Self-adaptive polynomial mutation in NSGA-II

A goal programming approach for agile-based software development resource allocation

The operation parameters and well layout parameters of aquifer thermal energy storage (ATES) system directly influence the thermal energy storage performance. How to optimize the parameters to obtain the optimal process scheme is of great significance to promote the field application of ATES. Taking the thermal storage performance of shallow aquifer as the optimization objective, this paper compares the influence degrees of key factors on thermal storage performance by means of gray correlation analysis (GCA), and prepares the optimal thermal storage scheme by using the multi-objective optimization method. The following results are obtained. First, the great difference between inlet temperature and aquifer weakens the thermal storage capacity of the system, while the thermal interference between thermal storage wells of the same type is favorable for thermal storage capacity instead. Second, aquifer thickness and well number have a greater impact on the thermal loss rate, while injection rate and well spacing have a significant influence on the thermal recovery rate. The inlet temperature has the least effect on both of them. Third, the optimal thermal storage scheme is the single well system with inlet temperature of 25 °C, aquifer thickness of 106.597 m and injection rate of 30 kg/s. In conclusion, the influence degrees of the key parameters on thermal loss rate and thermal recovery rate are different, so in order to improve the thermal storage performance, equilibrium optimization is necessary between both of them. In addition, the optimization scheme effectively expands the thermal storage volume, and reduces the heat loss while improving the thermal recovery, with thermal loss rate and thermal recovery rate of the whole system optimized by 12.69% and 3.19% respectively on the basic case, which can provide a reference for the rational design of ATES system. 

Optimizing the thermal energy storage performance of shallow aquifers based on gray correlation analysis and multi-objective optimization

Managing software development processes remains a significant challenge despite tool support. To remedy this situation, we propose better use of data collected in management support systems, a lightweight approach to planning inspired by agile approaches, and the assumption that constant change is inevitable. This approach lies at the heart of the described solution. The paper presents a model for planning tasks in an IT project. Its purpose is to support the management of the software development process. It allows planning work in the project, considering changes and learning new requirements, detecting defects by the testing team and by the client, and their repair. It uses a model that classifies project tasks and calculates task statistics. They control stateful and recurring task generators, creating new implementation tasks and defects during the project. Linking the types of tasks created to the roles of project team members allows for planning the composition of the project team. Two experiments demonstrate the model's replication and prediction capabilities using data from real IT software projects. 

Task Planning Model of Software Process

Un agente inteligente (AI) utiliza la inteligencia artificial (IA) para dialogar con los usuarios; las incidencias son interrupciones que surgen y que impiden a los usuarios hacer uso de las tecnologías de la información (TI); América Latina tiene un 15. 5% de las respuestas a incidencias de clientes. En Europa, cada año las incidencias de seguridad de TI se han visto incrementado desde 2019 en un 41%, las cuales se clasifican como de gravedad Alta y Muy Alta. El propósito de este estudio fue implementar un AI para mejorar la Gestión de Incidencias (GI), reducir el número de incidencias no resueltos, reducir el tiempo de resolución y aumentar la satisfacción de los usuarios. Para lograr este objetivo, se siguió un enfoque cuantitativo y un diseño preexperimental; se utilizó cuestionarios para el recojo de datos y, a continuación, todos los datos se sometieron a un análisis estadístico para validar la hipótesis. expertos verificaron la validez de los instrumentos, luego se obtuvo el índice de confiabilidad de los instrumentos utilizados. Además, se utilizó el marco de trabajo Scrum para el desarrollo de la solución inteligente. El logro de la implementación se obtuvo a través de la incorporación de diversas tecnologías como Dialogflow, Webhook, PostgreSQL; finalmente, se obtuvo un AI capaz de atender los incidentes reportados por los usuarios, asignando la tarea de manera automatizada. El desarrollo de este estudio permitió minimizar el número de incidencias no resueltas al día en un 14%; se redujo el tiempo de resolución de incidencias en un 63% y aumentó la satisfacción de los usuarios a "Satisfecho" al 43,3% y a "Muy Satisfecho" al 57,7%. Finalmente, se puede concluir que este trabajo proporciona una importante contribución para futuras implantaciones de diseños o desarrollos relacionados con la automatización de la GI mediante agentes inteligentes.

Agente inteligente para la gestión de incidencias

Agile software development methodologies are used to meet the changing needs in the market. The most popular framework among these methodologies is the Scrum framework. In Scrum planning, the assignment of user stories to sprints requires the consideration of multiple objectives to use the limited resources more effectively. In this paper, a multi-objective mixed-integer programming model is developed which considers three objectives: maximizing the sprint capacity usage, maximizing the assignment of user stories with high priority to primary sprints, and maximizing the assignment of affine user stories to the same sprint. The aim is to contribute to both theory and practice of Scrum planning considering multiple objectives. Additionally, different from the existing literature of Scrum planning, alternative user stories are also taken into account. The proposed model is applied to the small, medium, and big-sized instances of the problem taken from a real-life system. Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strong Pareto Evolutionary Algorithm (SPEA2) are used as heuristic approaches since big-sized instances of the problem could not be solved using optimization approaches. To analyze the performances of these algorithms, Hypervolume (HV), Epsilon (ϵ), Generational Distance (GD), Inverted Generational Distance (IGD), Inverted Generational Distance Plus (IGD+), and Spread (Δ) indicators are used. Results showed that NSGA-II performs better than SPEA2 according to ϵ indicator for big-sized instance. On the other hand, SPEA2 performs better than NSGA-II according to HV, GD, IGD, IGD+, and Δ indicators. However, the results are very close to each other for HV, ϵ, IGD, and IGD+ indicators. In conclusion, both algorithms can be used to deal with the multi-objective Scrum planning problem. 

A multi-objective agile project planning model and a comparative meta-heuristic approach

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A multi-objective agile project planning model and a comparative meta-heuristic approach