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Adriana Giret
Researcher at Polytechnic University of Valencia
Publications - 129
Citations - 2809
Adriana Giret is an academic researcher from Polytechnic University of Valencia. The author has contributed to research in topics: Multi-agent system & Scheduling (production processes). The author has an hindex of 23, co-authored 126 publications receiving 2345 citations. Previous affiliations of Adriana Giret include University of Valencia.
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Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm
TL;DR: In this paper, an energy-efficient model for flexible flow shop scheduling (FFS) is proposed, which is based on an energy efficient mechanism, and an improved, genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption.
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Sustainability in manufacturing operations scheduling: A state of the art review
TL;DR: A state-of-the-art review of the literature on sustainable manufacturing operations scheduling can be found in this article, where the relevant challenges and issues are discussed in depth, and subsequently urgent problems that must be solved through research in order to meet industry requirements are pointed out.
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Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization
TL;DR: A novel particle swarm optimization algorithm based on Hill function is presented to minimize makespan and energy consumption in dynamic flexible flow shop scheduling problems and shows that the proposed algorithm outperforms the behavior of state of the art algorithms.
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Holons and agents
Adriana Giret,Vicente Botti +1 more
TL;DR: A study of all the characteristics of the HS and the MAS approach is made in order to illustrate a comprehensive comparison of holons and agents, and to present a survey about the main research works in both areas.
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Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints
TL;DR: A multi-objective optimization model with the objective of minimizing energy consumption and makespan is formulated for a flexible job shop scheduling problem with transportation constraints and an enhanced genetic algorithm is developed to solve the problem.