Towards Merging Binary Integer Programming Techniques with Genetic Algorithms

This paper presents a framework based on merging a binary integer programming technique with a genetic algorithm. The framework uses both lower and upper bounds to make the employed mathematical formulation of a problem as tight as possible. For problems whose optimal solutions cannot be obtained, precision is traded with speed through substituting the integrality constrains in a binary integer program with a penalty. In this way, instead of constraining a variable u with binary restriction, u is considered as real number between 0 and 1, with the penalty of Mu(1-u), in which M is a large number. Values not near to the boundary extremes of 0 and 1 make the component of Mu(1-u) large and are expected to be avoided implicitly. The nonbinary values are then converted to priorities, and a genetic algorithm can use these priorities to fill its initial pool for producing feasible solutions. The presented framework can be applied to many combinatorial optimization problems. Here, a procedure based on this framework has been applied to a scheduling problem, and the results of computational experiments have been discussed, emphasizing the knowledge generated and inefficiencies to be circumvented with this framework in future

Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

Constraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate, takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature

Généralisations du problème d'ordonnancement de projet à ressources limitées

Un problème d'ordonnancement de projet à ressources limitées (POPRL) consiste en l'ordonnancement d'un ensemble de tâches, nécessitant un ou plusieurs types de ressources, renouvelables ou non renouvelables, en quantités limitées. La résolution d'un POPRL a pour but la détermination des dates d'exécution des tâches en tenant compte des contraintes de préséance et de disponibilité des ressources et ayant comme objectif la minimisation de la durée totale du projet. Le POPRL est un problème d'optimisation combinatoire de complexité NP-dur (Blazewicz et al. 1983). Une revue de littérature du (POPRL) est présentée au chapitre 2. Plus de 125 articles scientifiques sont analysés. Les contributions relatives à ce problème portent sur les méthodes exactes de résolution, la détermination de bornes inférieures sur la durée du projet et les méthodes heuristiques (approchées) de résolution. L'aspect pratique de ce problème dans des contextes industriels divers a conduit à de nombreuses généralisations du problème classique. On constate que malgré les efforts déployés pour définir des POPRL plus généraux, les contraintes de transfert des ressources continuent à être ignorées, nous constatons aussi que l'optimisation du problème en considérant les coûts a été très peu traitée dans la littérature. Ce qui forcent les gestionnaires dans la plus part des cas à se baser uniquement sur leur expérience pour réaliser ou ajuster manuellement les ordonnancements produits par des heuristiques conçues pour résoudre des versions simplifiées du problème. Cette thèse tente de combler partiellement ces lacunes. Le chapitre 3 traite le problème d'ordonnancement de projet à ressources limitées POPRLTT avec des temps de transfert des ressources. Un temps de transfert est le temps nécessaire pour transférer une ressource du lieu d'execution d'une activité vers un autre. Ainsi, le temps de transfert d'une ressource dépend des lieux des activités à exécuter, ainsi que des caractéristiques des ressources à transférer. L'objectif dans un POPRLTT est la détermination des dates d'exécution des tâches en tenant compte des contraintes de préséance et de disponibilité des ressources et les temps de transfert des ressources. L'objectif est de minimiser la durée totale du projet. Nous proposons un nouvel algorithme génétique basé sur un opérateur de croisement de deux positions. L'étude expérimentale menée sur un grand nombre de problèmes test prouve que l'algorithme proposé est meilleur que les deux méthodes déjà existantes dans la littérature. Une généralisation du problème d'ordonnancement de projet à ressources limitées et des temps de transfert des ressources au contexte multi mode (POPRL=PMETT) est présentée au chapitre 4. Dans ce problème, nous supposons que la préemption est non autorisée, et les ressources utilisées sont renouvelables et non renouvelables, chaque activité a plusieurs modes d'exécution, et les relations de préséance sont de type dit début-fin sans décalage. L'objectif est de choisir un temps de début (ou de fin) et un mode d'exécution pour chaque tâche du projet, pour que la durée du projet soit minimisée tout en respectant les contraintes de préséance, de disponibilité de ressources et les temps de transfert. Au meilleur de notre connaissance, cette version du problème n'a jamais été abordée auparavant. Nous proposons une formulation mathématique de ce problème, ensuite nous présentons un algorithme génétique, que nous avons conçu pour résoudre les instances de grandes tailles. Pour tester les méthodes proposées nous développons des nouveaux ensembles de problèmes-tests pour le POPRL=PMETT, qui pourront être utilisés dans l'avenir pour mener des recherches dans ce domaine. Dans le chapitre 5, nous définissons une nouvelle généralisation du problème d'ordonnancement de projet à ressources limitées en considérant l'objectif de minimiser le coût total d'exécution du projet. Celui-ci est composé de deux éléments principaux: le coût direct des ressources à utiliser et les frais généraux qui ne dépendent pas de la quantité de ressources allouées, mais qui sont proportionnels à la durée du projet. Ce problème, que nous appelons Problème général d'allocation et de nivellement des ressources d'un projet (PGANRP) est très commun en pratique, mais très peu de recherche est consacrée à ce problème. Dans un PGANRP, nous devons simultanément déterminer les quantités des ressources à allouer au projet au cours de son exécution et réduire la variabilité de l'utilisation des ressources au minimum tout en essayant de terminer le projet à une date de fin acceptable. Les quantités des ressources à allouer au projet devraient permettre l'accomplissement du projet à cette date et devient une limite sur la disponibilité de ces ressources durant toute l'exécution du projet. Nous proposons, une formulation mathématique du problème et deux approches de recherche dans le voisinage pour les instances de grandes tailles.The resource-constrained project scheduling problem (RCPSP) consists of scheduling a set of activities or tasks using one or more resource types available in limited quantity. In the standard version of this problem, pre-emption is not allowed, precedence relations are of the no-lag, finish-to-start type, and the used resources are renewable meaning that the same resources quantity are available each time period. Solving this NP-hard optimization problem requires the determination of tasks execution date such that the project duration is minimized without using more than the available resource quantities. In the first chapter of this thesis, the research problem and research objectives are presented while chapter 2 reviews the literature and contributions to the RCPSP and some of its extended versions. More than 125 published papers are reviewed. These contributions are divided into 4 groups of contributions. Those proposing optimal solution methods, those developing lower bounds on the project duration, those proposing heuristic and approximate solution methods, and those extending the standard version of the problem in order to make it closer to the real-life problem. This literature review revealed that very few contributions explicitly take into consideration the time required to transfer resources between execution sites of the project. Only three such contributions are published and none of these three publication deal with the case where tasks have more than one execution mode. This review also revealed that the large majority of the published research deals with the problem where the objective is to minimize the duration of the project. However, in almost all real-life situations, the objective is to minimise the total cost of the project. That is why this thesis is dedicated to solve these neglected extensions of the RCPSP. Chapter 3 deals with the resource-constrained project scheduling problem with transfer times (RCPSPTT). Thus the goal in this case is to determine execution dates that allows for resources to be transferred between execution sites while respecting the precedence relations between these tasks as well as resources availability. A new genetic algorithm (GA) is developed to solve the RCPSPTT. This algorithm uses a new and efficient crossover operator. The chapter also study the performance of the proposed genetic algorithm and shows that it produces better results than the two previously published solution heuristics. It is to notice that the proposed GA considers renewable resource types and assume that tasks have only one execution mode. Chapter 4 deals with the multi-mode resource-constrained project scheduling problem with transfer times (MRCPSPTT). Thus, it extends the problem studied in the previous chapter to the multi-mode case under the assumptions of no pre-emption while using renewable and non-renewable resources. This problem has never been the subject of any published research before. An integer linear mathematical formulation of the problem is given as well as new genetic algorithm is developed to solve it. An extensive empirical analysis is then presented and shows that the proposed GA is able to produce the optimal solution for 529 test instances with 10, 20 and 30 activities. Chapter 5 introduces the generalized resource allocation and leveling problem (GRALP). This problem can be stated as follows. Given a set of project tasks to execute, their possible execution modes and precedence relations, an upper bound on the amount of resources that can be made available to the project, a project due date, the cost of resource utilization and the overhead cost; determine the execution date and mode for each task and the amount of resources to allocate to the project. The objective is to minimize the total project execution cost while respecting precedence constraints, project due date and not using more than the amount of resources that we decided to allocate to the project. Again we notice that this problem has never been the subject of any published research work. Chapter 5 presents an integer linear formulation of the problem, a neighborhood search solution heuristic, a genetic algorithm to solve it and an empirical experiment to evaluate the proposed heuristics showing the superiority of the proposed GA. Finally, the conclusions of the thesis and some propositions for future research are given

A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem

This paper presents a genetic algorithm for solving the resource-constrained project scheduling problem. The innovative component of the algorithm is the use of a magnet-based crossover operator that can preserve up to two contiguous parts from the receiver and one contiguous part from the donator genotype. For this purpose, a number of genes in the receiver genotype absorb one another to have the same order and contiguity they have in the donator genotype. The ability of maintaining up to three contiguous parts from two parents distinguishes this crossover operator from the powerful and famous two-point crossover operator, which can maintain only two contiguous parts, both from the same parent. Comparing the performance of the new procedure with that of other procedures indicates its effectiveness and competence. © 2013 Elsevier B.V. All rights reserved

A new mixed-integer modeling approach for capacity-constrained continuous-time scheduling problems

Nowadays, scheduling and resource management are increasingly important issues for organizations. Indeed, they do not only constitute an underlying necessity to make things work properly within the companies, but are and will always be more critical means to reduce costs and get competitive advantage in the market. Different approaches have been typically employed for these problems during the years. Among the others, linear programming techniques represent a valid tool that, despite applicable only to instances of limited dimension, offers an extremely flexible modeling opportunity, able to produce either optimal or approximate solutions of certified quality. In this spirit, the definition of suitable indicator variables and the use of particular constraints are proposed in the present work, with the aim of providing a useful basis for different mathematical models, taking into account scarce resources and other potential limitations. More in detail, a very well-known problem from the literature, the Resource Constrained Project Scheduling Problem, is investigated, and a new mixed-integer linear formulation is introduced, which treats time as a continuous variable. The considered model presents several advantages from the computational point of view, that are deeply studied and compared with those of one of the best methods recently developed in the same field. Extensive experiments reveal the good performances achieved by the proposed formulation over all the KPIs included in the analysis, thus motivating further applications to derived problems, such as the workforce planning and scheduling framework presented at the end of this dissertation

A dynamic scheduling model for construction enterprises

The vast majority of researches in the scheduling context focused on finding optimal or near-optimal predictive schedules under different scheduling problem characteristics. In the construction industry, predictive schedules are often produced in advance in order to direct construction operations and to support other planning activities. However, construction projects operate in dynamic environments subject to various real-time events, which usually disrupt the predictive optimal schedules, leading to schedules neither feasible nor optimal. Accordingly, the development of a dynamic scheduling model which can accommodate these real-time events would be of great importance for the successful implementation of construction scheduling systems. This research sought to develop a dynamic scheduling based solution which can be practically used for real time analysis and scheduling of construction projects, in addition to resources optimization for construction enterprises. The literature reviews for scheduling, dynamic scheduling, and optimization showed that despite the numerous researches presented and application performed in the dynamic scheduling field within manufacturing and other industries, there was dearth in dynamic scheduling literature in relation to the construction industry. The research followed two main interacting research paths, a path related to the development of the practical solution, and another path related to the core model development. The aim of the first path (or the proposed practical solution path) was to develop a computer-based dynamic scheduling framework which can be used in practical applications within the construction industry. Following the scheduling literature review, the construction project management community s opinions about the problem under study and the user requirements for the proposed solution were collected from 364 construction project management practitioners from 52 countries via a questionnaire survey and were used to form the basis for the functional specifications of a dynamic scheduling framework. The framework was in the form of a software tool fully integrated with current planning/scheduling practices with all core modelling which can support the integration of the dynamic scheduling processes to the current planning/scheduling process with minimal experience requirement from users about optimization. The second research path, or the dynamic scheduling core model development path, started with the development of a mathematical model based on the scheduling models in literature, with several extensions according to the practical considerations related to the construction industry, as investigated in the questionnaire survey. Scheduling problems are complex from operational research perspective; so, for the proposed solution to be functional in optimizing construction schedules, an optimization algorithm was developed to suit the problem's characteristics and to be used as part of the dynamic scheduling model's core. The developed algorithm contained few contributions to the scheduling context (such as schedule justification heuristics, and rectification to schedule generation schemes), as well as suggested modifications to the formulation and process of the adopted optimization technique (particle swarm optimization) leading to considerable improvement to this techniques outputs with respect to schedules quality. After the completion of the model development path, the first research path was concluded by combining the gathered solution's functional specifications and the developed dynamic scheduling model into a software tool, which was developed to verify & validate the proposed model s functionalities and the overall solution s practicality and scalability. The verification process started with an extensive testing of the model s static functionality using several well recognized scheduling problem sets available in literature, and the results showed that the developed algorithm can be ranked as one of the best state-of-the-art algorithms for solving resource-constrained project scheduling problems. To verify the software tool and the dynamic features of the developed model (or the formulation of data transfers from one optimization stage to the next), a case study was implemented on a construction entity in the Arabian Gulf area, having a mega project under construction, with all aspects to resemble an enterprise structure. The case study results showed that the proposed solution reasonably performed under large scale practical application (where all optimization targets were met in reasonable time) for all designed schedule preparation processes (baseline, progress updates, look-ahead schedules, and what-if schedules). Finally, to confirm and validate the effectiveness and practicality of the proposed solution, the solution's framework and the verification results were presented to field experts, and their opinions were collected through validation forms. The feedbacks received were very positive, where field experts/practitioners confirmed that the proposed solution achieved the main functionalities as designed in the solution s framework, and performed efficiently under the complexity of the applied case study

Étude de l’impact du chevauchement sur les performances de projets complexes d’ingénierie

RÉSUMÉ : Le chevauchement d’activités au sein d’un projet est une des techniques les plus répandues pour accélérer l’exécution d’un projet. Le chevauchement d’activités consiste à autoriser des activités qui traditionnellement s’exécutent de façon séquentielle à se chevaucher de sorte que les activités en aval débutent avant la fin des activités en amont en se basant sur des informations partielles. Plusieurs stratégies d’exécution de projets appliquées dans la pratique, telles que l’ingénierie simultanée dans les projets de développement de produits et la construction en « fast-tracking », reposent sur ce principe. Cette technique a montré ses preuves dans sa capacité à réduire la durée de projets, avec cependant plusieurs inconvénients. Le chevauchement peut causer des retouches du travail exécuté à partir d’information préliminaire et mener à des itérations. Ces retouches sont difficilement quantifiables et représentent une charge de travail et des coûts supplémentaires qui peuvent réduire ou annuler les bénéfices du chevauchement. Cela soulève la question de quand et de combien les activités devraient être chevauchées dans les projets industriels. Dans la pratique, les gestionnaires de projets ne possèdent pas d’outils d’aide à la décision pour répondre à cette question. Cette thèse s’intéresse ainsi au problème d’ordonnancement de projet avec chevauchement d’activités dans un contexte déterministe. Ce problème cherche à déterminer conjointement le meilleur calendrier en termes de coût ou de durée de projet et les décisions de chevauchement, c’est-à-dire quelles activités chevaucher et dans quelle mesure. Nous nous intéressons aux projets complexes caractérisés par des contraintes de disponibilité de ressources, des réseaux complexes d’activités, un nombre important d’activités et de couples d’activités qui peuvent se chevaucher. Les objectifs de cette thèse sont de modéliser, quantifier et analyser, dans le cas de projets complexes d’ingénierie, l’impact des décisions de chevauchement activités sur les performances de projet (durée et coût), en considérant un modèle réaliste de chevauchement. Cette thèse vise aussi à apporter une meilleure compréhension de ces choix dans les projets complexes et proposer des stratégies générales applicables en pratique. Les travaux réalisés dans le cadre de cette thèse sont articulés autour de trois articles publiés ou soumis à des revues scientifiques. Le premier article intitulé « Time-cost trade-offs in resource-constrained project scheduling problems with overlapping modes » (publié en 2014 dans International Journal of Project Organisation and Management) introduit un modèle de chevauchement d’activités basé sur des modes de chevauchement reliés aux jalons internes des activités et permet de modéliser de façon réaliste et flexible la relation entre durée de chevauchement et durée de retouche. Ce modèle est inséré dans une modélisation du problème de compromis durée-coût de l’ordonnancement de projets complexes avec contraintes de ressource et chevauchement d’activités sous la forme d’un programme linéaire en nombres entiers. Les durées de communication/coordination et de retouches sont considérées. Le problème est résolu avec une méthode exacte pour un exemple virtuel de projet. Les résultats illustrent les interactions entre le coût de projet, la durée du projet et les contraintes de ressource ainsi que leur influence sur le temps de résolution. Le second article intitulé « A Path Relinking-based Scatter Search for the Resource-Constrained Project Scheduling Problem » (soumis dans European Journal of Operational Research) introduit une métaheuristique dans la famille des recherches dispersées (« scatter search ») pour résoudre le problème standard RCPSP (« Resource-Constrained Project Scheduling Problem ») sans chevauchement. Cet algorithme utilise la méthode FBI (« Forward-Backward Improvement »), inverse la direction d’ordonnancement à chaque itération et est basé sur deux mécanismes novateurs. Premièrement, un PR (« Path Relinking ») bidirectionnel avec un nouveau mouvement opérant sur les distances entre activités est utilisé comme méthode de combinaison des solutions. Deuxièmement, une méthode d’amélioration est utilisée pour améliorer la qualité et la diversité des solutions de l’ensemble de référence. Une méthode avancée de paramétrage de l’algorithme utilisant une méthode de recherche locale a été développée pour déterminer les meilleures valeurs de ses paramètres. L’article montre que cette métaheuristique est capable de fournir des solutions de grande qualité avec des temps de calcul acceptables et appartient aux meilleures méthodes approchées existantes dans la littérature pour la résolution des instances virtuelles de projet de PSPLIB. Enfin, le troisième article intitulé « Influence of the project characteristics on the efficiency of activity overlapping » (soumis dans Computers & Operations Research) a pour principales contributions de quantifier et d’analyser l’influence de huit caractéristiques de projets sur l’efficacité du chevauchement pour diminuer la durée de projet. La réduction de la durée de projet est obtenue en résolvant le problème RCPSP avec et sans chevauchement. Deux méthodes de résolution sont développées pour résoudre le problème avec chevauchement. Une nouvelle méthode exacte basée sur un programme linéaire en nombres entiers avec modes de chevauchement et des techniques de propagation de contraintes est développée. La seconde méthode est une métaheuristique dérivée de la métaheuristique proposée dans le second article. Ces méthodes sont appliquées à un bassin de 3888 instances virtuelles de projets de 30 à 120 activités avec chevauchement. La première observation est que le chevauchement n’apporte aucune réduction dans près de 25% des cas. Une analyse statistique permet de distinguer l’influence de caractéristiques de projets sur l’efficacité du chevauchement et de montrer que la proportion de couples d’activités chevauchables qui sont sur le chemin critique et la sévérité des contraintes de ressources ont le plus d’influence sur la réduction de la durée de projet. Également, les résultats indiquent que certains principes généraux se dégagent pour les décisions de chevauchement. La meilleure stratégie devrait consister à chevaucher peu de couples d’activités chevauchables et de les chevaucher beaucoup. De plus, même si les activités sur le chemin critique sont plus susceptibles d’être chevauchées, les décisions de chevauchement dans un contexte de contraintes de ressource ne doivent pas uniquement être basées sur la criticalité des activités. Enfin, ces observations sont confrontées aux stratégies pratiques de chevauchement proposées dans la littérature. Ces travaux visent à contribuer au développement d’outils pour assister les gestionnaires de projet dans leurs décisions relatives au chevauchement d’activités. Les principales contributions scientifiques de ces travaux sont les suivantes. Premièrement, nous proposons une modélisation plus réaliste du problème d’ordonnancement de projet avec chevauchement d’activités. Deuxièmement, une nouvelle métaheuristique de type recherche dispersée (« scatter search ») performante pour le problème classique RCPSP est développée. Troisièmement, nous introduisons des méthodes de résolution exacte et approchée performantes pour le problème d’ordonnancement de projet avec chevauchement d’activités. La capacité des méthodes exactes à résoudre des problèmes d’ordonnancement pour des projets de grande taille avec contraintes de ressource étant limitée, cette thèse présente en effet à notre connaissance la première méthode approchée de type métaheuristique pour ce problème. Quatrièmement, ces travaux quantifient et analysent l’effet de huit caractéristiques de projet sur l’efficacité du chevauchement d’activités pour diminuer la durée d’un projet. Enfin, nous proposons des principes généraux pour aide les praticiens à prendre les meilleures décisions de chevauchement d’activités.----------ABSTRACT : Activity overlapping is one of the most employed strategies used to accelerate project execution. It consists in relaxing the sequential execution of dependent activities by allowing downstream activities to begin before receiving all the final information required from upstream activities. Several practical strategies, such as concurrent engineering and fast-tracking construction, are based on the concept of overlapping. Overlapping has been demonstrated to be powerful for reducing project makespan, but it has some drawbacks. Overlapping often causes additional reworks in downstream activities, as well as iterations of interdependent activities, that are difficult to quantify and represent additional workloads and costs. Such reworks may outweigh the benefices of overlapping in terms of cost and time. This raises the question of when and to which extent overlapping should be applied. In practice, project teams determine overlapping strategies on an ad hoc basis without always considering rework and interaction between activities. This thesis considers the project scheduling problem with activity overlapping in a deterministic context. This problem aims to jointly determine the best schedule in terms of cost and duration and the best overlapping decisions, namely which activities should be overlapped and to which extent. We focus on the complex projects characterized by constraints on resource availability, a complex network of activities, a large number of activities and a large number of couples of overlappable activities. The main objectives of this thesis are to model, quantify and analyze the impact of overlapping decisions on the project performances (cost and duration) in the case of complex industrial projects, by considering a realistic model of the overlapping process. This thesis also aims at providing a better understanding of these decisions in complex projects and at guiding planners in improving existing practices. The research undertaken in this thesis is divided into three papers published or submitted to international peer-reviewed scientific journals. The first paper titled « Time-cost trade-offs in resource-constrained project scheduling problems with overlapping modes » (published in 2014 in International Journal of Project Organisation and Management) proposes an overlapping process model based on overlapping modes related to activities’ internal milestones that is a realistic and flexible model of the relation between the amount of overlap and the amount of rework. This overlapping model is then enclosed in a model for the time-cost trade-offs in resource-constrained project scheduling problem with activity overlapping. The model is formulated as a linear integer programming model. The times and costs for communication/coordination and reworks are considered. The problem is solved with an exact method for an illustrative project instance. The results highlight the interactions between the project total cost, its makespan and the severity of the resource constraints and also show their influence on the computational time. The second paper titled « A Path Relinking-based Scatter Search for the Resource-Constrained Project Scheduling Problem » (submitted to European Journal of Operational Research) introduces a metaheuristic based on scatter search for solving the standard RCPSP (Resource-Constrained Project Scheduling Problem) without overlapping. This algorithm involves FBI (Forward-Backward Improvement), reversing the project network at each iteration and two new mechanisms. First, a bidirectional PR (path relinking method) with a new move is used as method for combining solutions. Second, a new improvement procedure is proposed in the reference set update method for enhancing the quality and the diversity of the reference set. An advanced parameter tuning method based on local search is employed. The paper shows that the proposed scatter search produces high-quality solutions in reasonable computational time and is among the best performing heuristic procedures in the literature for solving the instance of the PSPLIB benchmark.Finally, the main contributions of the third paper titled « Influence of the project characteristics on the efficiency of activity overlapping » (submitted to Computers & Operations Research) are to quantify and analyze the influence of eight project characteristics on the efficiency of activity overlapping for reducing project makespan. The reduction of the project makespan is obtained by solving the project scheduling problem with and without overlapping. Two methods have been developed for solving the problem with overlapping. First, we introduce a 0-1 integer linear programming model with overlapping modes and constraint propagation techniques as preprocessing. Second, we propose a metaheuristic based on the scatter search algorithm described in the second paper. These methods are applied on a set of 3888 project instances with overlapping composed of 30 to 120 activities. The first finding is that no reduction of the makespan is observed in about 25% of the projects of the benchmark. A statistical analysis is conducted to measure the effect of eight project parameters on the makespan gain. It reveals that the proportion of couples of overlappable activities on the critical path and the scarcity of the resource constraints have the highest influence on the makespan gain. In addition, general rules of thumb are derived from the analysis of the results. The best overlapping decisions should consist in overlapping only few couples of overlappable activities and to overlap them with a large degree of overlapping. Even though the activities on the critical path are more likely to be overlapped, overlapping decision should not rely solely on the criticality of the activities. The results are also compared to practical strategies for applying overlapping proposed in the literature. The main scientific contributions of these works with respect to the scientific literature and from the perspective of assisting project managers to choose the most appropriate overlapping decisions can be summarized as follows. First, we propose a more realistic model of the project scheduling problem with activity overlapping. Second, a new competitive metaheuristic based on scatter search has been developed. Third, we propose competitive exact and heuristic methods for solving the project scheduling problem with activity overlapping. Indeed, as the capacity of exact methods for solving project scheduling problem for large scale projects with resource constraints is limited, the metaheuristic developed in this thesis for this kind of problem is the first in the literature to our knowledge. Fourth, this thesis proposes to quantify and analyze the influence of eight project characteristics on the efficiency of activity overlapping for reducing project makespan. Finally, the findings of this work provide a better understanding of the overlapping decisions and should guide planners for the decisions on activity overlapping