Outputs (6)

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.