The allocation of human resources to projects is a critical decision-making process in project management, and it is crucial to the organization’s performance. The paper presents a study developed in an automotive company, focused on industrialization projects, where the project manager can manage more than one project simultaneously, being necessary to assign project managers to the projects.
We address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource or P-resource and 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.
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.
Nowadays, cost reduction, without compromising the quality and functionality of products, is a strategic issue for most companies. Therefore, companies must have adequate cost management systems and be prepared to implement cost reduction plans in order to maintain the desired levels of profitability. Many companies perform cost reduction initiatives in the form of a project, involving the necessary departments and employees to work towards a specific target cost or cost reduction goal. This research is on product cost optimization projects applied by companies that follow a lean approach. The studied company, a first-tier supplier of the automotive industry, developed a cost reduction methodology to be applied in the production phase named Ratio Project Planning (RPP). This methodology was analysed from a project management perspective. To turn it more effective and efficient, a dashboard that includes the performance indicators regarding RPP was developed and implemented. This tool supports decision-making, allowing real-time monitoring and better management of the whole process.
No abstract is provided for this article.
This paper presents a tool which goal is aiding the user to choose the best Decision Support Software between a set of Software residing in a database, according to the features desired, and using multicriteria decision methods. This application was developed using the C# programming language and allows to save in a file all user data, such as the set of Decision Software Tools under consideration and its features to be used afterwards.
The main objective of this research was to integrate scheduling techniques, documented in the literature, with Microsoft Project 2010, in order to help project managers’ deal with scheduling tasks in an easier and controlled way, and at the same time achieve better results. As far as we know, there is no information about the techniques or algorithms used by Microsoft Project to do the scheduling tasks so the possibility to select the algorithms to use gives a higher degree of control to the project manager. The initial hypothesis was that it is possible to get better results, concerning the project’s duration, using the studied techniques rather than the default scheduling technique used by Microsoft Project 2010, with an increased control.
No abstract is provided for this article.
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.
No abstract is provided for this article.
Fundos FEDER atraves do Programa Operacional Fatores de Competitividade – COMPETE e por Fundos Nacionais atraves da FCT –Fundacao para a Ciencia e Tecnologia, Projecto: FCOMP-01-0124-FEDER-022674.
In practice, project managers must cope with uncertainty, and must manipulate the allocation of their resources adaptively in order to achieve their ultimate objectives. Yet, treatments of the well-known ‘resource constrained project scheduling problem’ have been deterministic and static, and have addressed mostly unimodal activities. We present an approach to resource allocation under stochastic conditions for multimodal activity networks. Optimization is via dynamic programming, which proves to be demanding computationally. We suggest approximation schemes that do not detract significantly from optimality, but are modest in their computational requirements.
No abstract is provided for this article.
This paper presents the results of a case study conducted in the purchasing department of Bosch Car Multimedia Portugal, aiming to analyze and improve risk management practices within its project environment. Projects in this department are characterized by high complexity and uncertainty, making effective risk management essential. The study adopts a multi-method qualitative approach, integrating document analysis, direct observation, semi-structured interviews, and questionnaires. A comprehensive literature review established the theoretical foundation and guided the identification of best practices in project risk management. The field research revealed significant gaps in the structuring, standardization, and cultural integration of risk management processes. A comparative analysis between theoretical models and current practices led to the development of a tailored risk management framework, including a practical good-practices manual and a workshop format designed to promote internal engagement and capacity-building. This work contributes both theoretically—by validating literature-based models in an industrial setting—and practically, by offering replicable tools for similar departments in the automotive sector. The findings highlight the necessity of fostering a proactive risk culture to ensure the sustained implementation and effectiveness of the proposed measures.
We address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource or P-resource and 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.
