All Outputs (5)

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

A genetic algorithm with composite chromosome for shift assignment of part-time employees (2018)
Conference Proceeding
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)
Conference Proceeding
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)
Conference Proceeding
Pinheiro, R. L., Landa-Silva, D., Laesanklang, W., & Constantino, A. A. (2018). Using goal programming on estimated Pareto fronts to solve multiobjective problems.

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.