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Multi-criteria human resources planning optimisation using genetic algorithms enhanced with MCDA

Jurczak Marcin, Miebs Grzegorz, Bachorz Rafał A.

Operations Research and Decisions

2022

Vol. 32, No. 4

57--74

The main objective of this paper is to present an example of the IT system implementation with advanced mathematical optimisation for job scheduling. The proposed genetic procedure leads to the Pareto front, and the application of the multiple criteria decision aiding (MCDA) approach allows extraction of the final solution. Definition of the key performance indicator (KPI), reflecting relevant features of the solutions, and the efficiency of the genetic procedure provide the Pareto front comprising the representative set of feasible solutions. The application of chosen MCDA, namely elimination et choix traduisant la réalité (ELECTRE) method, allows for the elicitation of the decision maker (DM) preferences and subsequently leads to the final solution. This solution fulfils all of the DM expectations and constitutes the best trade-off between considered KPIs. The proposed method is an efficient combination of genetic optimisation and the MCDA method.

Automatic shape optimisation of hydro turbine components based on CFD

Eisinger R., Ruprecht A.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2002

Vol. 6, No 1

101-111

Since hydro turbines are designed individually according to the local situation, this requires a huge engineering effort. In order to reduce this effort, automatic optimisation tools are necessary. In this paper the shape optimisation of a turbine draft tube is shown. Different optimisation algorithms have been applied and will be discussed. From the used algorithms, the one based on approximated gradients seems to be the fastest.

Rozszerzona metoda optymalizacji według strategii adaptacyjnego przeszukiwania losowego

Jeżowski J., Bochenek R., Słoma R., Jeżowska A., Poplewski G.

Inżynieria Chemiczna i Procesowa

2001

T. 22, z. 3C

567--572

Praca dotyczy optymalizacji matematycznej przy zastosowaniu metody stochastycznej znanej pod nazwą adaptacyjnego przeszukiwania losowego (APL). Opisano w niej rozszerzenia i modyfikacje algorytmu Luusa-Jaakoli (LJ) z artykułu [1]. Wyniki obliczeń testowych świadczą, że zaproponowane modyfikacje zwiększają znacznie niezawodność obliczania optimum globalnego.

The work deals with mathematical optimisation with the use of stochastic method commonly referred to as adaptive random search (ARS). Extensions and modifications of the algorithm presented first in [1] by Luus and Jaakola have been developed. Results of tests proved that these modifications increase largely reliability of obtaining global optimum.