## 4 Discussion
### Decision Support to Select a Solution on the Pareto Front  
A critical challenge when employing MOO in FLP is selecting an optimal solution from the diverse set of Pareto front solution configurations. This study proposes an objective decision-support approach based on the proximity of solutions to the ideal and the nadir points on the Pareto front. The dual strategy of leveraging the ideal point and nadir point allows decision-makers to address different organizational priorities: the ideal point focuses on achieving the best possible trade-offs among conflicting objectives, while the nadir point provides a more risk-averse perspective by avoiding poorly performing solutions. The NSGA-II algorithm was chosen for this study because of its efficiency in handling complex multi-objective optimization problems and generating a diverse set of trade-off solutions. Genetic algorithms like NSGA-II are particularly well-suited for multi-objective optimization due to their ability to explore a large solution space and balance conflicting objectives effectively. While other algorithms can be used to produce a Pareto front, NSGA-II's robust handling of solution diversity and convergence makes it ideal for this study's objectives. Furthermore, the ideal point, nadir point, and ideal nadir vector used in our analysis can be applied to Pareto fronts generated by alternative algorithms, ensuring the generalizability of our decision-support approach. The introduction of the ideal nadir vector further enriches the decision-making process by offering a compromise solution that balances the proximity to both the ideal and nadir points. This approach not only guides decision-makers in objectively selecting suitable configurations but also enhances transparency by providing evidence-based criteria. One of the significant implications of this decision-support approach is its potential to reduce subjectivity in selecting solutions, a common challenge in multi-objective optimization. By grounding the selection process in well-defined reference points, the approach helps decision-makers align their choices with organizational goals, such as minimizing space utilization, improving worker well-being, and optimizing efficiency. However, it is important to acknowledge some limitations of this approach. For instance, the selection of the ideal and nadir points is inherently dependent on the specific problem context and the chosen objective functions. In scenarios with more complex objectives or additional constraints, the definition of these reference points may require further refinement. Additionally, while this study demonstrated the effectiveness of using the ideal and nadir points, alternative methods such as preference-based selection or incorporating decision-makers' subjective inputs could complement this approach in more nuanced scenarios. Furthermore, knowledge discovery using the flexible pattern mining (FPM) algorithm and visualizations such as parallel coordinates plots can offer additional insights into the characteristic features of Pareto front solutions. The identified human-comprehensible rules align with non-dominated trade-off solutions, enhancing decision-making in complex multi-objective scenarios. By integrating FPM-based visualization techniques, stakeholders can better understand the underlying patterns and trade-offs, leading to more informed decisions. In this study, we opted to maintain an objective approach by avoiding the application of specific weights to each objective. While weighting could be performed, it would introduce an element of subjectivity. Our goal was to allow each objective, i.e., worker well-being, area utilization, and distance walked, to be evaluated independently to reflect the inherent conflicts between them. For example, although a longer walking distance could potentially provide rest periods beneficial to worker well-being, it remains preferable to minimize walking distance to enhance productivity.