Ordonnancement sous contraintes d'énergie et de ressources humaines

Ce travail s’intéresse à l’ordonnancement dans un atelier de fonderie. Il s’inspire du cas d’une usine de fabrication de tubes. La spécificité de cet exemple vient d’un objectif et de contraintes liés à la consommation d’électricité, de la variabilité des durées opératoires en fonction de la puissance allouée aux fours, ainsi que de la présence d’opérateurs en nombre limité. L’objectif est de minimiser la facture énergétique. Un premier modèle, basé sur une discrétisation fine, a été exposé dans Trépanier et al (2005), suite à l’étude d’une politique de délestage. Nous présentons ici un nouveau modèle en temps continu

Closed-loop integration of planning, scheduling and multi-parametric nonlinear control

In this article, motivated by the need for efficient closed-loop implementation of the control objectives set within the integrated planning, scheduling and control (iPSC) problem we introduce a novel framework that enables its online solution under dynamic disturbances. We introduce the concept of multi-setpoint explicit controllers through the use of a new multi-parametric nonlinear programming algorithm and develop a rigorous rescheduling mechanism that mitigates the impact of the dynamic disruptions on the operational decisions of planning and scheduling. The overall closed-loop problem is formulated as mixed integer linear program with the control problem integrated via an outer loop. The benefits of the proposed framework are highlighted through two case studies and the results indicate the necessity of considering dynamic disruptions within the scope of the integrated problem

Discrete Path Planing Strategies for Coverage and Multi-Robot Rendezvous

This thesis addresses the problem of motion planning for autonomous robots, given a map and an estimate of the robot pose within it. The motion planning problem for a mobile robot can be defined as computing a trajectory in an environment from one pose to another while avoiding obstacles and optimizing some objective such as path length or travel time, subject to constraints like vehicle dynamics limitations. More complex planning problems such as multi-robot planning or complete coverage of an area can also be defined within a similar optimization structure. The computational complexity of path planning presents a considerable challenge for real-time execution with limited resources and various methods of simplifying the problem formulation by discretizing the solution space are grouped under the class of discrete planning methods. The approach suggests representing the environment as a roadmap graph and formulating shortest path problems to compute optimal robot trajectories on it. This thesis presents two main contributions under the framework of discrete planning. The first contribution addresses complete coverage of an unknown environment by a single omnidirectional ground rover. The 2D occupancy grid map of the environment is first converted into a polygonal representation and decomposed into a set of convex sectors. Second, a coverage path is computed through the sectors using a hierarchical inter-sector and intra-sector optimization structure. It should be noted that both convex decomposition and optimal sector ordering are known NP-hard problems, which are solved using a greedy cut approximation algorithm and Travelling Salesman Problem (TSP) heuristics, respectively. The second contribution presents multi-robot path-planning strategies for recharging autonomous robots performing a persistent task. The work considers the case of surveillance missions performed by a team of Unmanned Aerial Vehicles (UAVs). The goal is to plan minimum cost paths for a separate team of dedicated charging robots such that they rendezvous with and recharge all the UAVs as needed. To this end, planar UAV trajectories are discretized into sets of charging locations and a partitioned directed acyclic graph subject to timing constraints is defined over them. Solutions consist of paths through the graph for each of the charging robots. The rendezvous planning problem for a single recharge cycle is formulated as a Mixed Integer Linear Program (MILP), and an algorithmic approach, using a transformation to the TSP, is presented as a scalable heuristic alternative to the MILP. The solution is then extended to longer planning horizons using both a receding horizon and an optimal fixed horizon strategy. Simulation results are presented for both contributions, which demonstrate solution quality and performance of the presented algorithms

Methods and algorithms for integrated multi-scale optimisation of production planning and scheduling

Imperial Users onl

Investigating Different Types of Variability in Food Production System

A high level of competition in the food industry, specifically in the Middle East and the UK has forced companies to improve their processes by reducing lead time, waste, and costs and increasing production efficiency. The main challenge to the achievement of the process improvement objectives is the high level of process variability. Therefore, this research investigates the different types of variability in food production system and proposes a methodology to reduce the effect variability in food production system. The variability can be caused by several factors, for instance, in biscuit production lines variability can be induced due to short breakdown and long breakdown, variable processing times, variable temperature, etc. The proposed approach addresses process time variability issues associated with both make-to-stock (MTS) and make-to-order (MTO) manufacturing environments using an iterated approach. The proposed methodology integrates process mapping, (which is a lean tool for identifying value added and non-value added activities), discrete event simulation (to mirror the real production line), Taguchi orthogonal arrays (to generate different scenarios in order to investigate the effect of variability on the simulation model), correlation analysis (to identify the highest variability factors), and the rule based system (to improve food production system performance based on identified key performance indicators (KPIs)). The research uses a biscuit production line as a case study to validate the proposed methodology. The application of the proposed approach determines that the highest effected KPI is %working. The results showed that after implementation of the rule-based system, key performance improved in high variable areas. Results analysis based on before scenario shows that %working performance indicator is highly effected by variable temperature, speed, and breakdown factors for high variable areas such as baking, cooling, aligning, and packing. Based on identified factors and high variable areas, rules are developed by applying standardisation setting (SOP, WI, PP) in high variable areas and the results shows %working improved in baking by 4.78%, in cooling by 16.06%, in aligning by 0.35%, in packing machine1 by 2.5%, in packing machine2 by 2.37%, in packaging1 by 3.35%, and in packaging2 by 3.16%. The integrated method allow quick response , control the environment without production interruption, reduce number of experiments , and reducing variability in high variable areas, which narrowed the improvement in the required areas and increased its effectiveness

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

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

Scheduling of crude oil and product blending and distribution operations in a refinery

Ph.DDOCTOR OF PHILOSOPH

Design for Flexibility in the Forest Biorefinery Supply Chain

Le climat d’affaires de industrie papetière nord américaine et européenne change présentement. La baisse de la demande, la volatilité des prix, l’augmentation de la compétition pour l’accès aux matières premières et le contrôle du marché, ainsi que des couts énergétiques passablement élevés poussent les entreprises forestières à rechercher de nouveaux modèles d’affaires afin d’être plus compétitives sur le long terme. Une des alternatives pour ces entreprises est de se tourner vers le secteur émergent de la bioéconomie et du bioraffinage. Possédant déjà un système d’utilité, un réseau d’approvisionnement de matières premières, un réseau de distribution de produits ainsi qu’un savoir-faire technique ouvrant la porte à de nombreuses possibilités d’intégration massique et énergétique, l’industrie forestière possède plusieurs avantages compétitifs pouvant améliorer la performance économique de l’implantation du bioraffinage. Plusieurs stratégies différentes peuvent être adoptées pour implanter des activités de bioraffinage au sein d’une entreprise. Par contre, en raison des risques technologiques et des risques de marché associés aux nouveaux procédés et produits, et le manque en capital des entreprises forestières, l’implantation du bioraffinage devrait être effectuée par phase. Des outils d’analyse appropriés sont toutefois requis afin d’identifier les stratégies possibles et les phases d’implantation. Puisque la chaine logistique (SC) d’une entreprise est critique pour la compétitivité à long terme des bioraffineries, un outil d’analyse de la SC peut donc jouer un rôle clé pour une transformation d’entreprise réussie. Une analyse de la SC calcule le bénéfice pour l’ensemble de la chaine logistique et prend en compte les différents contributeurs de couts qui sont typiquement ignorés dans les analyses économiques, tel que les couts d’inventaire, de transition, etc. Elle peut aussi être utilisée pour prendre en considération la volatilité du marché, et détermine comment la flexibilité inhérente d’un système de production peut être exploitée pour atténuer les risques et maximiser le profit. À cet effet, une analyse de la SC peut aussi être utilisée pour cibler le niveau de flexibilité souhaité d’un système afin d’atténuer les risques de volatilité du marché. De plus, cette analyse offre une meilleure compréhension des couts et de la rentabilité d’une stratégie d’implantation donnée. Ainsi, une analyse de la SC peut être utilisée à deux fins différentes : v • Pour la prise de décision au niveau de conception, et plus précisément, pour cibler le niveau de flexibilité d’un procédé de fabrication, • Pour comparer différentes stratégies pouvant être poursuivies par une entreprise, en évaluant leur performance selon différentes conditions de marché. L’objectif de cette recherche est d’illustrer une telle méthodologie de conception, soit une méthodologie qui cible un niveau de flexibilité manufacturière préférable à avoir, qui aide à concevoir le réseau de la SC, et qui permet d’évaluer différentes stratégies de bioraffinage pour transformer une entreprise forestière. Cette méthodologie est démontrée en utilisant une étude de cas qui inclut deux options de produits/procédé, dont des procédés thermochimiques et biochimiques, et plusieurs stratégies d’implantation à implanter au fil du temps. Le point d’ancrage de cette méthodologie est basé sur les principes de gestion de la chaine logistique centrée sur les marges. Plutôt que d’appliquer une approche traditionnelle centrée sur la production, où la gestion de la capacité des équipements et la minimisation des couts de production prime, une approche centrée sur les marges vise plutôt à maximiser le profit. Pour ce faire, tous les couts encourus au long de la SC doivent être considérés de façon intégrée. De même, le potentiel de flexibilité au sein de la SC, particulièrement au niveau de la production, doit être exploité pour maximiser le profit. Une formulation mathématique d’optimisation est développée pour représenter une telle mentalité. Selon cette dernière, une méthodologie de conception est proposée afin d’aider le processus de prise de décision stratégique reliée au design de la chaine logistique du bioraffinage. Cette méthodologie est alimentée par d’autres méthodologies qui identifient un ensemble d’options de procédés/produits prometteurs. Elle comprend quatre étapes principales : 1. La définition des alternatives de procédés représentant différents potentiels de flexibilité, 2. La définition d’options de réseau de SC, en tenant compte des caractéristiques des alternatives de procédés, de même que les politiques, les forces et les faiblesses de l’entreprise étudiant ces alternatives procédés/produits, 3. Le ciblage d’un degré de flexibilité manufacturière et d’un réseau de SC associé, 4. L’analyse de stratégies d’implantation des alternatives procédés/produits retenues vi Un ensemble d’indicateurs de performance représentant la rentabilité de la SC, la robustesse et la flexibilité des différentes options de bioraffinage est utilisé pour évaluer la performance de stratégies de bioraffinage selon différents scénarios de marchés. Les résultats montrent que lorsque la flexibilité d’un système est améliorée, le profit augmente. Cependant, cela ne mène pas nécessairement à une amélioration de la rentabilité. Pour que la rentabilité d’un système flexible augmente, les investissements supplémentaires déboursés pour augmenter le degré de flexibilité doivent être compensés par une amélioration au niveau des profits. Ainsi, pour certains cas, la rentabilité augmente avec la flexibilité du procédé, et dans certains cas non. De plus, la robustesse d’une option est directement liée à sa flexibilité. Plus le degré de flexibilité augmente, plus le système devient robuste envers la volatilité du marché. De même, les résultats montrent l’importance de l’analyse de la SC lors de la prise de décision reliée à la conception. Ils illustrent le fait qu’un changement dans le degré de flexibilité manufacturière d’un procédé affecte directement les opportunités de l’entreprise. Ainsi, des stratégies de marché et des degrés de flexibilité différents impliquent une configuration de réseau de SC et une stratégie de gestion spécifiques. Il devrait donc y avoir une intégration entre la conception de procédés et la conception du réseau de la SC. Il est aussi montré que les produits chimiques à valeur ajoutée sont prometteurs pour le succès futur du bioraffinage. Les options de procédés fabriquant ces derniers obtiennent une rentabilité en termes de taux de retour interne considérablement plus élevée que les options fabriquant des produits de commodités.---------- The pulp and paper industry business environment in North-America and Europe is changing. Declining and volatile product price and demand, increased competition for feedstock and market share, growing competition from global low-cost producers and considerably high energy cost are driving companies to seek alternative business models to be competitive over the longer term. One alternative is to enter the bio-energy and biorefinery sectors that have been emerging in recent years. Having the required utility systems in place and the engineering know-how, existing feedstock supply chain networks and product delivery systems as well as the potential for mass and/or energy integration between existing processes and new processes imply competitive advantages for the forestry companies to improve their economic performance via implementing biorefinery. Many different strategies can be pursued for implementing the biorefinery. Due to a lack of capital for implementing such strategies, technological risks and product market immaturities, the implementation should be executed in a phase-wise manner. Proper analysis tools are required to identify feasible strategies and their implementation phases. The design and management of supply chain (SC) is critical for the long-term competitive advantage of companies who would like to implement the biorefinery. In this regard, SC analysis can be used to evaluate the potential SC performance of different biorefinery strategies. It calculates the profit across the entire SC and accounts for cost contributors that are typically ignored in economic analyses, e.g. inventory cost, changeover cost, etc. It can also be used to take into consideration market volatility, and determine how the flexibility of the manufacturing system can be exploited to mitigate market risks in order to maximize profit. In this way, SC analysis can be used to target the desired level of flexibility of a manufacturing system needed to mitigate the impact of market price volatility. Moreover, these capabilities provide better insight into the costs and profit incurred by an implemented strategy. Thus, an SC analysis can be used for two different purposes: • For making design decisions, and more specifically, for targeting the level of flexibility of a system and designing the SC network configuration • For comparing several strategies by evaluating their performance for different market conditions viii The objective of this thesis is to develop a design methodology for targeting the required level of flexibility, designing the SC network configuration, and evaluating different FBR strategies for transforming a forest company. The methodology is demonstrated using a case study that involves two product/process options, including thermochemical and biochemical processes, with several implementation strategies, implemented over the years. The pivot of this methodology is the margins-based thinking used as an operating policy. It is discussed that, instead of applying the traditional manufacturing-centric approach in production which focuses on capacity management and tries to minimize the costs, the margins-based policy must be implemented, which has the following specifications: • It maximizes the profit instead of minimizing costs • It considers all costs incurred by SC activities in an integrated manner and doesn’t only focus on production cost • It exploits the potential for flexibility in the SC, especially in production, to maximize profit A SC optimization formulation is developed to represent such thinking. Using this formulation, a design methodology is proposed for making strategic decisions related to biorefinery SC design. This methodology is fed by separate methodologies which identify the most promising set of product to produce and technologies to employ. Given that, the methodology involves four major steps: • Defining process alternatives representing different potentials for flexibility • Defining SC network alternatives based on the defined process alternatives as well as the policies, advantages and restrictions of the company • Targeting the level of flexibility of processes and determining its associated SC network • Analyzing different implementation strategies for the proposed product/processes with their targeted level of flexibility and defined SC network A set of performance metrics that represents SC profitability, robustness and flexibility is used to evaluate the performance of biorefinery strategies for several market scenarios. The results show that when the flexibility of a system is enhanced, its profit increases. But this does not necessarily end in profitability improvement. For the profitability of a flexible system to ix improve, the extra capital cost paid for increasing the level of flexibility must be compensated by the profit improvement. Thus, for some cases profitability increases with flexibility and for some cases it does not. Moreover, robustness has a direct relationship with flexibility. As flexibility increases, the system becomes more robust against market volatility. The results reveal the importance of SC analysis in making design decisions. They illustrate that changes in the level of flexibility will directly affect the company’s opportunities and strategies in the market, and thus, each level of flexibility implies a specific SC network configuration and management strategy. Therefore, there must be integration between process design and SC network design. It is also shown that added-value chemicals are promising for the long-term success of biorefineries. Their profitability, in terms of internal rate of return (IRR), is considerably higher than that of commodities