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Advisor(s)
Abstract(s)
Este artigo apresenta uma nova abordagem (MM-GAV-FBI), aplicável ao
problema da programação de projectos com restrições de recursos e vários modos de execução
por actividade, problema conhecido na literatura anglo-saxónica por MRCPSP. Cada projecto
tem um conjunto de actividades com precedências tecnológicas definidas e um conjunto de
recursos limitados, sendo que cada actividade pode ter mais do que um modo de realização. A
programação dos projectos é realizada com recurso a um esquema de geração de planos
(do inglês Schedule Generation Scheme - SGS) integrado com uma metaheurística. A
metaheurística é baseada no paradigma dos algoritmos genéticos. As prioridades das
actividades são obtidas a partir de um algoritmo genético. A representação cromossómica
utilizada baseia-se em chaves aleatórias. O SGS gera planos não-atrasados. Após a
obtenção de uma solução é aplicada uma melhoria local. O objectivo da abordagem é
encontrar o melhor plano (planning), ou seja, o plano que tenha a menor duração
temporal possível, satisfazendo as precedências das actividades e as restrições de
recursos. A abordagem proposta é testada num conjunto de problemas retirados da
literatura da especialidade e os resultados computacionais são comparados com outras
abordagens. Os resultados computacionais validam o bom desempenho da abordagem,
não apenas em termos de qualidade da solução, mas também em termos de tempo útil.
As the complexity of projects increases, the requirement of an organized planning and scheduling process is enhanced. The need for organized planning and scheduling of a construction project is influenced by a variety of factors (e.g., project size and number of project activities). To plan and schedule a construction project, activities must be defined sufficiently. The level of detail determines the number of activities contained within the project plan and schedule. So, finding feasible schedules which efficiently use scarce resources is a challenging task within project management. In this context, the well-known Resource Constrained Project Scheduling Problem (RCPSP) has been studied during the last decades. In the RCPSP the activities of a project have to be scheduled such that the makespan of the project is minimized. So, the technological precedence constraints have to be observed as well as limitations of the renewable resources required to accomplish the activities. Once started, an activity may not be interrupted. This problem has been extended to a more realistic model, the multi-mode resource constrained project scheduling problem (MRCPSP), where each activity can be performed in one out of several modes. Each mode of an activity represents an alternative way of combining different levels of resource requirements with a related duration. Each renewable resource has a limited availability such as manpower and machines for the entire project. The objective of the MRCPSP problem is minimizing the makespan. While the exact methods are available for providing optimal solution for small problems, its computation time is not feasible for large-scale problems. This paper presents a genetic algorithm-based approach (MM-GAV-FBI) for the multi-mode resource constrained project scheduling problem. The idea of this new approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to improve the initial solution. The chromosome representation of the problem is based on random keys. The schedule is constructed using a schedule generation scheme (SGS) in which the priorities of the activities are defined by the genetic algorithm. The experimental results of MM-GAV-FBI on project instances show that this approach is an effective method for solving the MRCPSP.
As the complexity of projects increases, the requirement of an organized planning and scheduling process is enhanced. The need for organized planning and scheduling of a construction project is influenced by a variety of factors (e.g., project size and number of project activities). To plan and schedule a construction project, activities must be defined sufficiently. The level of detail determines the number of activities contained within the project plan and schedule. So, finding feasible schedules which efficiently use scarce resources is a challenging task within project management. In this context, the well-known Resource Constrained Project Scheduling Problem (RCPSP) has been studied during the last decades. In the RCPSP the activities of a project have to be scheduled such that the makespan of the project is minimized. So, the technological precedence constraints have to be observed as well as limitations of the renewable resources required to accomplish the activities. Once started, an activity may not be interrupted. This problem has been extended to a more realistic model, the multi-mode resource constrained project scheduling problem (MRCPSP), where each activity can be performed in one out of several modes. Each mode of an activity represents an alternative way of combining different levels of resource requirements with a related duration. Each renewable resource has a limited availability such as manpower and machines for the entire project. The objective of the MRCPSP problem is minimizing the makespan. While the exact methods are available for providing optimal solution for small problems, its computation time is not feasible for large-scale problems. This paper presents a genetic algorithm-based approach (MM-GAV-FBI) for the multi-mode resource constrained project scheduling problem. The idea of this new approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to improve the initial solution. The chromosome representation of the problem is based on random keys. The schedule is constructed using a schedule generation scheme (SGS) in which the priorities of the activities are defined by the genetic algorithm. The experimental results of MM-GAV-FBI on project instances show that this approach is an effective method for solving the MRCPSP.
Description
Keywords
Gestão de projectos Planeamento Programação Metaheurísticas MRCPSP Project management Planning Scheduling Metaheuristics
Pedagogical Context
Citation
Publisher
Universidade Federal de Santa Catarina