This dissertation solves an optimization problem in the area of scheduling large-scale petroleum development projects under several resources constraints. The dissertation focuses on the application of a metaheuristic search Genetic Algorithm (GA) in solving the problem. The GA is a global search method inspired by natural evolution. The method is widely applied to solve complex and sizable problems that are difficult to solve using exact optimization methods. A classical resource allocation problem in operations research known under Knapsack Problems (KP) is considered for the formulation of the problem. Motivation of the present work was initiated by certain petroleum development scheduling problem in which large-scale investment projects are to be selected subject to a number of resources constraints in several periods. The constraints may occur from limitations in various resources such as capital budgets, operating budgets, and drilling rigs. The model also accounts for a number of assumptions and business rules encountered in the application that motivated this work. The model uses an economic performance objective to maximize the sum of Net Present Value (NPV) of selected projects over a planning horizon subject to constraints involving discrete time dependent variables. Computational experiments of 30 projects illustrate the performance of the model. The application example is only illustrative of the model and does not reveal real data. A Greedy algorithm was first utilized to construct an initial estimate of the objective function. GA was implemented to improve the solution and investigate resources constraints and their effect on the assets value. The timing and order of investment decisions under constraints have the prominent effect on the economic performance of the assets. The application of an integrated optimization model provides means to maximize the financial value of the assets, efficiently allocate limited resources and to analyze more scheduling alternatives in less time.

In this study, results of a comparison on benchmark test problems are presented to investigate the performance of Primavera V.4.1 with its two resource allocation priority rules and MS Project 2003. Resource allocation capabilities of the packages are measured in terms of deviation from the upper bound of the minimum makespan. Resource constrained project scheduling problem instances are taken from PSPLIB which are generated under a factorial design from ProGen. Statistical tests are applied to the results for investigating the significance effectiveness of the parameters.

This thesis reviews the software acquisition models and management tools used for managing, tracking and planning the acquisition projects all around the world. Then the requirements of Turkish Land Forces Command (TLFC) for a project management tool are considered. Features of a project management tool that is suitable for TLFC are determined through the ideas of TLFC personnel of IS department. After evaluation of selected tools through the requirements of TLFC the best one is presented. Then an interface module is developed through the requirements of the current acquisition process of TLFC for effective usage of the tool. Finally the study is evaluated.

This thesis deals with the description of the state of resource-constrained project scheduling problem. It defines the formal problem and its complexity. It also describes variants of this problem. Algorithms for solving RCPSP are presented. Heuristic genetic algorithm GARTH is analyzed in depth. The implementation of prototypes solving RCPSP using GARTH is outlined. Several improvements to the original algorithm are designed and evaluated.

This paper analyses the current state of research regarding solution methods dealing with resource-constrained project scheduling problems. The intention is to present a concentrated survey and brief scientific overview on models, their decision variables and constraints as well as current solution methods in the field of project scheduling. The allocation of scarce resources among multiple projects with different, conflicting decision variables is a highly difficult problem in order to achieve an optimal schedule which meets all (usually different) of the projects’ objectives. Those projects, e.g. the assembly of complex machinery and goods, consume many renewable, e.g. workforce/staff, and non-renewable, e.g. project budget, resources. Each single process within these projects can often be performed in different ways – so called execution modes can help to make a schedule feasible. On the other hand the number of potential solutions increases dramatically through this fact. Additional constraints, e.g. min/max time lags, preemption or specific precedence relations of activities, lead to highly complex problems which are NP-hard in the strong sense.

Der Planungsprozess des Produktionsablaufes ist insbesondere in personalintensiven Bereichen durch einen hohen Komplexitätsgrad gekennzeichnet. Dies gilt nicht allein für die Domäne der Fertigung von Industriegütern, sondern ist überall dort charakteristisch, wo projektähnliche Aufgaben und Tätigkeiten im Kurzfristbereich hinsichtlich Ressourcenzuteilung determiniert werden müssen. Personal ist hierbei weitaus weniger homogen, als dies auf andere Ressourcentypen zutrifft. Daher gilt es der Heterogenität der Prozesse und Strukturen unter Beachtung individuell ausgeprägter Eigenschaften und Fähigkeiten des Personals einen quantitativ beschreibbaren und damit operationali-sierbaren Rahmen zu geben. Die Komplexität einer personalbezogenen, zielfunktionsorientierten Zuteilungsentscheidung kann im Kontext der Kapazitätsplanung damit signifikant reduziert werden.

It's commonly assumed that experience leads to efficiency, yet this is largely unaccounted for in resource-constrained project scheduling. This thesis considers the idea that learning effects could allow selected activities to be completed within reduced time, if they're scheduled after activities where workers learn relevant skills. This paper computationally explores the effect of this autonomous, intra-project learning on optimal makespan and problem difficulty. A learning extension is proposed to the standard RCPSP scheduling problem. Multiple parameters are considered, including project size, learning frequency, and learning intensity. A test instance generator is developed to adapt the popular PSPLIB library of scheduling problems to this model. Four different Constraint Programming model formulations are developed to efficiently solve the model. Bounding techniques are proposed for tightening optimality gaps, including four lower bounding model relaxations, an upper bounding model relaxation, and a Destructive Lower Bounding method. Hundreds of thousands of scenarios are tested to empirically determine the most efficient solution approaches and the impact of learning on project schedules. Potential makespan reduction as high as 50% is discovered, with the learning effects resembling a learning curve with a point of diminishing returns. A combination of bounding techniques is proven to produce significantly tighter optimality gaps.

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.

Yes / The current turbulent times call for adaptability, especially in non-repetitive endeavours being a vital characteristic of project management. The research organized along five objectives commenced in the autumn of 2008 with a pilot study. Then it proceeded through an inductive research process, involving a series of interviews with well-recognized international experts in the field. In addition conceptualized long-running observation of forty-five days was used, before proposal of a new framework for improving the accuracy of estimates in project management. Furthermore, the framework’s “know-how to apply” description have been systematically reviewed through the course of four hundred twenty-five days of meetings. This achieved socially agreed understanding assured that it may be possible to improve accuracy of estimates, while having flexible, adaptable framework exploiting dependency between project context and conditioned by it, use of tools and techniques.

Dans cette Thèse, nous avons introduit plusieurs procédures pour résoudre le problème d’ordonnancement du projet multi-compétences (MSPSP). L’objectif est de trouver un ordonnancement qui minimise le temps de terminaison (makespan) d’un projet, composé d’un ensemble d’activités. Les relations de précédences et les contraintes de ressource seront considérées. Dans ce problème, les ressources sont des membres du personnel qui maîtrisent plusieurs compétences. Ainsi, un certain nombre de travailleurs doit être affecté pour utiliser chaque compétence requise par une activité. Par ailleurs, nous accorderons une importance particulière aux méthodes exactes pour résoudre le MSPSP, puisqu’il y a encore un certain nombre d’instances pour lesquelles l’optimalité doit encore être prouvée. Néanmoins,pour traiter des instances plus importantes, nous implémentons une approche heuristique. / In this Phd Thesis we introduce several procedures to solve the Multi-Skill Project Scheduling Problem (MSPSP). The aim is to find a schedule that minimizes the completion time (makespan) of a project, composed of a set of activities. Precedence relations and resource constraints are considered. In this problem, resources are staff members that master several skills. Thus, a given number of workers must be assigned to perform each skill required by an activity. Furthermore, we give a particula rimportance to exact methods for solving the Multi-Skill Project Scheduling Problem (MSPSP), since there are still several instances for which optimality is still to be proven. Nevertheless, with the purpose of solving big sized instances we also developed and implemented a heuristic approach.