An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective

ABSTRACT

Sustainable development is a problem-solving method that simultaneously accounts for the economic, environmental, and social impacts of actions. Decision-makers have recently recognised the need for sustainable development. Multiobjective optimisation is the most reliable technique to solve multiple sustainable development goals. However, there needs to be more research examining the role of interactive methods in multiobjective optimisation problems. To integrate machine learning and human interactions, this paper develops a new three-stage interactive algorithm in business analytics, called the interactive Nautilus-based algorithm, to address complex problems. To show the method’s applicability, this paper uses the proposed algorithm in three sustainable and resilient case studies. The selected cases are the river pollution problem, the urban transit network design problem, and the resilience problem. Moreover, the proposed algorithm is compared with two other algorithms for validation purposes. The results reveal that the proposed algorithm outperforms non-interactive algorithms by providing superior solutions.

KEYWORDS:

• Multiobjective optimisation
• machine learning
• interactive nautilus-based algorithm
• sustainable and resilient cases

Acknowledgments

We express our gratitude to the Ministry of Higher Education Malaysia for providing support to this research project through grant # FRGS/1/2019/STG06/UPM/02/1. Moreover, we would like to take this opportunity to express our appreciation to the editor, associate editor, and three anonymous reviewers for their time, effort, and expertise in reviewing our manuscript. Their valuable feedback, suggestions, and constructive criticism have helped to improve the quality and clarity of our work. Their contributions have been instrumental in making this manuscript a stronger and more valuable contribution to the field.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the Ministry of Higher Education Malaysia through the Fundamental Research Grant Scheme with reference code FRGS/1/2019/STG06/UPM/02/1. This work was also partly supported by ITIS GmbH–Universiti Putra Malaysia through International Grant (ITIS-UPM/2021/W164709/6380076-10065).