Production planning mechanisms in demand-driven wood remanufacturing industry

L'objectif principal de cette thèse est d'étudier le problème de planification de la production dans le contexte d'une demande incertaine, d’un niveau de service variable et d’approvisionnements incontrôlables dans une usine de seconde transformation du bois. Les activités de planification et de contrôle de production sont des tâches intrinsèquement complexes et difficiles pour les entreprises de seconde transformation du bois. La complexité vient de certaines caractéristiques intrinsèques de cette industrie, comme la co-production, les procédés alternatifs divergents, les systèmes de production sur commande (make-to-order), des temps de setup variables et une offre incontrôlable. La première partie de cette thèse propose une plate-forme d'optimisation/simulation permettant de prendre des décisions concernant le choix d'une politique de planification de la production, pour traiter rapidement les demandes incertaines, tout en tenant compte des caractéristiques complexes de l'industrie de la seconde transformation du bois. À cet effet, une stratégie de re-planification périodique basée sur un horizon roulant est utilisée et validée par un modèle de simulation utilisant des données réelles provenant d'un partenaire industriel. Dans la deuxième partie de cette thèse, une méthode de gestion des stocks de sécurité dynamique est proposée afin de mieux gérer le niveau de service, qui est contraint par une capacité de production limitée et à la complexité de la gestion des temps de mise en course. Nous avons ainsi développé une approche de re-planification périodique à deux phases, dans laquelle des capacités non-utilisées (dans la première phase) sont attribuées (dans la seconde phase) afin de produire certains produits jugés importants, augmentant ainsi la capacité du système à atteindre le niveau de stock de sécurité. Enfin, dans la troisième partie de la thèse, nous étudions l’impact d’un approvisionnement incontrôlable sur la planification de la production. Différents scénarios d'approvisionnement servent à identifier les seuils critiques dans les variations de l’offre. Le cadre proposé permet aux gestionnaires de comprendre l'impact de politiques d'approvisionnement proposées pour faire face aux incertitudes. Les résultats obtenus à travers les études de cas considérés montrent que les nouvelles approches proposées dans cette thèse constituent des outils pratiques et efficaces pour la planification de production du bois.The main objective of this thesis is to investigate the production planning problem in the context of uncertain demand, variable service level, and uncontrollable supply in a wood remanufacturing mill. Production planning and control activities are complex and represent difficult tasks for wood remanufacturers. The complexity comes from inherent characteristics of the industry such as divergent co-production, alternative processes, make-to-order, short customer lead times, variable setup time, and uncontrollable supply. The first part of this thesis proposes an optimization/simulation platform to make decisions about the selection of a production planning policy to deal swiftly with uncertain demands, under the complex characteristics of the wood remanufacturing industry. For this purpose, a periodic re-planning strategy based on a rolling horizon was used and validated through a simulation model using real data from an industrial partner. The computational results highlighted the significance of using the re-planning model as a practical tool for production planning under unstable demands. In the second part, a dynamic safety stock method was proposed to better manage service level, which was threatened by issues related to limited production capacity and the complexity of setup time. We developed a two-phase periodic re-planning approach whereby idle capacities were allocated to produce more important products thus increasing the realization of safety stock level. Numerical results indicated that the solution of the two-phase method was superior to the initial method in terms of backorder level as well as inventory level. Finally, we studied the impact of uncontrollable supply on demand-driven wood remanufacturing production planning through an optimization and simulation framework. Different supply scenarios were used to identify the safety threshold of supply changes. The proposed framework provided managers with a novel advanced planning approach that allowed understanding the impact of supply policies to deal with uncertainties. In general, the wood products industry offers a rich environment for dealing with uncertainties for which the literature fails to provide efficient solutions. Regarding the results that were obtained through the case studies, we believe that approaches proposed in this thesis can be considered as novel and practical tools for wood remanufacturing production planning

Coordinated rescheduling of precast production

Ph.DDOCTOR OF PHILOSOPH

A Robust Reactive Scheduling System with Application to Parallel Machine Scheduling

In this turbulent world, scheduling role has become crucial in most manufacturing production, and service systems. It allows the allocation of limited resources to activities with the objective of optimizing one performance measure or more. Resources may be machines in a factory, operating rooms in a hospital, or employees in a company, while activities can be jobs in a manufacturing plant, surgeries in a hospital, or paper work in a company. The goal of each schedule is to optimize some performance measures, which could be the minimization of the schedule makespan, the jobs\u27 completion times, jobs\u27 earliness and tardiness, among others. Until very recently, research has concentrated on scenarios that assume a predefined schedule that is failure free. Initial schedules produced in advance are being followed hoping no delays will occur, because once they do, the whole schedule may be compromised as it is not designed to adapt to change. Researchers focused on the generation of good schedules in the presence of complex constraints while assuming fixed processing times, known job arrival times, unbreakable machines, and immune employees. However, this is not the case in the real world, where processing times are stochastic, job arrival times could be unknown, machines do break down, and employees get sick. In fact, most environments including manufacturing are dynamic by nature and not static, vulnerable to many unpredictable events, which leads the initial schedule to become obsolete once it is executed. The reason these deterministic schedules fail is because they do not account for variability, scheduling the activities directly after each other, so when a certain activity is delayed, all its successors will be delayed too. In this dissertation, new repair and rescheduling algorithms, and robust systems equipped with learning capability are developed for the unrelated parallel machine environment, a known NP-hard problem. The introduced rules and algorithms were subjected to different stochastic rates of breakdowns and delays and were judged based on several performance measures to ensure the optimization of both the schedule quality and stability. Schedule quality is assessed based on the schedule Makespan (time to finish all jobs) and CPU, while schedule stability is based on the number of shifted jobs from one machine to another and the time to match up with the original schedule after the occurrence of a breakdown. The extensive computational tests and analyses show the superiority of the proposed algorithms and systems compared to existing methods in the literature, especially when implemented with the learning capability. Moreover, the rules were ranked based on their performance for different performance measure combinations, allowing the decision maker to easily determine the most appropriate repair/rescheduling rule depending on the performance measure(s) desired

Diseño de un modelo de reprogramación de la producción bajo condiciones de incertidumbre en el proceso de aprovisionamiento de materias primas

El curso de esta investigación permitió desarrollar un modelo de reprogramación de la producción como respuesta a una interrupción ocasionada por la no disponibilidad de materia prima de sus tareas o trabajos, con el objetivo de obtener una nueva programación lo más cercana posible a la programación inicial obteniendo resultados similares a las métricas de Makespan y Tardanza total. Para obtener este modelo, se desarrolló en MATLAB un modelo inicial de programación para Flow shop para minimización de Makespan en base al algoritmo genético de Chu-Beasley; luego, en base al modelo anterior, se desarrolló un modelo de programación para Flexible Flow Shop para la solución de un problema bi-objetivo (minimización del Makespan y Tardanza total), y en base a éste, se logró desarrollar el modelo de reprogramación de la producción que minimiza la sumatoria de la diferencia de los tiempos de inicio de los trabajos entre la programación inicial y la nueva secuencia, y a su vez, minimiza la tardanza total.MaestríaMagister en Ingeniería Industria

Decentralized Scheduling Using The Multi-Agent System Approach For Smart Manufacturing Systems: Investigation And Design

The advent of industry 4.0 has resulted in increased availability, velocity, and volume of data as well as increased data processing capabilities. There is a need to determine how best to incorporate these advancements to improve the performance of manufacturing systems. The purpose of this research is to present a solution for incorporating industry 4.0 into manufacturing systems. It will focus on how such a system would operate, how to select resources for the system, and how to configure the system. Our proposed solution is a smart manufacturing system that operates as a self-coordinating system. It utilizes a multi-agent system (MAS) approach, where individual entities within the system have autonomy to make dynamic scheduling decisions in real-time. This solution was shown to outperform alternative scheduling strategies (right shifting and dispatching priority rule) in manufacturing environments subject to uncertainty in our simulation experiments. The second phase of our research focused on system design. This phase involved developing models for two problems: (1) resource selection, and (2) layout configuration. Both models developed use simulation-based optimization. We first present a model for determining machine resources using a genetic algorithm (GA). This model yielded results comparable to an exhaustive search whilst significantly reducing the number of required experiments to find the solution. To address layout configuration, we developed a model that combines hierarchical clustering and GA. Our numerical experiments demonstrated that the hybrid layouts derived from the model result in shorter and less variable order completion times compared to alternative layout configurations. Overall, our research showed that MAS-based scheduling can outperform alternative dynamic scheduling approaches in manufacturing environments subject to uncertainty. We also show that this performance can further be improved through optimal resource selection and layout design

Programación de la producción en un taller de flujo híbrido sujeto a incertidumbre: arquitectura y algoritmos. Aplicación a la industria cerámica

En un marco de competencia global en el cual los tiempos de respuesta son cada vez más relevantes como elemento competitivo y donde, en no pocas ocasiones, las empresas tiende a ofrecer un catálogo de productos amplio y diferenciado de la competencia, existen múltiples retos que las Organizaciones deben afrontar. Dentro de éstas la Dirección de Operaciones tiene el reto de adaptar los procesos de Gestión de los Sistemas Productivos y Logísticos a las actuales necesidades. En este proceso de cambio es habitual partir de Sistemas Productivos poco flexibles y orientados a la producción en masa en los que es fundamental emplear el mejor "saber-hacer" para procurar obtener el rendimiento más adecuado de los recursos disponibles. El despliegue de unas buenas prácticas en el ámbito de la Programación de la Producción puede ayudar en buena medida a mejorar la eficiencia de los recursos. Tradicionalmente se ha venido considerando a la Programación de la Producción con una visión bastante cuantitativa en la que su misión consistía en asignar, secuenciar y temporizar los diferentes trabajos del periodo en base a los recursos disponibles. No obstante, sin dejar de ser válido este planteamiento, en esta tesis se desea enfatizar como en realidad el fin último de las técnicas y métodos desarrollados durante años en el ámbito de la Programación de la Producción no es otro que el de ser empleados dentro de un Sistemas de Ayuda a la Toma de Decisiones. Y en este sentido, las decisiones operativas que se toman en el área del Programador de la Producción deben estar conectadas en todos los casos, al menos, con su entorno decisional más directo como es el de la Planificación de la Producción. Una revisión literaria en profundidad al extenso trabajo realizado en más de 50 años de existencia de lo que se ha denominado, empleando la terminología en lengua inglesa, como "Scheduling" pone de manifiesto la existencia una necesidad que debe ser cubierta.Gómez Gasquet, P. (2010). Programación de la producción en un taller de flujo híbrido sujeto a incertidumbre: arquitectura y algoritmos. Aplicación a la industria cerámica [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7728Palanci