All Outputs (77)

Local-global methods for generalised solar irradiance forecasting (2024)
Journal Article
Cargan, T. R., Landa-Silva, D., & Triguero, I. (2024). Local-global methods for generalised solar irradiance forecasting. Applied Intelligence, 54(2), 2225-2247. https://doi.org/10.1007/s10489-024-05273-9

For efficient operation, solar power operators often require generation forecasts for multiple sites with varying data availability. Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-serie... Read More about Local-global methods for generalised solar irradiance forecasting.

UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment (2023)
Presentation / Conference Contribution
Maaji, S. S., & Landa-Silva, D. (2023). UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment. In Computational Logistics. ICCL 2023 (467-481). https://doi.org/10.1007/978-3-031-43612-3_29

This paper proposes a model for unmanned aerial vehicles (UAV) grid-based coverage path planning, considering coverage completeness and energy consumption in complex environments with multiple obstacles. The work is inspired by the need for more effi... Read More about UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment.

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification (2020)
Journal Article
Le, H. L., Landa-Silva, D., Galar, M., Garcia, S., & Triguero, I. (2021). EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification. Applied Soft Computing, 101, Article 107033. https://doi.org/10.1016/j.asoc.2020.107033

© 2020 Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of... Read More about EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification.

An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes (2019)
Book Chapter
Pinheiro, R. L., Landa-Silva, D., Laesanklang, W., & Constantino, A. A. (2019). An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes. In Operations Research and Enterprise Systems (134-152). Springer Verlag. https://doi.org/10.1007/978-3-030-16035-7_8

© 2019, Springer Nature Switzerland AG. Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many-objective... Read More about An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes.

An agent based modelling approach for the office space allocation problem (2018)
Presentation / Conference Contribution
Dediu, A., Landa-Silva, D., & Siebers, P.-O. (2018). An agent based modelling approach for the office space allocation problem.

This paper describes an agent based simulation model to create solutions for the office space allocation (OSA) problem. OSA is a combinatorial optimization problem concerned with the allocation of available office space to a set of entities such as p... Read More about An agent based modelling approach for the office space allocation problem.

Lookahead policy and genetic algorithm for solving nurse rostering problems (2018)
Presentation / Conference Contribution
Peng, S., & Dario, L. (2018). Lookahead policy and genetic algorithm for solving nurse rostering problems. In n/a

Previous research has shown that value function approximation in dynamic programming does not perform too well when tackling difficult combinatorial optimisation problem such as multi-stage nurse rostering. This is because the large action space that... Read More about Lookahead policy and genetic algorithm for solving nurse rostering problems.

Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem (2018)
Journal Article
Martínez-Gavara, A., Algethami, H., & Landa-Silva, D. (2018). Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem. Journal of Heuristics, 25(4-5), 753-792. https://doi.org/10.1007/s10732-018-9385-x

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operat... Read More about Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem.

Fuzzy C-means-based scenario bundling for stochastic service network design (2018)
Presentation / Conference Contribution
Jiang, X., Bai, R., Landa-Silva, D., & Aickelin, U. (2018). Fuzzy C-means-based scenario bundling for stochastic service network design. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (1-8). https://doi.org/10.1109/SSCI.2017.8280905

Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solvi... Read More about Fuzzy C-means-based scenario bundling for stochastic service network design.

Using goal programming on estimated Pareto fronts to solve multiobjective problems (2018)
Presentation / Conference Contribution
Pinheiro, R. L., Landa-Silva, D., Laesanklang, W., & Constantino, A. A. (2018). Using goal programming on estimated Pareto fronts to solve multiobjective problems. In n Proceedings of the 7th International Conference on Operations Research and Enterprise Systems ICORES - Volume 1 (132-143). https://doi.org/10.5220/0006718901320143

Modern multiobjective algorithms can be computationally inefficient in producing good approximation sets for highly constrained many-objective problems. Such problems are common in real-world applications where decision-makers need to assess multiple... Read More about Using goal programming on estimated Pareto fronts to solve multiobjective problems.

An evolutionary strategy with machine learning for learning to rank in information retrieval (2018)
Journal Article
Ibrahim, O. A. S., & Landa-Silva, D. (2018). An evolutionary strategy with machine learning for learning to rank in information retrieval. Soft Computing, 22(10), 3171-3185. https://doi.org/10.1007/s00500-017-2988-6

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Learning to rank (LTR) is one of the problems attracting researchers in information retrieval (IR). The LTR problem refers to ranking the retrieved documents for users in search engines,... Read More about An evolutionary strategy with machine learning for learning to rank in information retrieval.

Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem (2017)
Presentation / Conference Contribution
Algethami, H., Landa-Silva, D., & Martinez-Gavara, A. (2017). Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem. In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems ICORES - Volume 1 (416-423)

The Workforce Scheduling and Routing Problem (WSRP) is a combinatorial optimisation problem that involves scheduling and routing of workforce. Tackling this type of problem often requires handling a considerable number of requirements, including cust... Read More about Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem.

Approximate dynamic programming with combined policy functions for solving multi-stage nurse rostering problem (2017)
Presentation / Conference Contribution
Shi, P., & Landa-Silva, D. (2017). Approximate dynamic programming with combined policy functions for solving multi-stage nurse rostering problem.

An approximate dynamic programming that incorporates a combined policy, value function approximation and lookahead policy, is proposed. The algorithm is validated by applying it to solve a set of instances of the nurse rostering problem tackled as a... Read More about Approximate dynamic programming with combined policy functions for solving multi-stage nurse rostering problem.

Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem (2017)
Presentation / Conference Contribution
Algethami, H., & Landa-Silva, D. (2017). Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem. In 2017 IEEE Congress on Evolutionary Computation (CEC 2017) - Proceedings (1771-1778). https://doi.org/10.1109/CEC.2017.7969516

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise total opera... Read More about Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem.

Multi-start methods for the capacitated clustering problem (2017)
Presentation / Conference Contribution
Martinez-Gavara, A., Campos, V., Landa-Silva, D., & Marti, R. (2017). Multi-start methods for the capacitated clustering problem. In Metaheuristics: Proceeding of the MIC and MAEB 2017 Conferences

In this work, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomi... Read More about Multi-start methods for the capacitated clustering problem.

Randomized heuristics for the Capacitated Clustering Problem (2017)
Journal Article
Martinez-Gavara, A., Landa-Silva, D., Campos, V., & Marti, R. (2017). Randomized heuristics for the Capacitated Clustering Problem. Information Sciences, 417, https://doi.org/10.1016/j.ins.2017.06.041

In this paper, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between random... Read More about Randomized heuristics for the Capacitated Clustering Problem.

Solving a large real-world bus driver scheduling problem with a multi-assignment based heuristic algorithm (2017)
Journal Article
Constantino, A. A., de Mendonca, C. F., de Araujo, S. A., Landa-Silva, D., Calvi, R., & dos Santos, A. F. (2017). Solving a large real-world bus driver scheduling problem with a multi-assignment based heuristic algorithm. Journal of Universal Computer Science, 23(5),

The bus driver scheduling problem (BDSP) under study consists in finding a set of duties that covers the bus schedule from a Brazilian public transportation bus company with the objective of minimizing the total cost. A deterministic 2-phase heuristi... Read More about Solving a large real-world bus driver scheduling problem with a multi-assignment based heuristic algorithm.

ES-Rank: evolution strategy learning to rank approach (2017)
Presentation / Conference Contribution
Ibrahim, O. A. S., & Landa-Silva, D. (2017). ES-Rank: evolution strategy learning to rank approach. In SAC '17: Proceedings of the Symposium on Applied Computing (944-950). https://doi.org/10.1145/3019612.3019696

Learning to Rank (LTR) is one of the current problems in Information Retrieval (IR) that attracts the attention from researchers. The LTR problem is mainly about ranking the retrieved documents for users in search engines, question answering and prod... Read More about ES-Rank: evolution strategy learning to rank approach.

A technique based on trade-off maps to visualise and analyse relationships between objectives in optimisation problems (2017)
Journal Article
Pinheiro, R. L., Landa-Silva, D., & Atkin, J. (2017). A technique based on trade-off maps to visualise and analyse relationships between objectives in optimisation problems. Journal of Multi-Criteria Decision Analysis, 24(1-2), 37-56. https://doi.org/10.1002/mcda.1604

Understanding the relationships between objectives in a multiobjective optimisation problem is important for developing tailored and efficient solving techniques. In particular, when tackling combinatorial optimisation problems with many objectives t... Read More about A technique based on trade-off maps to visualise and analyse relationships between objectives in optimisation problems.

Dynamic programming with approximation function for nurse scheduling (2016)
Journal Article
Shi, P., & Landa-Silva, D. (2016). Dynamic programming with approximation function for nurse scheduling. Lecture Notes in Artificial Intelligence, 10122, 269-280. https://doi.org/10.1007/978-3-319-51469-7_23

Although dynamic programming could ideally solve any combinatorial optimization problem, the curse of dimensionality of the search space seriously limits its application to large optimization problems. For example, only few papers in the literature h... Read More about Dynamic programming with approximation function for nurse scheduling.

An application programming interface with increased performance for optimisation problems data (2016)
Journal Article
Pinheiro, R. L., Landa-Silva, D., Qu, R., Constantino, A. A., & Yanaga, E. (in press). An application programming interface with increased performance for optimisation problems data. Journal of Management Analytics, 3(4), https://doi.org/10.1080/23270012.2016.1233514

An optimisation problem can have many forms and variants. It may consider different objectives, constraints, and variables. For that reason, providing a general application programming interface (API) to handle the problem data efficiently in all sce... Read More about An application programming interface with increased performance for optimisation problems data.