Esta tese aborda um problema de dimensionamento e sequenciamento de lotes de produção baseado em uma indústria alimentícia brasileira que opera por meio de diversas linhas de produção heterogêneas. Nesse ambiente produtivo, as linhas de produção compartilham recursos escassos, tais como, trabalhadores e máquinas e devem ser montadas (ativadas) em cada período produtivo, respeitando-se a capacidade disponível de cada recurso necessário para ativação das mesmas. Modelos de programação matemática inteira mista são propostos para representação do problema, bem como diversos métodos heurísticos de solução, compreendendo procedimentos construtivos e de melhoramento baseados na formulação matemática do problema e heurísticas lagrangianas. São propostas heurísticas do tipo relax-and-fix explorando diversas partições das variáveis binárias dos modelos e uma heurística baseada na decomposição do modelo para construção de soluções. Procedimentos do tipo fix-and-optimize e matheuristics do tipo iterative MIP-based neighbourhood search são propostas para o melhoramento das soluções iniciais obtidas pelos procedimentos construtivos. Testes computacionais são realizados com instâncias geradas aleatoriamente e mostram que os métodos propostos são capazes de oferecer melhores soluções do que o algoritmo Branch-and-Cut de um resolvedor comercial para instâncias de médio e grande porte. / This doctoral dissertation addresses the simultaneous lot sizing and scheduling problem in a real world production environment where production lines share scarce production resources. Due to the lack of resources, the production lines cannot operate all simultaneously and they need to be assembled in each period respecting the capacity constraints of the resources. This dissertation presents mixed integer programming models to deal with the problem as well as various heuristic approaches: constructive and improvement procedures based on the mathematical formulation of the problem and lagrangian heuristics. Relax-and-fix heuristics exploring some partitions of the set of binary variables of a model and a decomposition based heuristic are proposed to construct solutions. Fix-and-optimize heuristics and iterative MIP-based neighbourhood search matheuristics are proposed to improvement solutions obtained by constructive procedures. Computational tests are performed with randomly instances and show that the proposed methods can find better solutions than the Branch-and-Cut algorithm of a commercial solver for medium and large size instances.

O problema de corte consiste no corte de objetos maiores para produção de peças menores, de modo que uma certa função objetivo seja otimizada, por exemplo, a perda seja minimizada. O problema de empacotamento pode também ser visto como um problema de corte, onde as peças menores são arranjadas dentro dos objetos. Uma abordagem em grafo E/OU para a resolução de problemas de corte e empacotamento foi proposta inicialmente por Morabito (1989) para problemas de corte bidimensionais e, mais tarde, estendida para problemas tridimensionais (Morabito, 1992). Nesta abordagem foi utilizada uma técnica de busca híbrida, onde se combinou a busca em profundidade primeiro com limite de profundidade e a busca hill-climbing, utilizando-se heurísticas baseadas nos limitantes superiores e inferiores. Experiências computacionais mostraram a viabilidade de uso na prática desta abordagem. Mais tarde, Arenales (1993) generalizou esta a abordagem em grafo E/OU mostrando como diferentes problemas de corte poderiam ser resolvidos, independentemente da dimensão, formas dos objetos e itens, baseado em simples hipóteses, sem realizar, entretanto, estudos computacionais. O presente trabalho tem por objetivo estender a abordagem em grafo E/OU para tratar outros casos não analisados pelos trabalhos anteriores, tais como situações envolvendo diferentes processos de corte, bem como a implementação computacional de métodos baseados na abordagem em grafo E/OU, mostrando, assim, a versatilidade da abordagem para tratar diversas situações práticas de problemas de corte e sua viabilidade computacional. / The cutting problem consists of cutting larger objects in order to produce smaller pieces, in such a way as to optimizing a given objective function, for example, minimizing the waste. The packing problem can also be seen as a cutting problem, where the position that each smaller piece is arranged inside of the objects can be seen as the place it was cut from. An AND/OR-graph approach to solve cutting and packing problems was initially proposed by Morabito (1989) for two-dimensional cutting problem and, later, extended to threedimensional problems (Morabito, 1992). That approach uses a hybrid search, which combines depth-first search under depth bound and hill-climbing strategy. Heuristics were devised based on upper and lower bounds. Computational experiences demonstrated its practical feasibility. The AND/OR-graph approach was later generalized by Arenales (1993) based on simple hypothesis. He showed that different cutting problems Gould be solved using the AND/ORgraph approach, independently of the dimension and shapes. The main objective of this thesis is the practical extension of the AND/OR-graph approach to handle other cases not considered by previous works. It was considered different cutting processes, as well as the analysis of computational implementation, showing how can it be adapted to many classes of practical cutting and packing problems.

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2015-07-17T16:00:19Z No. of bitstreams: 1 Stanley Jefferson de Araujo Lima.pdf: 1500605 bytes, checksum: 2aec7d5c11c9781ce7f70eb2019c01f4 (MD5) / Made available in DSpace on 2015-07-17T16:00:19Z (GMT). No. of bitstreams: 1 Stanley Jefferson de Araujo Lima.pdf: 1500605 bytes, checksum: 2aec7d5c11c9781ce7f70eb2019c01f4 (MD5) Previous issue date: 2015-01-27 / In recent years, the Vehicle Routing Problem (VRP) has attracted an increasing attention from researchers due to the great difficulty of its solution and its presence in various practical situations. As consequence, there has been great effort to develop more robust, agile and flexible algorithms that can be modeled according to the scenario that describes the problem. The Capacitated Vehicle Routing Problem (CVRP) is a version of VRP and consists in determining a set of routes to be followed by a fleet of homogeneous vehicles, which must serve a set of customers. The objective is to minimize the total cost of the routes subject to the following restrictions: i) routes must start and end in the same distribution center; ii) each customer must be visited once and its demand must be met in full by only one vehicle and iii) the sum of customers' demands included in a route cannot exceed the vehicle capacity. The CVRP belongs to the class of NP-hard problems, that is, problems whose the solution usually requires non-polynomial complexity time algorithms and because of this are usually resolved with the use of heuristic and metaheuristics algorithms. In this work, it was investigated the optimization of CVRP using Genetic Algorithm (GA) with alternative chromosome representations and heuristics. To this end, three strategies, each one employing a different model of chromosome representation for encoding solution in AG were proposed. In addition, the heuristics of Gillett and Miller to generate solutions that are included in the initial population of GA and Hill-climbing for refinement of GA solutions, after a number of generations without improvement, were adopted. In the performed experiments, the results obtained by the proposed strategies were compared with each other and also with the best results found in the literature for a set of known instances. These experiments showed that the proposed strategies provided good results with respect to quality of solutions well as the computational cost. In addition, it was possible to evaluate the viability of each employed chromosome representation and the contribution of the heuristics in the convergence process of GA. / Nos últimos anos o Problema de Roteamento de Veículos (PRV) tem atraído cada vez mais a atenção de pesquisadores devido à grande dificuldade de solução e sua presença em várias situações do cotidiano. Em decorrência disso, tem havido um grande esforço para desenvolver algoritmos cada vez mais robustos, ágeis e flexíveis e que possam ser modelados com base no cenário que descreve o problema. O Problema de Roteamento de Veículos Capacitado (PRVC) é uma versão do PRV e consiste em encontrar um conjunto de rotas a serem seguidas por uma frota de veículos homogêneos, os quais devem atender a um conjunto de clientes. O objetivo é minimizar o custo total das rotas respeitando as seguintes restrições: i) as rotas devem iniciar e terminar no mesmo centro de distribuição; ii) cada cliente deve ser visitado uma única vez e sua demanda deve ser atendida integralmente por apenas um veículo e iii) a soma das demandas dos clientes incluídos em uma rota não pode exceder a capacidade do veículo. Problemas desta natureza podem ser classificados como NP-Hard, ou seja, possuem ordem de complexidade não polinomial e normalmente são resolvidos com uso de algoritmos heurísticos e meta-heurísticos. Neste trabalho investigou-se a otimização do PRVC usando Algoritmo Genético (AG) com representações cromossômicas e heurísticas alternativas. Para tanto, foram propostas três estratégias, cada uma delas empregando um modelo diferente de representação cromossômica para codificação da solução no AG. Além disso, foram empregadas as heurísticas de Gillett e Miller para gerar soluções que são incluídas na população inicial do AG e Subida/Descida de Encosta para refinamento das soluções, após um certo número de gerações sem melhoria. Nos experimentos realizados, os resultados obtidos pelas estratégias propostas foram comparados entre si e também com os melhores resultados encontrados na literatura para um conjunto de instâncias conhecidas. Pode-se constatar, a partir desses experimentos, que as estratégias apresentaram bons resultados tanto no que tange a qualidade das soluções quanto ao tempo computacional dispendido. Em adição, foi possível avaliar a viabilidade de cada uma das representações cromossômicas empregadas, além da contribuição das heurísticas no processo de convergência do ag.

Nesta dissertação estudamos --- do ponto de vista algorítmico --- o seguinte problema, conhecido como problema da partição conexa balanceada. Dado um grafo conexo G com pesos atribuídos a seus vértices, e um inteiro q >= 2, encontrar uma partição dos vértices de G em q classes, de forma que cada classe da partição induza um grafo conexo e que, ao considerar as somas dos pesos dos vértices de cada classe, a menor das somas seja o maior possível. Em outras palavras, o objetivo é encontrar q classes cujos pesos sejam tão balanceados quanto possível. Sabe-se que este problema é NP-difícil. Mencionamos alguns resultados sobre complexidade computacional e algoritmos que são conhecidos para este problema. Apresentamos algumas heurísticas que desenvolvemos, todas elas baseadas no uso do algoritmo polinomial para árvores, devido a Perl e Schach, que apresentamos com detalhe. Implementamos quatro heurísticas e um algoritmo de 3/4-aproximação conhecido para o caso q=2. Exibimos os resultados obtidos com os vários testes computacionais conduzidos com instâncias aleatórias, com grafos de diferentes pesos e densidades. Os resultados computacionais indicam que o desempenho dessas heurísticas --- todas elas polinomiais --- é bem satisfatório. No caso especial em que q=2, observamos que a heurística mais onerosa sistematicamente produziu soluções melhores ou iguais às do algoritmo de aproximação / In this dissertation we study algorithmic aspects of the following problem, known as the balanced connected partition. Given a connected graph G with weights defined on its vertices, and an integer q >= 2, find a partition of the vertices of G into q classes such that each class induces a connected graph, and furthermore, when we consider the sum of the weights of the vertices in each class, the smallest sum is as large as possible. In other words, the q classes must have weights that are as balanced as possible. This problem is known to be NP-hard. We mention some computational complexity and algorithmic results that are known for this problem. We present some heuristics that we designed, all of them based on the use of the polynomial algorithm for trees, due to Perl and Schach, which we show in detail. We implemented four heuristics and a 3/4-approximation algorithm that is known for q=2. We run tests on many random instances, of graphs with different weights and densities. The computational results indicate that the performance of these heuristics --- all of polynomial time complexity --- are very satisfactory. For q=2, we observed that the most expensive heuristic produced solutions with values which are systematically better or equal to those produced by the approximation algorithm.

Uma hiper-heurística é uma heurística que pode ser utilizada para lidar com qualquer problema de otimização, desde que a ela sejam fornecidos alguns parâmetros, como estruturas e abstrações, relacionados ao problema considerado. As hiper-heurísticas têm sido aplicadas a alguns problemas práticos e apresentadas como métodos de grande potencial, no que diz respeito à capacidade de possibilitar o desenvolvimento, em tempo bastante reduzido, de algoritmos capazes de lidar satisfatoriamente, do ponto de vista prático, com problemas de otimização complexos e pouco conhecidos. No entanto, é difícil situar as hiper-heurísticas em algum nível de qualidade e avaliar a robustez dessas abordagens caso não as apliquemos a problemas para os quais existam diversas instâncias disponíveis publicamente e já experimentadas por algoritmos relevantes. Este trabalho procura dar alguns passos importantes rumo a essas avaliações, além de ampliar o conjunto das hiper-heurísticas, compreender o impacto de algumas alternativas naturais de desenvolvimento e estabelecer comparações entre os resultados obtidos por diferentes métodos, o que ainda nos permite confrontar as duas diferentes classes de hiper-heurísticas que identificamos. Com essas finalidades em mente, desenvolvemos 3 novas hiper-heurísticas e implementamos 2 das hiper-heurísticas mais importantes criadas por outros autores. Para estas últimas, experimentamos ainda algumas extensões e modificações. Os dois métodos hiper-heurísticos selecionados podem ser vistos como respectivos representantes de duas classes distintas, que aparentemente englobam todas as hiper-heurísticas já desenvolvidas e nos permitem denominar cada um desses métodos como \"hiper-heurística de busca direta por entornos\" ou como \"hiper-heurística evolutiva indireta\". Implementamos cada hiper-heurística como uma biblioteca (em linguagem C), de forma a evidenciar e estimular a independência entre o nível em que se encontra a hiper-heurística e aquele onde se apresentam as estruturas e abstrações diretamente relacionadas ao problema considerado. Naturalmente, essa separação é de ingente importância para possibilitar a reutilização imediata das hiper-heurísticas e garantir que nelas haja total ausência de informações relativas a um problema de otimização específico. / A hyperheuristic is a heuristic that can be used to handle any optimization problem, provided that the algorithm is fed with some parameters, as structures and abstractions, related to the problem at hand. Hyperheuristics have been applied to some practical problems and presented as methods with great potential to allow the quick development of algorithms that are able to successfully deal, from a practical standpoint, with complex ill-known optimization problems. However, it\'s difficult to position hyperheuristics at some quality level and evaluate their robustness without applying them to problems for which there are many instances available in the public domain and already attacked by worthy algorithms. This work aims to give some important steps towards that process of evaluation, additionally increasing the number of available hyperheuristics, studying the impact of some natural development alternatives and comparing the results obtained by different methods, what also enables us to confront the two classes of hyperheuristics that we have identified. With those purposes in mind, we have developed 3 original hyperheuristics and implemented 2 of the most important hyperheuristics created by other authors. For those latter two approaches, we have also experimented with some modifications and extensions. The two methods we have chosen for implementation may be seen as respectively representing two distinct classes, which seem to contain all hyperheuristics developed so far and that allow us to classify any of these methods as either being a \"direct neighbourhood search hyperheuristic\" or an \"indirect evolutive hyperheuristic\". We have implemented each hyperheuristic as a library (in the C language), so as to clearly show and estimulate the independence between the level where the hyperheuristic is and that to which the structures and abstractions directly related to the problem at hand belong. Obviously, this separation of concerns is extremely important to make the immediate reuse of hyperheuristics possible and enforce in them the complete absence of information from a specific optimization problem.

No description available.

Les travaux menés dans cette thèse de doctorat s’inscrivent dans le cadre d’un effort pluslarge d’automatisation des systèmes de simulation industriels. Dans l’industrie aéronautique,et plus particulièrement au sein d’Airbus, l’application historique de la simulation est laformation des pilotes. Il existe aussi des utilisations plus récentes dans la conception desystèmes, ainsi que dans l’intégration de ces systèmes. Ces dernières utilisations exigent untrès haut degré de représentativité, là où historiquement le plus important était le ressenti dupilote. Les systèmes sont aujourd’hui divisés en plusieurs sous-systèmes qui sont conçus, implémentéset validés indépendamment, afin de maintenir leur contrôle malgré l’augmentationde leurs complexités et la réduction des temps de mise sur le marché. Airbus maîtrise déjà lasimulation de ces sous-systèmes, ainsi que leurs intégrations en simulation. Cettemaîtriseest empirique, les spécialistes de la simulation reprennent l’ordonnancement d’intégrationsprécédentes, et l’adaptent à une nouvelle intégration. C’est un processus qui peut parfois êtrechronophage, et qui peut introduire des erreurs. Les tendances actuelles de l’industrie sont à la flexibilité des moyens de production, àl’intégration d’outils logistiques permettant le suivi, à l’utilisation d’outils de simulation enproduction, et à l’optimisation des ressources. Les produits sont de plus en plus souvent desitérations d’anciens produits améliorés, et les tests et simulations intégrés à leurs cycles de vie.Travailler de manière empirique dans une industrie qui nécessite de la flexibilité estune contrainte, et il est aujourd’hui important de facilement modifier des simulations. Laproblématique est donc de mettre en place des méthodes et outils permettant a priori degénérer des ordonnancements de simulations représentatifs.Afin de répondre à ce problème, nous avons mis en place une méthode permettant de décrireles composants d’une simulation, la manière dont cette simulation pourra être exécutée,ainsi que des fonctions permettant de générer des ordonnancements. Par la suite, nous avonsimplémenté un outil afin d’automatiser la recherche d’ordonnancement, en se basant sur desheuristiques. Enfin nous avons testé et vérifié notre méthode et outils sur des cas d’étudesacadémiques et industriels. / The work carried out in this Ph.D. thesis is part of a broader effort to automate industrialsimulation systems. In the aeronautics industry, and more especially within Airbus, thehistorical application of simulation is pilot training. There are also more recent uses in thedesign of systems, as well as in the integration of these systems. These latter applicationsrequire a very high degree of representativeness, where historically the most important factorhas been the pilot’s feeling. Systems are now divided into several subsystems that are designed, implemented andvalidated independently, in order tomaintain their control despite the increase in their complexity,and the reduction in time-to-market. Airbus already has expertise in the simulationof these subsystems, as well as their integration into a simulation. This expertise is empirical;simulation specialists use the previous integrations schedulings and adapt it to a newintegration. This is a process that can sometimes be time-consuming and can introduce errors.The current trends in the industry are towards flexible production methods, integrationof logistics tools for tracking, use of simulation tools in production, as well as resourcesoptimization. Products are increasingly iterations of older, improved products, and tests andsimulations are increasingly integrated into their life cycles. Working empirically in an industry that requires flexibility is a constraint, and nowadays itis essential to facilitate the modification of simulations. The problem is, therefore, to set upmethods and tools allowing a priori to generate representative simulation schedules.In order to solve this problem, we have developed a method to describe the elementsof a simulation, as well as how this simulation can be executed, and functions to generateschedules. Subsequently, we implemented a tool to automate the scheduling search, based onheuristics. Finally, we tested and verified our method and tools in academic and industrialcase studies.

Dans cette thèse, nous étudions deux problèmes de planification de production motivés par des applications du monde réel. Tout d'abord, un problème de planification de production pour un projet de fabrication de vêtements est étudié et un outil d'optimisation est développé pour le résoudre. Deuxièmement, une version restreinte du problème de dimensionnement du lot de capacité avec des configurations dépendantes de la séquence est explorée. Diverses formulations mathématiques sont développées et une analyse de complexité est effectuée pour donner une première analyse du problème. / In this thesis, we study two production planning problems motivated by challenging real-world applications. First, a production planning problem for an apparel manufacturing project is studied and an optimization tool is developed to tackle it. Second, a restricted version of the capacitated lot sizing problem with sequence dependent setups is explored. Various mathematical formulations are developed and complexity analysis is performed to offer a first glance to the problem.

The group scheduling problem commonly arises in cellular manufacturing systems, where parts are grouped into part families. It is characterized by a sequencing task on two levels: on the one hand, a sequence of jobs within each part family has to be identified while, on the other hand, a family sequence has to be determined. In order to solve this NP-hard problem usually heuristic solution approaches are used. In this thesis different aspects of group scheduling are discussed and problem specific heuristics are developed to solve group scheduling problems efficiently. Thereby, particularly characteristic properties of flowshop group scheduling problems, such as the structure of a group schedule or missing operations, are identified and exploited. In a simulation study for job shop manufacturing cells several novel dispatching rules are analyzed. Furthermore, a comprehensive review of the existing group scheduling literature is presented, identifying fruitful directions for future research.

In den vergangenen Jahrzehnten konzentrierte sich das Operations Management auf Optimierungsstrategien, insbesondere wurden Meta-Heuristiken für das komplexe, kombinatorische Problem der ressourcenbegrenzten Ablaufplanung erforscht. In einfachen Worten gehört dieses Problem zu den NP-schweren Problemen, die einen derart großen Lösungsraum besitzen, der mittels Enumerationverfahren rechnerisch unlösbar ist. Daher erfordert die Exploration von optimalen Lösungen andere Methoden als Zufallssuchverfahren. Solche Suchalgorithmen in Meta-Heuristik starten mit einer oder mehreren Ausgangslösung und erkunden den Suchraum nach optimalen Lösungen. Jedoch stellen die existierenden Forschungsansätze zur Lösungssuche nur diejenigen Lösungen bereit, die ausschließlich unter den gegebenen Eingangsbedingungen optimal sind. Diese Eingabebedingungen definieren einen Lösungsraum, in dem alles nach Plan geht. Jedoch ist das in der Praxis sicherlich nicht der Fall. Wie wir sagen, der Wandel ist die einzige Konstante in dieser Welt. Risiken und Unsicherheiten begegnen stets im täglichen Leben. Die vorliegende Dissertation untersucht Optimierungsansätze unter Unsicherheit. Der Forschungsbeitrag ist zweigeteilt. Wie bereits gesagt, wurden Optimierungsstrategien zum Durchsuchen des Lösungsraums in den letzten Jahren stark erforscht. Obwohl es eine anerkannte Tatsache ist, dass die Verbesserung und die Leistung von Optimierungsstrategien stark mit den Initiallösungen korreliert, scheint die Literatur diesbezüglich inexistent, während zumeist auf die Entwicklung von meta-heuristischen Algorithmen wie Genetische Algorithmen und Particle-Swarm-Optimierung fokussiert wird. Die Initiallösungen werden durch simulationsbasierte Strategien entwickelt, die typischerweise gierige Regeln und ereignisbasierte Simulation nutzen. Allerdings verhalten sich kommerzielle Basis-Softwareprodukte meist als Black-Box und stellen keine Informationen über das interne Verhalten bereit. Außerdem erfordern derartige Softwareprodukte meist spezielle Architekturen und missachten Ressourcenbeschränkungen. Die vorliegende Studie diskutiert die ressourcenbeschränkte Projektplanung mit alternativen Modi und schlägt ein simulationsbasiertes Rahmenwerk vor, mit dem ein heuristisches Multi-Pass-Verfahren zur Verfügung gestellt wird. Das erweiterte Multi-Modus-Problem ist in der Lage, den Produktionsbereich in einer besseren Art und Weise nachzubilden, bei dem eine Aktivität von mehreren Ressourcen unterschiedlicher Qualifikation ausgeführt werden kann. Der vorgeschlagene Rahmen diskutiert die Leistung von Algorithmen und verwendet hierfür Benchmark-Instanzen. Das Verhalten verschiedener Projektnetze und deren Eigenschaften werden auch innerhalb des vorgeschlagenen Rahmenwerks bewertet. Darüber hinaus hilft das offene Rahmenwerk, besondere Eigenschaften von Aktivitäten zu analysieren, um deren Verhalten im Fall von Störungen zu prognostizieren. Die traditionellen Methoden der Risikoanalyse schlagen Slack-basierte Maßzahlen vor, um die Effizienz von Basisplänen zu bestimmen. Das Rahmenwerk wird weiter entwickelt, um mit diesem einen Prüfstand zu gestalten, mit dem nicht-reguläre Maßzahlen bestimmt werden können. Diese Maßnahmen werden als Robustheitsindikatoren bezeichnet und korrelieren mit der Verzögerung derartiger Multi-Modus-Probleme. Solche Leistungsmaße können genutzt werden, um die Wirksamkeit von Basisplänen zu bewerten und ihr Verhalten unter Unsicherheiten zu prognostizieren. Die Ergebnisse dieser Tests werden als modifizierte Zielfunktion verwendet, in der ein bi-objektives Leistungsmaß aus Durchlaufzeit und Robustheit eingesetzt wird, um die Effizienz der vorgeschlagenen Heuristiken zu testen. Da diese Leistungsmaße das Verhalten von Aktivitäten unter Störungen zeigen, werden diese auch genutzt, um die Formfaktoren und Puffergrößen für die Entwicklung eines stochastischen Modells zu bestimmen. Die Analyse der Projektergebnisse, durchgeführt mittels Monte-Carlo-Simulationen, unterstützt das Argument von Teilpuffern für die Modellierung von Aktivitätsdauern anstatt Ansätze mit Extrempuffern und PERT-beta-Schätzungen. / Over the past decades, researches in the field of operations management have focused on optimization strategies based on meta-heuristics for the complex-combinatorial problem of resource constrained scheduling. In simple terms, the solution for this particular problem categorized as NP-hard problem, exhibits a large search space, is computationally intractable, and requires techniques other than random search. Meta-heuristic algorithms start with a single or multiple solutions to explore and optimize using deterministic data and retrieve a valid optimum only under specified input conditions. These input conditions define a solution search space for a theoretical world undergoing no disturbance. But change is inherent to the real world; one is faced with risks and uncertainties in everyday life. The present study explores solution methodologies in the face of uncertainties. The contributions of this thesis are two-fold. As mentioned earlier, existing optimization strategies have been vigorously investigated in the past decade with respect to exploring large solution search space. Although, it is an established fact that the improvement and performance of optimization strategies is highly correlated with the initial solutions, existing literature regarding this area is not exhaustive and mostly focuses on the development of meta-heuristic algorithms such as genetic algorithms and particle swarm optimization. The initial solutions are developed through simulation based strategies mainly based on greedy rules and event based simulation. However, the available commercial softwares are primarily modeled as a black box and provide little information as to internal processing. Additionally, such planners require special architecture and disregard resource constraints. The present study discusses the multi-mode resource constrained scheduling problem and proposes a simulation-based framework to provide a multi-pass heuristic method. The extended version of multi-mode problem is able to imitate production floor in an improved manner where a task can be performed with multiple resources with certain qualifications. The performance of the proposed framework was analyzed using benchmark instances. The behavior of different project networks and their characteristics is also evaluated within the proposed framework. In addition, the open framework aids in determining the particular characteristic of tasks in order to analyze and forecast their behavior in case of disruptions. The traditional risk analysis techniques suggest slack-based measures in order to determine the efficiency of baseline schedules. The framework is further developed to design a test bench in order to determine non-regular performance measures named as robustness indicators which correlate with the delay of such cases as multi-mode problem. Such performance measures can be used to indicate the effectiveness of baseline schedules and forecast their behavior. The outputs of these tests are used to modify the objective function which uses makespan and robustness indicators as a bi-objective performance measure in order to test the efficiency of proposed heuristics. Furthermore, since these measures indicate the behavior of tasks under disruptions, they are utilized in order to determine the shape factors and buffers for the development of a stochastic model. The analysis of project outcomes performed through Monte-Carlo simulations supports the argument of partial buffer sizing for modeling activity duration estimates rather than extreme buffer approaches proposed via PERT-beta estimates.