All Outputs (89)

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.

Towards Blockchain-based Ride-sharing Systems (2021)
Presentation / Conference Contribution
Vazquez, E., & Landa-Silva, D. (2021, February). Towards Blockchain-based Ride-sharing Systems. Presented at International Conference on Operations Research and Enterprise Systems, Online

Blockchain technology has been used in finance, health care, supply chain, and transport, with the main goals of improving security and eliminating the need for a third party to manage transactions in the system. In blockchain, smart contracts are us... Read More about Towards Blockchain-based Ride-sharing Systems.

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.

Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design (2020)
Journal Article
Jiang, X., Bai, R., Wallace, S. W., Kendall, G., & Landa-Silva, D. (2021). Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design. Computers and Operations Research, 128, Article 105182. https://doi.org/10.1016/j.cor.2020.105182

© 2020 Elsevier Ltd We present a method for bundling scenarios in a progressive hedging heuristic (PHH) applied to stochastic service network design, where the uncertain demand is represented by a finite number of scenarios. Given the number of scena... Read More about Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design.

Evolving Deep CNN-LSTMs for Inventory Time Series Prediction (2019)
Presentation / Conference Contribution
Xue, N., Triguero, I., Figueredo, G. P., & Landa-Silva, D. (2019). Evolving Deep CNN-LSTMs for Inventory Time Series Prediction. . https://doi.org/10.1109/CEC.2019.8789957

Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non-stationary characteristics of inventory data, the mo... Read More about Evolving Deep CNN-LSTMs for Inventory Time Series Prediction.

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.

A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food (2019)
Presentation / Conference Contribution
Xue, N., Landa-Silva, D., Figueredo, G. P., & Triguero, I. (2019, February). A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food. Presented at 8th International Conference on Operations Research and Enterprise Systems, Prague, Czech Republic

The taste and freshness of perishable foods decrease dramatically with time. Effective inventory management requires understanding of market demand as well as balancing customers needs and references with products’ shelf life. The objective is to av... Read More about A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food.

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.

A genetic algorithm with composite chromosome for shift assignment of part-time employees (2018)
Presentation / Conference Contribution
Xue, N., Landa-Silva, D., Triguero, I., & Figueredo, G. P. (2018). A genetic algorithm with composite chromosome for shift assignment of part-time employees.

Personnel scheduling problems involve multiple tasks, including assigning shifts to workers. The purpose is usually to satisfy objectives and constraints arising from management, labour unions and employee preferences. The shift assignment problem is... Read More about A genetic algorithm with composite chromosome for shift assignment of part-time employees.

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.

Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows (2018)
Journal Article
Curtois, T., Landa-Silva, D., Qu, Y., & Laesanklang, W. (2018). Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows. Euro Journal of Transportation and Logistics, 7(2), 151-192. https://doi.org/10.1007/s13676-017-0115-6

© 2018, The Author(s). An effective and fast hybrid metaheuristic is proposed for solving the pickup and delivery problem with time windows. The proposed approach combines local search, large neighbourhood search and guided ejection search in a novel... Read More about Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows.

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.

Towards collaborative optimisation in a shared-logistics environment for pickup and delivery operations (2017)
Presentation / Conference Contribution
Curtois, T., Laesanklang, W., Landa-Silva, D., Mesgarpour, M., & Qu, Y. (2017). Towards collaborative optimisation in a shared-logistics environment for pickup and delivery operations. In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems ICORES - Volume 1 (477-482)

This paper gives an overview of research work in progress within the COSLE (Collaborative Optimisation in a Shared Logistics Environment) project between the University of Nottingham and Microlise Ltd. This is an R&D project that seeks to develop opt... Read More about Towards collaborative optimisation in a shared-logistics environment for pickup and delivery operations.

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.