Professor DARIO LANDA SILVA

Directed Perturbations for Efficient Learning of Surrogate Losses (2025)
Presentation / Conference Contribution
Cargan, T. R., Landa-Silva, D., & Triguero, I. (2025, June). Directed Perturbations for Efficient Learning of Surrogate Losses. Presented at International Joint Conference on Neural Networks (IJCNN 2025), Rome, Italy

Decision-Focused Learning (DFL) is a paradigm to learn neural network-based predictive models tailored to a specific optimisation problem. A key challenge for DFL methods lies in the non-differentiable nature of most optimisation problems. Recent sol... Read More about Directed Perturbations for Efficient Learning of Surrogate Losses.

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, September). UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment. Presented at 14th International Conference on Computational Logistics, ICCL 2023, Berlin, Germany

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.

Accelerated pattern search with variable solution size for simultaneous instance selection and generation (2022)
Presentation / Conference Contribution
Le, H. L., Neri, F., Landa-Silva, D., & Triguero, I. (2022, July). Accelerated pattern search with variable solution size for simultaneous instance selection and generation. Poster presented at Genetic and Evolutionary Computation Conference Companion (GECCO 2022), Boston, USA and online

The search for the optimum in a mixed continuous-combinatorial space is a challenging task since it requires operators that handle both natures of the search domain. Instance reduction (IR), an important pre-processing technique in data science, is o... Read More about Accelerated pattern search with variable solution size for simultaneous instance selection and generation.

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 10th 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.

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 ICORES 2021 - Proceedings of the 10th 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.

A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification (2020)
Presentation / Conference Contribution
Le, H. L., Landa-Silva, D., Galar, M., Garcia, S., & Triguero, I. (2020, July). A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification. Presented at 2020 IEEE Congress on Evolutionary Computation, CEC 2020, Glasgow, UK

© 2020 IEEE. Data preprocessing is a key stage in data mining that allows machine learning algorithms to obtain meaningful insights. Many preprocessing problems such as feature selection or instance selection can be modelled as optimisation/search pr... Read More about A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification.

Evolving Deep CNN-LSTMs for Inventory Time Series Prediction (2019)
Presentation / Conference Contribution
Xue, N., Triguero, I., Figueredo, G. P., & Landa-Silva, D. (2019, June). Evolving Deep CNN-LSTMs for Inventory Time Series Prediction. Presented at 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand

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.

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. An agent based modelling approach for the office space allocation problem. Presented at 2018 European Modeling and Simulation Symposium (EMSS 2018)

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.-S. (2018, September). Lookahead policy and genetic algorithm for solving nurse rostering problems. Presented at 4th International Conference on Machine Learning, Optimization and Data Science (LOD 2018)

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.

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. A genetic algorithm with composite chromosome for shift assignment of part-time employees. Presented at 2018 IEEE Congress in Evolutionary Computation (IEEE CEC 2018)

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. (2017, November). Fuzzy C-means-based scenario bundling for stochastic service network design. Presented at 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, HI, USA

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, January). Using goal programming on estimated Pareto fronts to solve multiobjective problems. Presented at 7th International Conference on Operations Research and Enterprise Systems (ICORES 2018), Funchal, Madeira, Portugal

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.

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, February). Towards collaborative optimisation in a shared-logistics environment for pickup and delivery operations. Presented at 6th International Conference on Operations Research and Enterprise Systems (ICORES 2017), Porto, Portugal

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, February). Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem. Presented at 6th International Conference on Operations Research and Enterprise Systems (ICORES 2017), Porto, Portugal

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. Approximate dynamic programming with combined policy functions for solving multi-stage nurse rostering problem. Presented at 3rd International Workshop on Machine Learning, Optimization and Big Data (MOD 2017)

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, June). Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem. Presented at 2017 IEEE Congress on Evolutionary Computation (CEC 2017), Donostia, Spain

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, July). Multi-start methods for the capacitated clustering problem. Presented at 12th Metaheuristics International Conference (MIC 2017), Barcelona, Spain

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.

ES-Rank: evolution strategy learning to rank approach (2017)
Presentation / Conference Contribution
Ibrahim, O. A. S., & Landa-Silva, D. (2017, April). ES-Rank: evolution strategy learning to rank approach. Presented at 32nd ACM Symposium on Applied Computing (SAC 2017), Marrakech, Morocco

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.

Professor DARIO LANDA SILVA's Outputs (62)