Um algoritmo genético para programação de projectos em redes de actividades com complementaridade de recursos

Neste artigo abordamos a questão da alocação óptima dos recursos, mais especificamente, a análise da complementaridade dos recursos (recurso principal ou recurso-P e do recurso de suporte ou recurso-S) às actividades de um projecto. Neste artigo apresenta-se um algoritmo genético, baseado num alfabeto de chaves aleatórias, e analisa-se o procedimento para criar soluções a partir de um cromossoma.We address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource or P-resource and supportive resource or S-resource) to activities in a project. In this paper we present a Genetic Algorithm, based in a random keys alphabet, and we analyse the procedure to build solutions from a chromosome

Sequencing activities in a project network considering resource complementarity

Project management is a methodology widely used in organizations that believe in innovation and choose to organize their resources around projects. This paper presents new results and developments of a model that address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource and supportive resource) in a project. The concept of complementarity, which has been discussed based on an economic view, can be incorporated into the engineering domain as an enhancement of the efficacy of a “primary” resource (P-resource) by adding to it another “supportive” resource (S-resource). No replacement takes place. The gain achieved from such action is manifested in improved performance; e.g., shorter duration or improved quality, because of the enhanced performance of the P-resource. But such gain is usually achieved at an increased cost; namely the cost of the support resource(s). We developed a conceptual system capable of determining the ideal timing, and the ideal mixture of resources allocated to the activities of a project, such that the project is completed on time, if not earlier, with minimal cost. We present new computational results of a Genetic Algorithm, based in a random keys alphabet, with an optimized process that allowed reaching better results. The sequence of activities and the resource combinations for each pair activity/resources were obtained, respecting network constraints, showing the flexibility of the solution considering resources distribution and early resources release

Sequencing activities in a project network using resource complementarity model

The methodology of project management has been widespread in organizations of different functions and sizes. In this context, we address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource and supportive resource) in a project. We develop a conceptual system capable of determining the ideal timing, and the ideal mixture of resources allocated to the activities of a project, such that the project is completed on time, if not earlier, with minimal cost. In this paper we present new computational results of a Genetic Algorithm, based in a random keys alphabet to optimize the process to reach better results

A genetic algorithm for project scheduling in activity networks under resource complementarity

We address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource or P-resource and supportive resource or S-resource) to activities in a project. The concept of complementarity can be incorporated into the engineering domain as an enhancement of the efficacy of a "primary" resource (P-resource) by adding to it other "supportive" resources (S-resources). We developed a Genetic Algorithm capable of determining the ideal mixture of resources allocated to the activities of a project, such that the project is completed with minimal cost. This problem has a circularity issue that greatly increases its complexity. In this paper we present a constructive algorithm to build solutions from a chromosome that will be integrated in a Genetic Algorithm, which we illustrate by application to a small instance of the problem. The Genetic Algorithm is based on a random keys chromosome that is very easy to implement and allows using conventional genetic operators for combinatorial optimization problems. A project is formed by a set of activities. Each activity uses a specific set of resources, and it is also necessary to guarantee that there is no overlap in the time it takes to process activities in the same resource

A genetic algorithm with a quasi-local search for the job shop problem with recirculation

In this work we present a genetic algorithm for the job shop problem with recirculation. The genetic algorithm includes a local search procedure that is implemented as a genetic operator. This strategy differs from the memetic algorithm because it is not guaranteed that the local minimum is achieved in each iteration. This algorithm performs well with classical JSP and can be applied to the JSP with recirculation. The genetic algorithm includes specific knowledge of the problem to improve its efficiency. We test the algorithm by using some benchmark problems and present computational results