Percorrer por autor "Varela, Maria Leonilde R."
A mostrar 1 - 3 de 3
Resultados por página
Opções de ordenação
- A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustmentPublication . Alves, Filipe; Varela, Maria Leonilde R.; Rocha, Ana Maria A.C.; Pereira, Ana I.; Leitão, PauloManufacturing activities and production control are constantly growing. Despite this, it is necessary to improve the increasing variety of scheduling and layout adjustments for dynamic and flexible responses in volatile environments with disruptions or failures. Faced with the lack of realistic and practical manufacturing scenarios, this approach allows simulating and solving the problem of job shop scheduling on a production system by taking advantage of genetic algorithm and particle swarm optimization algorithm combined with the flexibility and robustness of a multi-agent system and dynamic rescheduling alternatives. Therefore, this hybrid decision support system intends to obtain optimized solutions and enable humans to interact with the system to properly adjust priorities or refine setups or solutions, in an interactive and user-friendly way. The system allows to evaluate the optimization performance of each one of the algorithms proposed, as well as to obtain decentralization in responsiveness and dynamic decisions for rescheduling due to the occurance of unexpected events.
- Hybrid system for simultaneous job shop scheduling and layout optimization based on multi-agents and genetic algorithmPublication . Alves, Filipe; Varela, Maria Leonilde R.; Rocha, Ana Maria A.C.; Pereira, Ana I.; Barbosa, José; Leitão, PauloA challenge is emerging in the design of scheduling support systems and facility layout planning, both for manufacturing environments where dynamic adaptation and optimization become increasingly important on the efficiency and productivity. Focusing on the interactions between these two problems, this work combines two paradigms in sequential manner, optimization techniques and multi-agent systems, to better reflect practical manufacturing scenarios. This approach, in addition to significantly improve the quality of the solutions, enables fast reaction to condition changes. In such stochastic and very volatile environments, the manufacturing industries, the fast rescheduling, or planning, are crucial to maintain the system in operation. The proposed architecture was codified in MatLab and NetLogo and applied to a real-world job shop case study. The experimental results achieved optimized solutions, as well as in the responsiveness to achieve dynamic results for disruptions and simultaneously layout optimization
- Production scheduling using multi-objective optimization and cluster approachesPublication . Azevedo, Beatriz Flamia; Varela, Maria Leonilde R.; Pereira, Ana I.Production scheduling is a crucial task in the manufacturing process. In this way, the managers need to make decisions about the jobs production schedule. However, this task is not simple to perform, often requiring complex software tools and specialized algorithms to find the optimal solution. This work considers a multi-objective optimization algorithm to explore the production scheduling performance measure in order to help managers in decision making related to jobs attribution in a set of parallel machines. For this, five important production scheduling performance measures (makespan, tardiness and earliness time, number of tardy and early jobs) were combined into three objective functions and the Pareto front generated was analyzed by cluster techniques. The results presented different combinations to optimize the production process, providing to the manager different possibilities to prioritize the objectives considered.