This thesis presents a review of optimal production scheduling in the steel industry. Steel production encompasses various processes, such as the ‘’Blast Furnace’’ and ‘’Hot-Rolled Steel Sheets Mill’’. However, this thesis specifically focuses on the steel making process due to its intermediate nature and substantial influence on profits and costs. \\\\This paper presents a MILP scheduling method to tackle practical steel scheduling problems. The scheduling of steel process poses a significant challenge due to complicated constraints and machine rules, making it a time-consuming task to obtain an optimal solution. To address this problem, a strategy has been proposed to break down the huge and complex problem into smaller sub-problems. The foundational concept behind this approach was initially introduced by Harjunkoski and Grossmann (2001). However, further improvements are proposed in this thesis by introducing a more flexible model for process and grading selections, tailored to practical steel scheduling problems. The thesis presents a strategy to obtain optimal steel making process scheduling by using a MILP approach. In addition, this thesis shows an optimal steel making process scheduling under processing time uncertainty. Uncertain processing time can have great impact the schedule accuracy. To tackle with this problem, a stochastic scheduling model is represented. Moreover, this thesis illustrates an improvement to generate a practical scheduling of steel making process by making use of real processing time data. To validate the effectiveness of our proposed methods, we provide a small example for each step of the scheduling process. The results demonstrate that our approach yields reasonable scheduling solutions. / Thesis / Master of Applied Science (MASc) / In this work, we propose a decomposition strategy for solving practical complex scheduling problems in the steel-making process within a sufficiently short computation time. While there are various processes involved, such as the Cold-Rolled Steel Sheets mill and Steel Pipe mill, we focus on the steel-making process. The optimal scheduling of this process is crucial for increasing profits, reducing waste, and minimizing costs. However, scheduling optimization for the steel-making part presents significant challenges due to complex constraints and specific process rules. To address these challenges, we suggest a decomposition strategy in Chapter 3 of this thesis. This strategy primarily involves breaking down the large and complex scheduling problems into smaller subproblems. While a basic solution strategy is provided in the work of Harjunkoski and Grossmann (2001), our research introduces several improvements tailored to practical scheduling problems. For example, the original paper suggests grouping products together only if they have the same grade. In practical scheduling, however, it is often necessary to mix products of different grades within the same group to maximize productivity and operate efficiently. Additionally, the original paper considers only a single machine for each downstream process. In reality, there are often multiple machines involved in each downstream process. Therefore, our research addresses this challenge by incorporating two refining machines and two continuous casting processes into the scheduling formulations for the downstream process. As a result, the suggestions presented in this paper contribute to handling more flexible patterns of scheduling problems. In Chapter 3, the formulation is based on the aforementioned idea, and its validation is confirmed through a case study. While the obtained scheduling results may not be optimal, they are reasonable for each step when compared to the perspective of an experienced person. Furthermore, the computational time required for each step is less than 1 minute. As a result, the proposed scheduling strategy can effectively solve practical scheduling problems within a limited time frame. The strategy is specifically designed to incorporate mixed grade grouping, as well as multiple and flexible structures for downstream processes. In addition, in the steel industry, fluctuation in process time is inevitable because of the high temperature and high-speed conditions to produce products. To address these problems, in Chapter 4, we propose a strategy to incorporate processing time uncertainty into a decomposition strategy. The strategy is based on the two-stage stochastic scheduling formulation. In practical steel industries, there are many preparations before producing the products such as setting a specific condition and maintaining the facilities, and the preparations are based on the scheduling product's order. Therefore, in this formulation, the variables defining the product's order are regarded as the first decision variables to reflect a practical scheduling problem. The formulation is based on this concept, and its validation is confirmed through application to a practical case study. The results are reasonable by comparing to the knowledge of an experienced person. Furthermore, the computational time required for this strategy is also less than 1 minute. Therefore, the strategies presented in this thesis offer an efficient approach for addressing practical steel-making scheduling problems.

Failure of pipes in water distribution systems is a common occurrence especially in large cities. The failure of a pipe results in: loss of water; property damage; interruption of service; decreased system performance; and the financial cost of restoring the failed pipe. The cost of replacement and rehabilitation in the United States is estimated at 23 plus billion dollars. It is virtually impossible to replace all vulnerable pipes at the same time. As a result, there is a need for methods that can help in progressive system rehabilitation and replacement subject to budgetary constraints. If delaying is considered a good strategy due to the time value of money then, the timing of preventive maintenance becomes a crucial element for system maintenance and operation. The central under pinning element in the decision process for scheduling preventive maintenance is the deteriorating nature of a pipe under a given surrounding. By planning to replace pipes before they fail, proper planning can be put in place for securing of finances and labor force needed to rehabilitate the pipes. With this approach, service interruptions are minimized as the loss of service time is limited to the time used in replacing the pipe. In this research, a mechanistic model for assessing the stage of deterioration of an underground pipe is developed. The developed model consists of three sub-models namely, the Pipe Load Model (PLM), the Pipe Deterioration Model (PDM), and the Pipe Break Model (PBM). The PLM simulates the loads and stresses exerted on a buried water main. These loads include the earth load, traffic load, internal pressure, expansive soil loads, thermal, and frost loads. The PDM simulates the deterioration of the pipe due to corrosion resulting from the physical characteristics of the pipe environment. The pipe deterioration effect is modeled in two stages. First, the thinning of the pipe wall is modeled using a corrosion model. Second, the localized pit growth is used to determine the residual strength of the pipe based on the fracture toughness and the initial design strength of the pipe. The PBM assesses the vulnerability of a pipe at any time in terms of a critical safety factor. The safety factor is defined as the ratio of residual strength to applied stress. For a conservative estimate the multiplier effect due to thermal and frost loads are considered. For a chosen analysis period, say 50 years, the pipes with safety factors less than the critical safety factor are selected and ordered by their rank. Aided by the prioritized list of failure prone pipes, utilities can organize a replacement schedule that minimizes cost over time. Additionally a physically based regression model for determining the optimal replacement time of pipe is also presented. A methodology for assessing the consequences of accelerated and delayed replacement is also provided. The methodologies developed in this dissertation will enable utilities to formulate future budgetary needs compatible with the intended level of service. An application of the model and results are included in the dissertation. / Ph. D.

This work deals with the energy management of belt conveyor systems (BCS) under various demandside management (DSM) programmes. The primary objective of this work is to model the energy consumption and energy related cost of operating troughed belt conveyor systems under different electricity pricing tariffs. This research is motivated by the increasing need for energy efficiency and energy cost reduction in the operation of BCS. This is as a result of technological improvements in BCS technology leading to increasingly longer belts being commissioned and as a result of rapidly rising electricity costs. An energy model derived from established industry standards is proposed for long conveyors. The newly proposed model uses a first-order partial differential equation (PDE) in order to capture the state of material on the belt. This new model describes the conveyor's power requirement using an equation with two parameters. A system identification set-up involving a recursive parameter estimating algorithm is simulated for measurements with varying degrees of noise. The results show that the proposed model estimates conveyor power and material delivered by long conveyors more accurately than the existing steady-state models. Downhill conveyors (DHCs) are important potential energy sources that can be tapped to improve the overall energy efficiency of BCSs. A generic optimisation model that is able to optimally schedule three configurations of BCS with DHC is proposed. The economic assessment of implementing dynamic braking and regenerative drives technology on downhill conveyors is undertaken with the help of the model. The assessment shows that combining regenerative drives and optimal operation of BCS with DHC generates energy savings that give attractive payback period of less than 5 years. A chance-constrained model predictive control (cc-MPC) algorithm is proposed for scheduling belt conveyor systems with uncertain material demand on the output storage. The chance-constraints are based on the modelling of material demand by a sum of known mean demand and, zero-mean and normally distributed random component. The cc-MPC algorithm is shown to produce schedules that give a smaller number and smaller magnitude of storage limit violations compared to normal MPC and chance-constrained optimal control algorithms. An equation that gives the amount of effective storage required to meet storage constraints for a given value of standard deviation is established. The optimal scheduling of BCS under the real-time pricing (RTP) tariff is considered. This study develops a methodology for establishing the economic value of price forecasting schemes for loads capable of load-shifting. This methodology is used to show that the economic benefit obtained from a forecast is highly dependent on the volatility of the electricity prices being predicted and not their mean value. The methodology is also used to illustrate why the commonly used indices mean absolute percentage error (MAPE) and root mean square error (RMSE) are poor indicators of economic benefit. The proposed index using Kendall's rank correlation between the actual and predicted prices is shown to be a good indicator of economic benefit, performing far better than RSME and MAPE. / Thesis (PhD)--University of Pretoria, 2016. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

No description available.

Operation of a pumped storage system is dictated by the time dependent price of electricity and capacity limitations of the generating plants. This thesis considers the optimization of the Smith Mountain Lake-Leesville Pumped Storage-Hydroelectric facility. The constraints include the upper and lower reservoir capacities, downstream channel capacity and flood stage, in-stream flow needs, efficiency and capacity of the generating and pumping units, storage-release relationships, and permissible fluctuation of the upper reservoir water surface elevation to provide a recreational environment for the lake shore property owners. Two formulations are presented: (1) a nonlinear mixed integer program and (2) a discretized linear mixed integer program. These formulations optimize the operating procedure to generate maximum revenue from the facility. Both formulations are general and are applicable to any pumped storage system. The nonlinear program retains the physical aspects of the system as they are but suffers from non-convexity related issues. The linear formulation uses a discretization scheme to approximate the nonlinear efficiency, pump, turbine, spillway discharge, tailrace elevation-discharge, and storage-elevation relationships. Also, there are binary unit dispatch and either/or constraints accommodating spill and gated release. Both formulations are applied to a simplified scheme of the Smith Mountain Lake and Leesville pumped storage system. The simplified scheme uses a reduced number of generating and pumping units at the upper reservoir to accommodate the software limitations. Various sensitivity analyses were performed to test the formulations. The linear formulation consistently performs better than the nonlinear. The nonlinear solution requires a good starting point for optimization. It is most useful as a verification tool for the solution from the linear program on all occasions. The formulations yield the best schedules for generating and pumping. A coarse time interval limits the use of all pumps in the presence of the spill constraint. A sufficiently large difference in the diurnal unit price encourages short-term pump back as opposed to a weekly cycle. The Leesville (downstream) reservoir affects the power production schedule with its large (approx. 9 ft) forebay rise for every foot drop at the Smith Mountain Lake. The linear formulation provides a valuable tool for studying the system under a wide range of conditions without having to worry about the computational difficulties associated with the nonlinear formulation. / Master of Science

This paper presents an investigation into the optimal scheduling of realtime tasks of a multiprocessor system using hybrid genetic algorithms (GAs). A comparative study of heuristic approaches such as `Earliest Deadline First (EDF)¿ and `Shortest Computation Time First (SCTF)¿ and genetic algorithm is explored and demonstrated. The results of the simulation study using MATLAB is presented and discussed. Finally, conclusions are drawn from the results obtained that genetic algorithm can be used for scheduling of real-time tasks to meet deadlines, in turn to obtain high processor utilization.

Au cours des dernières décennies, de grands efforts en recherche et développement ont été faits pour développer et promouvoir les véhicules électriques (VEs). La plupart de ces recherches portent essentiellement sur le développement des moteurs électriques de ces véhicules et des technologies de batteries de recharge. Cependant, un des obstacles majeurs pour le déploiement des VEs à grande échelle réside dans l'incertitude d’assister et de guider les conducteurs de ce type de véhicule d’une façon appropriée pour atteindre les stations de recharge tout en satisfaisant leurs souhaits (points de recharge disponibles, moins d’attente possible, proposition d’autres points d’intérêts : restaurant, shopping, etc.). Afin de remédier à ce manque, nous proposons dans ce travail de thèse une approche distribuée et adaptative orientée modèles pour la gestion de l'énergie pour la recharge des VEs. Pour ce faire, nous nous somme focalisés sur la modélisation des processus de recharge en utilisant une approche formelle basée sur des outils de systèmes à événements discrets, à savoir l'algèbre (max, +) et les réseaux de Petri. Les modèles développés ont permis d’étudier, d’analyser et d’évaluer le comportement du système de recharge. De plus, une approche d'optimisation basée sur la programmation linéaire est proposée afin d’affecter et d’orienter d'une façon optimale les VEs vers les stations de recharge appropriées et ordonnancer leurs opérations de recharge. Afin de prédire le taux et la durée de recharge moyens des VEs compte tenu des dates d’arrivée des demandes de recharge et l'état de recharge de chaque véhicule, une approche dédiée basée sur une fonction prédictive est proposée. En utilisant cette approche, les opérations de recharge pourraient être planifiées en minimisant les temps d'attente des VEs au sein des stations de recharge et en assurant un taux de recharge acceptable pour chaque demande. Les résultats d’analyse et de simulations obtenus ont montré que les approches de modélisation, d’optimisation et de prédiction proposées permettent d’affecter de façon adéquate et optimale les VEs aux stations de recharge tout en satisfaisant toutes les contraintes du processus de recharge. / In the last decades, very great research and development efforts have been made to develop and promote electric vehicles (EVs). Most efforts have been made to further develop the power engine of these vehicles and batteries technologies. However, one of the major obstacles to the large deployment of EVs is the uncertainty of drivers to get a suitable and vacant place at a charging station (CS). In this manuscript, we focus on the charging process modelling using formal approaches based on discrete event system tools namely (max,+) algebra and Petri nets. In addition, an optimization approach based on linear programming is proposed to optimally assign and reroute EVs to the suitable CSs and schedule their charging operations. In order to predict, manage and handle charging needs of EVs, a dedicated model based on a predictive function is introduced. The aim is to predict the average charging rate and time while considering the inter-arrival of charging requests and the state of charging of EVs. Using this approach, charging operations could be planned while minimizing waiting times of EVs and avoiding queuing situations within CSs. Simulation results showed that the proposed approaches allow assigning adequately and optimally EVs to CSs while satisfying all process constraints.

Les systèmes embarqués temps réel critiques intègrent des architectures complexes qui évoluent constamment afin d'intégrer des nouvelles fonctionnalités requises par les utilisateurs finaux des systèmes (automobile, avionique, ferroviaire, etc.). Ces nouvelles architectures ont un impact direct sur la variabilité du comportement temporel des systèmes temps réel. Cette variabilité entraîne un sur-approvisionnement important si la conception du système est uniquement basée sur le raisonnement pire cas. Approches probabilistes proposent des solutions basées sur la probabilité d'occurrence des valeurs les plus défavorables afin d'éviter le sur-approvisionnement, tout en satisfaisant les contraintes temps réel. Les principaux objectifs de ce travail sont de proposer des nouvelles techniques d'analyse des systèmes temps réel probabilistes et des moyens de diminuer la complexité de ces analyses, ainsi que de proposer des algorithmes optimaux d'ordonnancement à priorité fixe pour les systèmes avec des temps d'exécution décrits par des variables aléatoires. Les résultats que nous présentons dans ce travail ont été prouvés surs et à utiliser pour les systèmes temps réel durs, qui sont l'objet principal de notre travail. Notre analyse des systèmes avec plusieurs paramètres probabilistes a été démontrée considérablement moins pessimiste que d'autres types d'analyses. Cet analyse combinée avec des algorithmes d'ordonnancement optimaux appropriées pour les systèmes temps réel probabilistes peut aider les concepteurs de systèmes à mieux apprécier la faisabilité d'un système, en particulier de ceux qui sont jugé irréalisable par des analyses/algorithmes d'ordonnancement déterministes / Critical real-time embedded systems integrate complex architectures that evolve constantly in order to provide new functionality required by the end users of the systems (automotive, avionics, railway, etc). These new architectures have a direct impact on the variability of the timing behavior of the real-time system. This variability leads to important over-provisioning if the design of the system is based only on worst case reasoning. Probabilistic approaches propose solutions are based on the probability of occurrence of the worst case values in order to avoid over provisioning while satisfying real-time constraints. The main objectives of this work are new analysis techniques for probabilistic real-time systems and ways of decreasing the complexity of these analyses, as well as to propose optimal fixed priority scheduling algorithms for systems that have variability at the level of execution times. The results that we provide in this work have been proved applicable to hard real-time systems, which are the main focus of our work. Our proposed analysis for systems with multiple probabilistic parameters has been shown to greatly decrease the pessimism introduced by other types of analyses. This type of analysis combined with the proper optimal scheduling algorithms for probabilistic real-time system help the system designers to better appreciate the feasibility of a system, especially of those that are deemed unfeasible by deterministic analyses/scheduling algorithms

Scheduling has always been an indispensable part of resource allocation in wireless networks. Accurate information about channel-state is assumed as a modeling simplification. However, in a real-life network ,e.g., Long Term Evolution(LTE) or IEEE 802.16e WiMAX, the channel-state information feedback to the transmitter can have uncertainty. The primary reason being that although resource allocation is done at the finer granularity of a Physical Resource Block (PRB), channel-state information is still feedback at the coarser granularity of a sub band, which is a group of PRBs. This is done to reduce the feedback traffic from the users to the Base Station. However, this averaging causes information loss and hence, the resulting uncertainty at the scheduler. Moreover, uncertainty might be present in the channel-estimates because of the very process of estimation. In the first part of the thesis, we model the channel-estimate in accuracy and characterize the network stability region. Compared to earlier works, we allow the channel estimates to have dependence among themselves, which is a more realistic situation in a modern LTE or WiMax network. We then propose two simple Max Weight based scheduling schemes that achieve any rate in the interior of the stability region. We also derive an asymptotically tight upper bound on the mean queueing delay in our system under one of the throughput-optimal policies we propose. The above policies ensure stability of the network and we have also obtained bounds on the mean queueing delays. However, different applications may require certain quality of service which may not be satisfied by these policies. Thus, we also propose a throughput-optimal policy for the network under traffic with heterogeneous QoS constraints and present some numerical results studying its performance. In the second part of the thesis, we study the problem of energy-efficient scheduling under average delay constraint. For wireless access technologies, the largest power consumer is the Base Station(BS). Any reduction in the power consumption in a BS will reduce carbon footprint from the Information and Communication Technology sector. We concentrate on the problem of minimizing the total non-renewable power consumed in a Green BS, that is powered by renewable energy sources ,e.g., solar/wind energy and may also be connected to the power grid or diesel generators. Specifically, we consider the problem of minimizing the average grid power consumption of a Green BS downlink in scheduling multiple users with average delay constraints. We have a packetized model for the data packets (i.e., the packets cannot be fragmented) which is a more realistic model for packet-switched networks. The power function is a non-decreasing convex function of the queue-lengths and only one user is allowed to transmit in a slot. We prove the existence of a power optimal policy under delay constraints for multiple users. We analyse the problem and provide some structural results for the optimal policy.

Tesis por compendio / [ES] Los sistemas de tiempo real se caracterizan por exigir el cumplimento de unos requisitos temporales que garanticen el funcionamiento aceptable de un sistema. Especialmente, en los sistemas de tiempo real estricto estos requisitos temporales deben ser inviolables. Estos sistemas suelen aplicarse en áreas como la aviación, la seguridad ferroviaria, satélites y control de procesos, entre otros. Por tanto, el incumplimiento de un requisito temporal en un sistema de tiempo real estricto puede ocasionar un fallo catastrófico. La planificación de sistemas de tiempo real es una área en la que se estudian y aplican diversas metodologías, heurísticas y algoritmos que intentan asignar el recurso de la CPU sin pérdidas de plazo. El uso de sistemas de computación multinúcleo es una opción cada vez más recurrente en los sistemas de tiempo real estrictos. Esto se debe, entre otras causas, a su alto rendimiento a nivel de computación gracias a su capacidad de ejecutar varios procesos en paralelo. Por otro lado, los sistemas multinúcleo presentan un nuevo problema, la contención que ocurre debido a la compartición de los recursos de hardware. El origen de esta contención es la interferencia que en ocasiones ocurre entre tareas asignadas en distintos núcleos que pretenden acceder al mismo recurso compartido simultáneamente, típicamente acceso a memoria compartida. Esta interferencia añadida puede suponer un incumplimiento de los requisitos temporales, y por tanto, la planificación no sería viable. En este trabajo se proponen nuevas metodologías y estrategias de planificación no convencionales para aportar soluciones al problema de la interferencia en sistemas multinúcleo. Estas metodologías y estrategias abarcan algoritmos de planificación, algoritmos de asignación de tareas a núcleos, modelos temporales y análisis de planificabilidad. El resultado del trabajo realizado se ha publicado en diversos artículos en revistas del área. En ellos se presentan estas nuevas propuestas que afrontan los retos de la planificación de tareas. En la mayoría de los artículos presentados la estructura es similar: se introduce el contexto en el que nos situamos, se plantea la problemática existente, se expone una propuesta para solventar o mejorar los resultados de la planificación, después se realiza una experimentación para evaluar de forma práctica la metodología propuesta, se analizan los resultados obtenidos y finalmente se exponen unas conclusiones sobre la propuesta. Los resultados de las metodologías no convencionales propuestas en los artículos que conforman esta tesis muestran una mejora del rendimiento de las planificaciones en comparación con algoritmos clásicos del área. Especialmente la mejora se produce en términos de disminución de la interferencia producida y mejora de la tasa de planificabilidad. / [CA] Els sistemes de temps real es caracteritzen per exigir el compliment d'uns requisits temporals que garantisquen el funcionament acceptable d'un sistema. Especialment, en els sistemes de temps real estricte aquests requisits temporals han de ser inviolables. Aquests sistemes solen aplicar-se en àrees com l'aviació, la seguretat ferroviària, satèl·lits i control de processos, entre altres. Per tant, l'incompliment d'un requisit temporal en un sistema de temps real estricte pot ocasionar un error catastròfic. La planificació de sistemes de temps real és una àrea en la qual s'estudien i apliquen diverses metodologies, heurístiques i algorismes que intenten assignar el recurs de la CPU sense pèrdues de termini. L'ús de sistemes de computació multinucli és una opció cada vegada més recurrent en els sistemes de temps real estrictes. Això es deu, entre altres causes, al seu alt rendiment a nivell de computació gràcies a la seua capacitat d'executar diversos processos en paral·lel. D'altra banda, els sistemes multinucli presenten un nou problema, la contenció que ocorre a causa de la compartició dels recursos de hardware. L'origen d'aquesta contenció és la interferència que a vegades ocorre entre tasques assignades en diferents nuclis que pretenen accedir al mateix recurs compartit simultàniament, típicament accés a memòria compartida. Aquesta interferència afegida pot suposar un incompliment dels requisits temporals, i per tant, la planificació no seria viable. En aquest treball es proposen noves metodologies i estratègies de planificació no convencionals per aportar solucions al problema de la interferència en sistemes multinucli. Aquestes metodologies i estratègies comprenen algorismes de planificació, algorismes d'assignació de tasques a nuclis, models temporals i anàlisis de planificabilitat. El resultat del treball realitzat s'ha publicat en diversos articles en revistes de l'àrea. En ells es presenten aquestes noves propostes que afronten els reptes de la planificació de tasques. En la majoria dels articles presentats l'estructura és similar: s'introdueix el context en el qual ens situem, es planteja la problemàtica existent, s'exposa una proposta per a solucionar o millorar els resultats de la planificació, després es realitza una experimentació per a avaluar de manera pràctica la metodologia proposada, s'analitzen els resultats obtinguts i finalment s'exposen unes conclusions sobre la proposta. Els resultats de les metodologies no convencionals proposades en els articles que conformen aquesta tesi mostren una millora del rendiment de les planificacions en comparació amb algorismes clàssics de l'àrea. Especialment, la millora es produeix en termes de disminució de la interferència produïda i millora de la taxa de planificabilitat. / [EN] Real-time systems are characterised by the demand for temporal constraints that guarantee acceptable operation and feasibility of a system. Especially, in hard real-time systems these temporal constraints must be respected. These systems are typically applied in areas such as aviation, railway safety, satellites and process control, among others. Therefore, a missed deadline in a hard-real time system can lead to a catastrophic failure. The scheduling of real-time systems is an area where various methodologies, heuristics and algorithms are studied and applied in an attempt to allocate the CPU resources without missing any deadline. The use of multicore computing systems is an increasingly recurrent option in hard real-time systems. This is due, among other reasons, to its high computational performance thanks to the ability to run multiple processes in parallel. On the other hand, multicore systems present a new problem, the contention that occurs due to the sharing of hardware resources. The source of this contention is the interference that sometimes happens between tasks allocated in different cores that try to access the same shared resource simultaneously, typically shared memory access. This added interference can lead to miss a deadline, and therefore, the scheduling would not be feasible. This paper proposes new non-conventional scheduling methodologies and strategies to provide solutions to the interference problem in multicore systems. These methodologies and strategies include scheduling algorithms, task allocation algorithms, temporal models and schedulability analysis. The results of this work have been published in several journal articles in the field. In these articles the new proposals are presented, they face the challenges of task scheduling. In the majority of these articles the structure is similar: the context is introduced, the existing problem is identified, a proposal to solve or improve the results of the scheduling is presented, then the proposed methodology is experimented in order to evaluate it in practical terms, the results obtained are analysed and finally conclusions about the proposal are expressed. The results of the non-conventional methodologies proposed in the articles that comprise this thesis show an improvement in the performance of the scheduling compared to classical algorithms in the area. In particular, the improvement is produced in terms of reducing the interference and a higher schedulability rate. / Esta tesis se ha realizado en el marco de dos proyectos de investigación de carácter nacional. Uno de ellos es el proyecto es PRECON-I4. Consiste en la búsqueda de sistemas informáticos predecibles y confiables para la industria 4.0. El otro proyecto es PRESECREL, que consiste en la búsqueda de modelos y plataformas para sistemas informáticos industriales predecibles, seguros y confiables. Tanto PRECON-I4 como PRESECREL son proyectos coordinados financiados por el Ministerio de Ciencia, Innovación y Universidades y los fondos FEDER (AEI/FEDER, UE). En ambos proyectos participa la Universidad Politécnica de Valencia, la Universidad de Cantabria y la Universidad Politécnica de Madrid. Además, en PRESECREL también participa IKERLAN S. COOP I.P. Además, parte de los resultados de esta tesis también han servido para validar la asignación de recursos temporales en sistemas críticos en el marco del proyecto METROPOLIS (PLEC2021-007609). / Aceituno Peinado, JM. (2024). Investigación de nuevas metodologías para la planificación de sistemas de tiempo real multinúcleo mediante técnicas no convencionales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203212 / Compendio