Towards robustness of production planning and control against supply chain disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control

Towards Robustness Of Production Planning And Control Against Supply Chain Disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control

Random Keys Genetic Algorithms Scheduling and Rescheduling Systems for Common Production Systems

The majority of scheduling research deals with problems in specific production environments with specific objective functions. However, in many cases, more than one problem type and/or objective function exists, resulting in the need for a more generic and flexible system to generate schedules. Furthermore, most of the published scheduling research focuses on creating an optimal or near optimal initial schedule during the planning phase. However, after production processes start, circumstances like machine breakdowns, urgent jobs, and other unplanned events may render the schedule suboptimal, obsolete or even infeasible resulting in a rescheduling problem, which is typically also addressed for a specific production environment, constraints, and objective functions. This dissertation introduces a generic framework consisting of models and algorithms based on Random Keys Genetic Algorithms (RKGA) to handle both the scheduling and rescheduling problems in the most common production environments and for various types of objective functions. The Scheduling system produces predictive (initial) schedules for environments including single machines, flow shops, job shops and parallel machine production systems to optimize regular objective functions such as the Makespan and the Total Tardiness as well as non-regular objective functions such as the Total Earliness and Tardiness. To deal with the rescheduling problem, and using as a basis the same RKGA, a reactive Rescheduling system capable of repairing initial schedules after the occurrence of unexpected events is introduced. The reactive Rescheduling system was designed not only to optimize regular and non-regular objective functions but also to minimize the instability, a very important aspect in rescheduling to avoid shop chaos due to disruptions. Minimizing both schedule inefficiency and instability, however, turns the problem into a multi-objective optimization problem, which is even more difficult to solve. The computational experiments for the predictive model show that it is able to produce optimal or near optimal schedules to benchmark problems for different production environments and objective functions. Additional computational experiments conducted to test the reactive Rescheduling system under two types of unexpected events, machine breakdowns and the arrival of a rush job, show that the proposed framework and algorithms are robust in handling various problem types and computationally reasonable

Proactive management of uncertainty to improve scheduling robustness in proces industries

Dinamisme, capacitat de resposta i flexibilitat són característiques essencials en el desenvolupament de la societat actual. Les noves tendències de globalització i els avenços en tecnologies de la informació i comunicació fan que s'evolucioni en un entorn altament dinàmic i incert. La incertesa present en tot procés esdevé un factor crític a l'hora de prendre decisions, així com un repte altament reconegut en l'àrea d'Enginyeria de Sistemes de Procés (PSE). En el context de programació de les operacions, els models de suport a la decisió proposats fins ara, així com també software comercial de planificació i programació d'operacions avançada, es basen generalment en dades estimades, assumint implícitament que el programa d'operacions s'executarà sense desviacions. La reacció davant els efectes de la incertesa en temps d'execució és una pràctica habitual, però no sempre resulta efectiva o factible. L'alternativa és considerar la incertesa de forma proactiva, és a dir, en el moment de prendre decisions, explotant el coneixement disponible en el propi sistema de modelització.Davant aquesta situació es plantegen les següents preguntes: què s'entén per incertesa? Com es pot considerar la incertesa en el problema de programació d'operacions? Què s'entén per robustesa i flexibilitat d'un programa d'operacions? Com es pot millorar aquesta robustesa? Quins beneficis comporta? Aquesta tesi respon a aquestes preguntes en el marc d'anàlisis operacionals en l'àrea de PSE. La incertesa es considera no de la forma reactiva tradicional, sinó amb el desenvolupament de sistemes proactius de suport a la decisió amb l'objectiu d'identificar programes d'operació robustos que serveixin com a referència pel nivell inferior de control de planta, així com també per altres centres en un entorn de cadenes de subministrament. Aquest treball de recerca estableix les bases per formalitzar el concepte de robustesa d'un programa d'operacions de forma sistemàtica. Segons aquest formalisme, els temps d'operació i les ruptures d'equip són considerats inicialment com a principals fonts d'incertesa presents a nivell de programació de la producció. El problema es modelitza mitjançant programació estocàstica, desenvolupant-se finalment un entorn d'optimització basat en simulació que captura les múltiples fonts d'incertesa, així com també estratègies de programació d'operacions reactiva, de forma proactiva. La metodologia desenvolupada en el context de programació de la producció s'estén posteriorment per incloure les operacions de transport en sistemes de múltiples entitats i incertesa en els temps de distribució. Amb aquesta perspectiva més àmplia del nivell d'operació s'estudia la coordinació de les activitats de producció i transport, fins ara centrada en nivells estratègic o tàctic. L'estudi final considera l'efecte de la incertesa en la demanda en les decisions de programació de la producció a curt termini. El problema s'analitza des del punt de vista de gestió del risc, i s'avaluen diferents mesures per controlar l'eficiència del sistema en un entorn incert.En general, la tesi posa de manifest els avantatges en reconèixer i modelitzar la incertesa, amb la identificació de programes d'operació robustos capaços d'adaptar-se a un ampli rang de situacions possibles, enlloc de programes d'operació òptims per un escenari hipotètic. La metodologia proposada a nivell d'operació es pot considerar com un pas inicial per estendre's a nivells de decisió estratègics i tàctics. Alhora, la visió proactiva del problema permet reduir el buit existent entre la teoria i la pràctica industrial, i resulta en un major coneixement del procés, visibilitat per planificar activitats futures, així com també millora l'efectivitat de les tècniques reactives i de tot el sistema en general, característiques altament desitjables per mantenir-se actiu davant la globalitat, competitivitat i dinàmica que envolten un procés.Dynamism, responsiveness, and flexibility are essential features in the development of the current society. Globalization trends and fast advances in communication and information technologies make all evolve in a highly dynamic and uncertain environment. The uncertainty involved in a process system becomes a critical problem in decision making, as well as a recognized challenge in the area of Process Systems Engineering (PSE). In the context of scheduling, decision-support models developed up to this point, as well as commercial advanced planning and scheduling systems, rely generally on estimated input information, implicitly assuming that a schedule will be executed without deviations. The reaction to the effects of the uncertainty at execution time becomes a common practice, but it is not always effective or even possible. The alternative is to address the uncertainty proactively, i.e., at the time of reasoning, exploiting the available knowledge in the modeling procedure itself. In view of this situation, the following questions arise: what do we understand for uncertainty? How can uncertainty be considered within scheduling modeling systems? What is understood for schedule robustness and flexibility? How can schedule robustness be improved? What are the benefits? This thesis answers these questions in the context of operational analysis in PSE. Uncertainty is managed not from the traditional reactive viewpoint, but with the development of proactive decision-support systems aimed at identifying robust schedules that serve as a useful guidance for the lower control level, as well as for dependent entities in a supply chain environment. A basis to formalize the concept of schedule robustness is established. Based on this formalism, variable operation times and equipment breakdowns are first considered as the main uncertainties in short-term production scheduling. The problem is initially modeled using stochastic programming, and a simulation-based stochastic optimization framework is finally developed, which captures the multiple sources of uncertainty, as well as rescheduling strategies, proactively. The procedure-oriented system developed in the context of production scheduling is next extended to involve transport scheduling in multi-site systems with uncertain travel times. With this broader operational perspective, the coordination of production and transport activities, considered so far mainly in strategic and tactical analysis, is assessed. The final research point focuses on the effect of demands uncertainty in short-term scheduling decisions. The problem is analyzed from a risk management viewpoint, and alternative measures are assessed and compared to control the performance of the system in the uncertain environment.Overall, this research work reveals the advantages of recognizing and modeling uncertainty, with the identification of more robust schedules able to adapt to a wide range of possible situations, rather than optimal schedules for a hypothetical scenario. The management of uncertainty proposed from an operational perspective can be considered as a first step towards its extension to tactical and strategic levels of decision. The proactive perspective of the problem results in a more realistic view of the process system, and it is a promising way to reduce the gap between theory and industrial practices. Besides, it provides valuable insight on the process, visibility for future activities, as well as it improves the efficiency of reactive techniques and of the overall system, all highly desirable features to remain alive in the global, competitive, and dynamic process environment

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

A real-time simulation-based optimisation environment for industrial scheduling

n order to cope with the challenges in industry today, such as changes in product diversity and production volume, manufacturing companies are forced to react more flexibly and swiftly. Furthermore, in order for them to survive in an ever-changing market, they also need to be highly competitive by achieving near optimal efficiency in their operations. Production scheduling is vital to the success of manufacturing systems in industry today, because the near optimal allocation of resources is essential in remaining highly competitive. The overall aim of this study is the advancement of research in manufacturing scheduling through the exploration of more effective approaches to address complex, real-world manufacturing flow shop problems. The methodology used in the thesis is in essence a combination of systems engineering, algorithmic design and empirical experiments using real-world scenarios and data. Particularly, it proposes a new, web services-based, industrial scheduling system framework, called OPTIMISE Scheduling System (OSS), for solving real-world complex scheduling problems. OSS, as implemented on top of a generic web services-based simulation-based optimisation (SBO) platform called OPTIMISE, can support near optimal and real-time production scheduling in a distributed and parallel computing environment. Discrete-event simulation (DES) is used to represent and flexibly cope with complex scheduling problems without making unrealistic assumptions which are the major limitations of existing scheduling methods proposed in the literature. At the same time, the research has gone beyond existing studies of simulation-based scheduling applications, because the OSS has been implemented in a real-world industrial environment at an automotive manufacturer, so that qualitative evaluations and quantitative comparisons of scheduling methods and algorithms can be made with the same framework. Furthermore, in order to be able to adapt to and handle many different types of real-world scheduling problems, a new hybrid meta-heuristic scheduling algorithm that combines priority dispatching rules and genetic encoding is proposed. This combination is demonstrated to be able to handle a wider range of problems or a current scheduling problem that may change over time, due to the flexibility requirements in the real-world. The novel hybrid genetic representation has been demonstrated effective through the evaluation in the real-world scheduling problem using real-world data

A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers

In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation

Self-organisation of mobile robots in large structure assembly using multi-agent systems

Competition between manufacturers in large structure assembly (LSA) is driven by the need to improve the adaptability and versatility of their manufacturing systems. The lack of these qualities in the currently used systems is caused by the dedicated nature of their fixtures and jigs. This has led to their underutilisation and costly changeover procedures. In addition to that, modern automation systems tend to be dedicated to very specific tasks. This means that such systems are highly specialised and can reach obsolescence once there is a substantial change in production requirements. In this doctoral thesis, a dynamic system consisting of mobile robots is proposed to overcome those limitations. As a first knowledge contribution in this doctoral thesis, it is investigated under which conditions using mobile robots instead of the traditional, fixed automation systems in LSA can be advantageous. In this context, dynamic systems are expected to be more versatile and adaptive than fixed systems. Unlike traditional, dedicated automation systems, they are not constrained to gantry rails or fixed to the floor. This results in an expanded working envelope and consequently the ability to reach more workstations. Furthermore, if a product is large enough, the manufacturer can choose how many mobile robots to deploy around it. Accordingly, it was shown that the ability to balance work rates on products and consequently meet their due times is improved. For the second knowledge contribution, two fundamentally different decision-making models for controlling mobile agents in the complex scheduling problem are investigated. This is done to investigate ways of taking full advantage from the potential benefits of applying mobile robots. It is found that existing models from related academic literature are not suited for the given problem. Therefore, two new models had to be proposed for this purpose. It was plausible to use an agent-based approach for self-organisation. This is because similarly to agents, mobile robots can perform independently of one-another; and have limited perception and communication abilities. Finally, through a comparison study, scenarios are identified where either model is better to use. In agreement with much of the established literature in the field, the models are shown to exhibit the common advantages and disadvantages of their respective architecture types. Considering that the enabling technologies are nearing sufficient maturity for deploying mobile robots in LSA, it is concluded that this approach can have several advantages. Firstly, the granularity and freedom of movement enables much more control over product completion times. Secondly, the increased working envelope enables higher utilisation of manufacturing resources. In the context of LSA, this is a considerable challenge because products take a very long time to get loaded and unloaded from workstations. However, if the product flow is steady, there are rare disruptions and rare production changes, fixed automation systems have an advantage due to requiring much less time (if any) for moving and localising. Therefore, mobile systems become more preferred to fixed systems in environments where there is an increasing frequency of disruptions and changes in production requirements. The validation of agent-based self-organisation models for mobile robots in LSA confirms the expectations based on existing literature. Also, it reveals that with relatively low amounts of spare capacity (5%) in the manufacturing systems, there is little need for sophisticated models. The value of optimised models becomes apparent when spare capacity approaches 0% (or even negative values) and there is less room for inefficiencies in scheduling