quality products at lower cost to remain competitive. Scheduling of semiconductor manufacturing is one of the keys to increasing productivity, however the complexity of manufacturing high capacity semiconductor devices and the cost considerations mean that it is impossible to experiment within the facility. There is an immense need for effective decision support models, characterizing and analyzing the manufacturing process, allowing the effect of changes in the production environment to be predicted in order to increase utilization and enhance system performance. Although many simulation models have been developed within semiconductor manufacturing very little research on the simulation of the photolithography process has been reported even though semiconductor manufacturers have recognized that the scheduling of photolithography is one of the most important and challenging tasks due to complex nature of the process.
Traditional scheduling techniques and existing approaches show some benefits for solving small and medium sized, straightforward scheduling problems. However, they have had limited success in solving complex scheduling problems with stochastic elements in an economic timeframe. This thesis presents a new methodology combining advanced solution approaches such as simulation, artificial intelligence, system modeling and Taguchi methods, to schedule a photolithography toolset. A new structured approach was developed to effectively support building the simulation models. A single tool and complete toolset model were developed using this approach and shown to have less than 4% deviation from actual production values. The use of an intelligent scheduling agent for the toolset model shows an average of 15% improvement in simulated throughput time and is currently in use for scheduling the photolithography toolset in a manufacturing plant
Autonomous Finite Capacity Scheduling using Biological Control Principles
The vast majority of the research efforts in finite capacity scheduling over the past several years has focused on the generation of precise and almost exact measures for the working schedule presupposing complete information and a deterministic environment. During execution, however, production may be the subject of considerable variability, which may lead to frequent schedule interruptions.
Production scheduling mechanisms are developed based on centralised control architecture in which all of the knowledge base and databases are modelled at the same location. This control architecture has difficulty in handling complex manufacturing systems that require knowledge and data at different locations. Adopting biological control principles refers to the process where a schedule is developed prior to the start of the processing after considering all the parameters involved at a resource involved and updated accordingly as the process executes.
This research reviews the best practices in gene transcription and translation control methods and adopts these principles in the development of an autonomous finite capacity scheduling control logic aimed at reducing excessive use of manual input in planning tasks. With autonomous decision-making functionality, finite capacity scheduling will as much as practicably possible be able to respond autonomously to schedule disruptions by deployment of proactive scheduling procedures that may be used to revise or re-optimize the schedule when unexpected events occur.
The novelty of this work is the ability of production resources to autonomously take decisions and the same way decisions are taken by autonomous entities in the process of gene transcription and translation. The idea has been implemented by the integration of simulation and modelling techniques with Taguchi analysis to investigate the contributions of finite capacity scheduling factors, and determination of the âwhat ifâ scenarios encountered due to the existence of variability in production processes. The control logic adopts the induction rules as used in gene expression control mechanisms, studied in biological systems. Scheduling factors are identified to that effect and are investigated to find their effects on selected performance measurements for each resource in used. How they are used to deal with variability in the process is one major objective for this research as it is because of the variability that autonomous decision making becomes of interest.
Although different scheduling techniques have been applied and are successful in production planning and control, the results obtained from the inclusion of the autonomous finite capacity scheduling control logic has proved that significant improvement can still be achieved
Demystifying reinforcement learning approaches for production scheduling
Recent years has seen a sharp rise in interest pertaining to Reinforcement Learning (RL) approaches for production scheduling.
This is because RL is seen as a an advantageous compromise between the two most typical scheduling solution approaches, namely priority rules and exact approaches.
However, there are many variations of both production scheduling problems and RL solutions.
Additionally, the RL production scheduling literature is characterized by a lack of standardization, which leads to the field being shrouded in mysticism.
The burden of showcasing the exact situations where RL outshines other approaches still lies with the research community.
To pave the way towards this goal, we make the following four contributions to the scientific community, aiding in the process of RL demystification.
First, we develop a standardization framework for RL scheduling approaches using a comprehensive literature review as a conduit.
Secondly, we design and implement FabricatioRL, an open-source benchmarking simulation framework for production scheduling covering a vast array of scheduling problems and ensuring experiment reproducibility.
Thirdly, we create a set of baseline scheduling algorithms sharing some of the RL advantages.
The set of RL-competitive algorithms consists of a Constraint Programming (CP) meta-heuristic developed by us, CP3, and two simulation-based approaches namely a novel approach we call Simulation Search and Monte Carlo Tree Search.
Fourth and finally, we use FabricatioRL to build two benchmarking instances for two popular stochastic production scheduling problems, and run fully reproducible experiments on them, pitting Double Deep Q Networks (DDQN) and AlphaGo Zero (AZ) against the chosen baselines and priority rules.
Our results show that AZ manages to marginally outperform priority rules and DDQN, but fails to outperform our competitive baselines
Aide à la prise de décision en temps réel dans un contexte de production adaptative
La dynamique des marchĂ©s a Ă©voluĂ© et les entreprises manufacturiĂšres ont dĂ» sâadapter pour rester compĂ©titives. Une usine se dĂ©finissait historiquement par les biens quâelle produisait. La valeur de ces biens Ă©tait Ă©valuĂ©e avant tout par leurs composants. Mais sous la pression du marchĂ© et de son dynamisme accru, les usines souhaitant rester compĂ©titives deviennent de plus en plus des centres de service. Cela provoque des changements et des problĂšmes de gestion pour lesquels elles nâĂ©taient pas prĂ©parĂ©es. LâefficacitĂ© Ă©conomique de la crĂ©ation de valeur nâest plus aujourdâhui la seule propriĂ©tĂ© des produits, mais se dĂ©place vers les processus. Cela signifie que les potentiels qui seront dĂ©cisifs ne sont pas Ă chercher dans les capacitĂ©s des produits, mais dans les capacitĂ©s des processus. En effet, la mondialisation accroit lâanonymat des produits tout au long de chaĂźnes dâapprovisionnement plus longues et plus complexes. Toute entreprise souhaitant se dĂ©marquer de la compĂ©tition doit proposer Ă ses clients de la valeur ajoutĂ©e additionnelle tels quâune flexibilitĂ© accrue, des dĂ©lais de livraison plus courts, un meilleur respect des dĂ©lais, un plus grand choix dâoptions. Ces propriĂ©tĂ©s sont le fruit des processus. Leur valeur ajoutĂ©e se transfĂšre Ă leur rĂ©sultat et donc au client. Une des conditions nĂ©cessaires Ă la transparence des processus est leur capacitĂ© Ă coller en temps rĂ©el au flux de valeur de lâentreprise. Les processus doivent ĂȘtre en mesure de sâadapter aux conditions changeantes de lâenvironnement, de rĂ©agir Ă des Ă©vĂ©nements imprĂ©vus et de rĂ©soudre ces difficultĂ©s en collaborant. Câest Ă ces conditions quâils pourront devenir des processus adaptatifs.
Cette thĂšse sâintĂ©resse aux processus de rĂ©ordonnancement en milieu industriel. Elle vise lâimplantation de composantes dâaide Ă la prise de dĂ©cision en temps rĂ©el ainsi que des mĂ©canismes de boucle rĂ©troactive intĂ©grant lâoptimisation et les techniques de simulation au sein dâapplications ERP et MES permettant ainsi de connecter lâatelier de production au reste de lâentreprise. La plateforme qui a Ă©tĂ© mise en place permet de rĂ©pondre en temps rĂ©el aux divers alĂ©as survenant dans lâatelier et peut ĂȘtre Ă©tendue au-delĂ de la problĂ©matique de lâordonnancement.----------ABSTRACT The market dynamics have evolved and manufacturing facilities have followed this trend to stay competitive. The classic factory has been defined by its manufactured goods. The value of these goods has been measured primarily by their material components. But under the market pressure and its increasing dynamism, factories wishing to stay competitive are becoming modern service centers. It has resulted in management problems for which many companies are not yet prepared. Today, the economic efficiency of value creation is not a property of the products but rather of the process. It means the decisive potentials of companies are to be found not so much in their production capability but in their process capability. Indeed, increasing globalization is necessarily leading towards more anonymous products out of long supply chains. Any enterprise wishing to stand out from the competition in the future needs a strategy which offers the customer an additional added value, such as, for example, high flexibility, short delivery times, high delivery reliability, and wide range of variants. These properties are created by the processes. The requirement for process capability gives rise in turn to the requirement that all value-adding processes be geared to the process result and thus to the customer. A necessary condition of process transparency is the ability to map the company's value stream in real time. Processes must be able to adapt to environmental changing conditions, react to unforeseen events and to solve these difficulties by collaborating. Under these conditions they can be called adaptive processes.
This thesis focuses on scheduling process in manufacturing environments. The main objective is to implement real time decision-making support components as well as feedback loop mechanisms integrating optimization and simulation techniques in ERP and MES applications allowing connecting the shop floor to the rest of the enterprise. The proposed platform responds in real time to various events occurring on the shop floor and may be extended beyond the scheduling issue.
The works developed during this thesis are based on four published, accepted or submitted papers to specialized papers
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