Successful projects are characterized by the quality of their planning. Good planning that better takes into account contextual factors allows more accurate estimates to be achieved. As an outcome of this research, a new framework composed of best practices has been discovered. This comprises an open platform that project experts and practitioners can work with efficiently, and that researchers can develop further as required. The research investigation commenced in the autumn of 2008 with a pilot study and then proceeded through an inductive research process, involving a series of eleven interviews. These consisted of interviews with four well-recognized experts in the field, four interviews with different practitioners and three group interviews. In addition, a long-running observation of forty-five days was conceptualized, together with other data sources, before culminating in the proposal of a new framework for improving the accuracy of estimates. Furthermore, an emerging framework – and a description of its know-how in terms of application – have been systematically reviewed through the course of four hundred twenty-five days of meetings, dedicated for the most part to improving the use of a wide range of specific project management tools and techniques and to an improvement in understanding of planning and the estimation process associated with it. This approach constituted an ongoing verification of the research’s findings against project management practice and also served as an invaluable resource for the researcher’s professional and practice-oriented development. The results obtained offered fresh insights into the importance of knowledge management in the estimation process, including the “value of not knowing”, the oft-overlooked phenomenon of underestimation and its potential to co-exist with overestimation, and the use of negative buffer management in the critical chain concept to secure project deadlines. The project also highlighted areas of improvement for future research practice that wishes to make use of an inductive approach in order to achieve a socially agreed framework, rather than a theory alone. In addition, improvements were suggested to the various qualitative tools employed in the customized data analysis process.

Estimation of Distribution Algorithm (EDA) is a branch of evolutionary computation that learn a probabilistic model of good solutions. Probabilistic models are used to represent relationships between solution variables which may give useful, human-understandable insights into real-world problems. Also, developing an effective PM has been shown to significantly reduce function evaluations needed to reach good solutions. This is also useful for real-world problems because their representations are often complex needing more computation to arrive at good solutions. In particular, many real-world problems are naturally represented as permutations and have expensive evaluation functions. EDAs can, however, be computationally expensive when models are too complex. There has therefore been much recent work on developing suitable EDAs for permutation representation. EDAs can now produce state-of-the-art performance on some permutation benchmark problems. However, models are still complex and computationally expensive making them hard to apply to real-world problems. This study investigates some limitations of EDAs in solving permutation and scheduling problems. The focus of this thesis is on addressing redundancies in the Random Key representation, preserving diversity in EDA, simplifying the complexity attributed to the use of multiple local improvement procedures and transferring knowledge from solving a benchmark project scheduling problem to a similar real-world problem. In this thesis, we achieve state-of-the-art performance on the Permutation Flowshop Scheduling Problem benchmarks as well as significantly reducing both the computational effort required to build the probabilistic model and the number of function evaluations. We also achieve competitive results on project scheduling benchmarks. Methods adapted for solving a real-world project scheduling problem presents significant improvements.

In this thesis, meta-heuristic algorithms are developed to obtain optimum or near optimum solutions for the time-cost-resource trade-off and resource leveling problems in project scheduling. Time cost trade-off, resource leveling, single-mode resource constrained project scheduling, multi-mode resource constrained project scheduling and resource constrained time cost trade-off problems are analyzed. Genetic algorithm simulated annealing, quantum simulated annealing, memetic algorithm, variable neighborhood search, particle swarm optimization, ant colony optimization and electromagnetic scatter search meta-heuristic algorithms are implemented for time cost trade-off problems with unlimited resources. In this thesis, three new meta-heuristic algorithms are developed by embedding meta-heuristic algorithms in each other. Hybrid genetic algorithm with simulated annealing presents the best results for time cost trade-off. Resource leveling problem is analyzed by five genetic algorithm based meta-heuristic algorithms. Apart from simple genetic algorithm, four meta-heuristic algorithms obtained same schedules obtained in the literature. In addition to this, in one of the test problems the solution is improved by the four meta-heuristic algorithms. For the resource constrained scheduling problems / genetic algorithm, genetic algorithm with simulated annealing, hybrid genetic algorithm with simulated annealing and particle swarm optimization meta-heuristic algorithms are implemented. The algorithms are tested by using the project sets of Kolisch and Sprecher (1996). Genetic algorithm with simulated annealing and hybrid genetic algorithm simulated annealing algorithm obtained very successful results when compared with the previous state of the art algorithms. 120-activity multi-mode problem set is produced by using the single mode problem set of Kolisch and Sprecher (1996) for the analysis of resource constrained time cost trade-off problem. Genetic algorithm with simulated annealing presented the least total project cost.

This study develops a systems integration project scheduling model which identifies the assignment of activity responsibilities that minimizes expected project implementation cost, considering the project risk. Assignment of resources to the individual jobs comprising the project is a persistent problem in project management. Mostly, skilled labor is an essential resource and both the time and the cost incurred to perform a job depend on the resource to which job is assigned. A systems integration project includes implementation issues in the areas of shipping, installation, and commissioning. Implementation problems lead to project delays, increased costs, and decreased performance, leading to customer dissatisfaction with the systems integrator. Activities can be performed in one of three ways: by the integrator, by the customer, or jointly between the integrator and customer. In this study we select the performer (mode) of each activity comprising the project network while taking into consideration the varying cost, duration and extreme event probability of each activity among different modes-integrator, joint work and customer. Use of the model will permit customers and integrators to mutually agree on an appropriate assignment of responsibilities in the contract. Systems integrators can also use the model to improve their implementation services offerings. An experimental design and a Monte-Carlo simulation study were conducted to see the effects of the parameters of the problem on the selection of modes.

Made available in DSpace on 2016-12-23T14:33:39Z (GMT). No. of bitstreams: 1 Dissertacao_CRAVO_G_L_2009.pdf: 1278828 bytes, checksum: ebab7f313edc64bb51241b5c7d587d33 (MD5) Previous issue date: 2009-06-25 / This master s thesis presents an implementation of the GRASP meta-heuristic for solving the Multi-mode Resource constrained Problem of Scheduling Project (MRCPSP). The MRCPSP belongs to the class NP-Hard and therefore has received attention of many researchers. In this thesis, a case study problem of Scheduling Industrial Maintenance is viewed as a MRCPSP. The GRASP was tested with a set of benchmark tests obtained from PSPLIB (Project Scheduling Library). The results showed that the GRASP is a good strategy for solving MRCPSP instances. / Esse trabalho apresenta uma implementação da meta-heurística GRASP para a resolução do Problema de Escalonamento de Projetos com Restrições de Recursos e Múltiplos Modos de Processamento (MRCPSP). O MRCPSP é um problema da classe NP Difícil e por isso vem recebendo atenção dos pesquisadores. Nessa dissertação, também é apresentado um estudo de caso cujo problema de Programação de Manutenção Industrial é visto como um problema de escalonamento de projeto. O GRASP foi testado com o conjunto de instâncias do MRCPSP disponíveis na PSPLIB (Project Scheduling Problem Library). Os resultados obtidos mostraram que o GRASP proposto se configura como uma boa estratégia de solução para o MRCPSP.

The diploma thesis deals with the process of planning the costs of a contract for construction. The thesis describes the pricing, contract for construction, construction time models, and allocation of costs during the construction. It introduces a particular contract for construction, which is analysed mainly with regard to the allocation of costs during the construction. The thesis introduces also alternative solutions for the allocation of costs during the construction. The purpose of the thesis is to compare the alternative solutions with the reality, and analyse the allocation of costs during the construction.

Die zentrale europäische Verkehrsflusssteuerung (englisch: ATFM) und Luftverkehrsgesellschaften (englisch: Airlines) verwenden unterschiedliche Paradigmen für die Priorisierung von Flügen. Während ATFM jeden Flug als individuelle Einheit betrachtet, um die Kapazitätsauslastung aller Sektoren zu steuern, bewerten Airlines jeden Flug als Teilabschnitt eines Flugzeugumlaufes, eines Crew-Einsatzplanes bzw. einer Passagierroute. Infolgedessen sind ATFM-Zeitfenster für Flüge in Kapazitätsengpässen oft schlecht auf die Ressourcenabhängigkeiten innerhalb eines Airline-Netzwerks abgestimmt, sodass die Luftfahrzeug-Bodenabfertigung – als Verbindungselement bzw. Bruchstelle zwischen einzelnen Flügen im Netzwerk – als Hauptverursacher primärer und reaktionärer Verspätungen in Europa gilt. Diese Dissertation schließt die Lücke zwischen beiden Paradigmen, indem sie ein integriertes Optimierungsmodell für die Flugplanwiederherstellung entwickelt. Das Modell ermöglicht Airlines die Priorisierung zwischen Flügen, die von einem ATFM-Kapazitätsengpass betroffen sind, und berücksichtigt dabei die begrenzte Verfügbarkeit von Abfertigungsressourcen am Flughafen. Weiterhin werden verschiedene Methoden untersucht, um die errechneten Flugprioritäten vertraulich innerhalb von kooperativen Lösungsverfahren mit externen Stakeholdern austauschen zu können. Das integrierte Optimierungsmodell ist eine Erweiterung des Resource-Constrained Project Scheduling Problems und integriert das Bodenprozessmanagement von Luftfahrzeugen mit bestehenden Ansätzen für die Steuerung von Flugzeugumläufen, Crew-Einsatzplänen und Passagierrouten. Das Modell soll der Verkehrsleitzentrale einer Airline als taktische Entscheidungsunterstützung dienen und arbeitet dabei mit einer Vorlaufzeit von mehr als zwei Stunden bis zur nächsten planmäßigen Verkehrsspitze. Systemimmanente Unsicherheiten über Prozessabweichungen und mögliche zukünftige Störungen werden in der Optimierung in Form von stochastischen Prozesszeiten und mittels des neu-entwickelten Konzeptes stochastischer Verspätungskostenfunktionen berücksichtigt. Diese Funktionen schätzen die Kosten der Verspätungsausbreitung im Airline-Netzwerk flugspezifisch auf der Basis historischer Betriebsdaten ab, sodass knappe Abfertigungsressourcen am Drehkreuz der Airline den kritischsten Flugzeugumläufen zugeordnet werden können. Das Modell wird innerhalb einer Fallstudie angewendet, um die taktischen Kosten einer Airline in Folge von verschiedenen Flugplanstörungen zu minimieren. Die Analyseergebnisse zeigen, dass die optimale Lösung sehr sensitiv in Bezug auf die Art, den Umfang und die Intensität einer Störung reagiert und es folglich keine allgemeingültige optimale Flugplanwiederherstellung für verschiedene Störungen gibt. Umso dringender wird der Einsatz eines flexiblen und effizienten Optimierungsverfahrens empfohlen, welches die komplexen Ressourcenabhängigkeiten innerhalb eines Airline-Netzwerks berücksichtigt und kontextspezifische Lösungen generiert. Um die Effizienz eines solchen Optimierungsverfahrens zu bestimmen, sollte das damit gewonnene Steuerungspotenzial im Vergleich zu aktuell genutzten Verfahren über einen längeren Zeitraum untersucht werden. Aus den in dieser Dissertation analysierten Störungsszenarien kann geschlussfolgert werden, dass die flexible Standplatzvergabe, Passagier-Direkttransporte, beschleunigte Abfertigungsverfahren und die gezielte Verspätung von Abflügen sehr gute Steuerungsoptionen sind und während 95 Prozent der Saison Anwendung finden könnten, um geringe bis mittlere Verspätungen von Einzelflügen effizient aufzulösen. Bei Störungen, die zu hohen Verspätungen im gesamten Airline-Netzwerk führen, ist eine vollständige Integration aller in Betracht gezogenen Steuerungsoptionen erforderlich, um eine erhebliche Reduzierung der taktischen Kosten zu erreichen. Dabei ist insbesondere die Möglichkeit, Ankunfts- und Abflugzeitfenster zu tauschen, von hoher Bedeutung für eine Airline, um die ihr zugewiesenen ATFM-Verspätungen auf die Flugzeugumläufe zu verteilen, welche die geringsten Einschränkungen im weiteren Tagesverlauf aufweisen. Die Berücksichtigung von Unsicherheiten im nachgelagerten Airline-Netzwerk zeigt, dass eine Optimierung auf Basis deterministischer Verspätungskosten die taktischen Kosten für eine Airline überschätzen kann. Die optimale Flugplanwiederherstellung auf Basis stochastischer Verspätungskosten unterscheidet sich deutlich von der deterministischen Lösung und führt zu weniger Passagierumbuchungen am Drehkreuz. Darüber hinaus ist das vorgeschlagene Modell in der Lage, Flugprioritäten und Airline-interne Kostenwerte für ein zugewiesenes ATFM-Zeitfenster zu bestimmen. Die errechneten Flugprioritäten können dabei vertraulich in Form von optimalen Verspätungszeitfenstern pro Flug an das ATFM übermittelt werden, während die Definition von internen Kostenwerten für ATFM-Zeitfenster die Entwicklung von künftigen Handelsmechanismen zwischen Airlines unterstützen kann.:1 Introduction 2 Status Quo on Airline Operations Management 3 Schedule Recovery Optimization Approach with Constrained Resources 4 Implementation and Application 5 Case Study Analysis 6 Conclusions / Air Traffic Flow Management (ATFM) and airlines use different paradigms for the prioritisation of flights. While ATFM regards each flight as individual entity when it controls sector capacity utilization, airlines evaluate each flight as part of an aircraft rotation, crew pairing and passenger itinerary. As a result, ATFM slot regulations during capacity constraints are poorly coordinated with the resource interdependencies within an airline network, such that the aircraft turnaround -- as the connecting element or breaking point between individual flights in an airline schedule -- is the major contributor to primary and reactionary delays in Europe. This dissertation bridges the gap between both paradigms by developing an integrated schedule recovery model that enables airlines to define their optimal flight priorities for schedule disturbances arising from ATFM capacity constraints. These priorities consider constrained airport resources and different methods are studied how to communicate them confidentially to external stakeholders for the usage in collaborative solutions, such as the assignment of reserve resources or ATFM slot swapping. The integrated schedule recovery model is an extension of the Resource-Constrained Project Scheduling Problem and integrates aircraft turnaround operations with existing approaches for aircraft, crew and passenger recovery. The model is supposed to provide tactical decision support for airline operations controllers at look-ahead times of more than two hours prior to a scheduled hub bank. System-inherent uncertainties about process deviations and potential future disruptions are incorporated into the optimization via stochastic turnaround process times and the novel concept of stochastic delay cost functions. These functions estimate the costs of delay propagation and derive flight-specific downstream recovery capacities from historical operations data, such that scarce resources at the hub airport can be allocated to the most critical turnarounds. The model is applied to the case study of a network carrier that aims at minimizing its tactical costs from several disturbance scenarios. The case study analysis reveals that optimal recovery solutions are very sensitive to the type, scope and intensity of a disturbance, such that there is neither a general optimal solution for different types of disturbance nor for disturbances of the same kind. Thus, airlines require a flexible and efficient optimization method, which considers the complex interdependencies among their constrained resources and generates context-specific solutions. To determine the efficiency of such an optimization method, its achieved network resilience should be studied in comparison to current procedures over longer periods of operation. For the sample of analysed scenarios in this dissertation, it can be concluded that stand reallocation, ramp direct services, quick-turnaround procedures and flight retiming are very efficient recovery options when only a few flights obtain low and medium delays, i.e., 95% of the season. For disturbances which induce high delay into the entire airline network, a full integration of all considered recovery options is required to achieve a substantial reduction of tactical costs. Thereby, especially arrival and departure slot swapping are valuable options for the airline to redistribute its assigned ATFM delays onto those aircraft that have the least critical constraints in their downstream rotations. The consideration of uncertainties in the downstream airline network reveals that an optimization based on deterministic delay costs may overestimate the tactical costs for the airline. Optimal recovery solutions based on stochastic delay costs differ significantly from the deterministic approach and are observed to result in less passenger rebooking at the hub airport. Furthermore, the proposed schedule recovery model is able to define flight priorities and internal slot values for the airline. Results show that the priorities can be communicated confidentially to ATFM by using the concept of 'Flight Delay Margins', while slot values may support future inter-airline slot trading mechanisms.:1 Introduction 2 Status Quo on Airline Operations Management 3 Schedule Recovery Optimization Approach with Constrained Resources 4 Implementation and Application 5 Case Study Analysis 6 Conclusions

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Résumé indisponible. / In this thesis we have concerned ourselves with university timetabling problems both course timetabling and examination timetabling problems. Most of the timetabling problems are computationally NP-complete problems, which means that the amount of computation required to find solutions increases exponentially with problem size. These are idiosyncratic nature problems, for example different universities have their own set of constraints, their own definition of good timetable, feasible timetable and their own choice about the use of constraint type (as a soft or hard constraint). Unfortunately, it is often the case that a problem solving approach which is successfully applied for one specific problem may not become suitable for others. This is a motivation, we propose a generalized problem which covers many constraints used in different universities or never used in literature. Many university timetabling problems are sub problems of this generalized problem. Our proposed algorithms can solve these sub problems easily, moreover constraints can be used according to the desire of user easily because these constraints can be used as reference to penalty attached with them as well. It means that give more penalty value to hard constraints than soft constraint. Thus more penalty value constraints are dealt as a hard constraint by algorithm. Our algorithms can also solve a problem in two phases with little modification, where in first phase hard constraints are solved. In this work we have preferred and used two phase technique to solve timetabling problems because by using this approach algorithms have broader search space in first phase to satisfy hard constraints while not considering soft constraints at all. Two types of algorithms are used in literature to solve university timetabling problem, exact algorithms and approximation algorithms. Exact algorithms are able to find optimal solution, however in university timetabling problems exact algorithms constitute brute-force style procedures. And because these problems have the exponential growth rates of the search spaces, thus these kinds of algorithms can be applied for small size problems. On the other side, approximation algorithms may construct optimal solution or not but they can produce good practically useable solutions. Thus due to these factors we have proposed approximation algorithms to solve university timetabling problem. We have proposed metaheuristic based techniques to solve timetabling problem, thus we have mostly discussed metaheuristic based algorithms such as evolutionary algorithms, simulated annealing, tabu search, ant colony optimization and honey bee algorithms. These algorithms have been used to solve many other combinatorial optimization problems other than timetabling problem by modifying a general purpose algorithmic framework. We also have presented a bibliography of linear integer programming techniques used to solve timetabling problem because we have formulated linear integer programming formulations for our course and examination timetabling problems. We have proposed two stage algorithms where hard constraints are satisfied in first phase and soft constraints in second phase. The main purpose to use this two stage technique is that in first phase hard constraints satisfaction can use more relax search space because in first phase it does not consider soft constraints. In second phase it tries to satisfy soft constraints when maintaining hard constraints satisfaction which are already done in first phase. (...)

Le contexte d’application de cette recherche a été le projet CRIBA. CRIBA vise à industrialiser une solution intégrée de rénovation et d’isolation de grands bâtiments. De ce fait, une part importante de la valeur ajoutée est transférée des chantiers de rénovation vers des usines de fabrications devant être synchronisées avec les chantiers. La planification est l'une des étapes importantes de la gestion de projets. S’adaptant à une organisation, elle vise une réalisation optimale en considérant les facteurs de temps, coût, qualité ainsi que l’affectation efficace des ressources. Cette affectation est d’autant plus complexe lorsqu’un ensemble de projets se partagent les ressources, renouvelables ou non renouvelables. L'objectif global de notre étude est de développer un outil d’aide à la décision pour un décideur visant à planifier plusieurs projets en intégrant l'allocation des ressources renouvelables, et la planification des flux de ressources non-renouvelables vers ces projets. Dans ce cadre, les ressources non renouvelables telles que les machines et la main-d'œuvre ont une disponibilité initiale limitée sur les chantiers. Cependant, nous supposons que des quantités limitées supplémentaires peuvent être achetées. En outre, nous prenons en compte la volonté des coordinateurs des projets pour l’approvisionnement des chantiers en juste à temps (just in time), en particulier pour les ressources peu demandées, encombrantes et à forte valeur. Ceci oblige à étendre le cadre du modèle de la planification des projets en incluant la planification de la chaîne logistique qui approvisionne les ressources non renouvelables des chantiers. Enfin, pour répondre au besoin d’outils décisionnels responsables sur le plan environnemental, le modèle prévoit le transport et le recyclage des déchets des chantiers dans les centres appropriés. Un modèle linéaire mixte du problème est ainsi posé. Puisqu’il rentre dans la classe des modèles d'optimisation NP-durs, une double résolution est proposée. D’abord à l’aide d’un solveur puis une méta-heuristique basée sur un algorithme génétique. De plus, pour faciliter l'utilisation du modèle par des utilisateurs peu familiers avec la recherche opérationnelle, un système d'aide à la décision basé sur une application web a été développé. L’ensemble de ces contributions ont été évaluées sur des jeux de test issus du projet CRIBA. / The application context of the current study is on a CRIBA project. The CRIBA aims to industrialize an integrated solution for the insulation and thermal renovation of building complexes in France. As a result, a significant part of the added value is transferred from the renovation sites to the manufacturing centers, making both synchronized. Planning is one of the important steps in project management. Depending on the different viewpoints of organizations, successful planning for projects can be achieved by performing to optimality within the time, cost, quality factors as well as the efficient assignment of resources. Planning for the allocation of resources becomes more complex when a set of projects is sharing renewable and non-renewable resources. The global objective of the study is to develop a decision-making tool for decision-makers to plan multiple projects by integrating the allocation of the renewable resources and planning the flow of non-renewable resources to the project worksites. In this context, non-renewable resources such as equipment and labor have a limited initial availability at the construction sites. Nevertheless, we assume that additional limited amounts can be added to the projects. In addition, we take into account the interest of the project coordinators in supplying the non-renewable resources in a just-in-time manner to the projects, especially for low-demand resources with a high price. This requires extending the framework of the project planning by including the planning of the supply chain which is responsible. Finally, in order to meet the requirements for environmentally responsible decision-making, the model envisages the transportation and recycling of waste from project sites to appropriate centers. A mixed integer linear model of the problem is proposed. Since it falls within the class of NP-hard optimization models, a double resolution is targeted: first, using a solver and then a metaheuristic based on the genetic algorithm. In addition, in order to facilitate the use of the model by users unfamiliar with operational research, a web-based decision-making support system has been developed. All the contributions are evaluated in a set of case studies from the CRIBA project.