This study evaluated different alternatives using entropy-based TOPSIS and CoCoSo techniques, which are multi-criteria decision-making (MCDM) methods, to optimise the decision-making process in wind turbine selection. The study considered six criteria: price, rated power, rotor diameter, turbine size, noise emission value, and annual energy production. As a result of the analyses, Wind turbine-3 and Wind turbine-4 were the most suitable options. The findings revealed that criteria such as energy efficiency, sizeable nominal power, and minimisation of sound emission play a critical role in wind turbine selection. The findings of this study provide a more objective and analytical approach for decision-makers in wind energy projects. The Entropy-based TOPSIS and CoCoSo methodologies used in this study provide consistent and reliable results in evaluating alternatives. In particular, the fact that the CoCoSo method obtains similar rankings with TOPSIS shows that the methods can complement each other in multi-criteria decision-making problems.

Purpose: Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.Design/methodology/approach: This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.Findings: The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.Originality/value: This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.