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All Journal International Journal of Enterprise Modelling
Aisyah Alesha
Tajik State University of Commerce, Tajikistan
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Quantum computing for production planning Fristi Riandari; Aisyah Alesha; Hengki Tamando Sihotang
International Journal of Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (467.321 KB) | DOI: 10.35335/emod.v15i3.50

Abstract

This research investigates the potential of quantum computing in production planning and addresses the limitations of conventional computing approaches. Traditional methods have been partially effective, but they struggle to solve complex optimization problems, accurately predict demand, and manage supply chains efficiently. The unique computational capabilities of quantum computing offer promising solutions to surmount these obstacles and revolutionize production planning processes. This study seeks to bridge the gap between quantum computing and production planning by analyzing the benefits, limitations, and challenges of its applicability in this field. It proposes customized algorithms and methodologies for leveraging quantum computation to enhance production planning efficiency, cost reduction, and decision-making processes. The research demonstrates the potential of quantum algorithms to minimize total production costs while appeasing demand and resource constraints through a numerical example and mathematical formulation. The results emphasize the advantages of quantum computing in terms of cost reduction, enhanced efficiency, and scalability. Comparisons with conventional methods illuminate the benefits and drawbacks of quantum computing in production planning. This research contributes to the development of novel strategies to improve production planning efficiency, lower costs, and enhance decision-making processes, allowing organizations to leverage quantum computing for optimized production operations
A hybrid approach for adaptive fuzzy network partitioning and rule generation using rough set theory: Improving data-driven decision making through accurate and interpretable rules Jonhariono Sihotang; Aisyah Alesha; Juliana Batubara; Sonya Enjelina Gorat; Firta Sari Panjaitan
International Journal of Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.041 KB) | DOI: 10.35335/emod.v16i1.54

Abstract

Data-driven decision making is vital in credit risk assessment and other areas. Complex datasets are hard to rule. We use adaptive fuzzy network partitioning, rough set theory, and rule generation to improve data-driven credit risk assessment. An adaptive fuzzy network partitioning algorithm is used to cluster the dataset. Each cluster instance receives fuzzy membership degrees. Next, rough set-based attribute reduction identifies credit risk assessment attributes inside each cluster. Finally, attributes are used to build accurate and understandable credit risk assessment criteria. A loan application dataset is used to test the suggested method. The results show successful loan application clustering and the creation of credit risk criteria for each cluster. Accurate predictions and interpretable rules improve credit risk assessment comprehension and decision-making. By merging adaptive fuzzy network partitioning, rough set theory, and rule generation, the hybrid methodology overcomes classic technique constraints. These methods create a comprehensive framework for credit risk assessment criteria that improves accuracy and interpretability. Financial institutions and credit providers may benefit from the approach. The proposed approach can be tested in multiple domains and extended to handle increasingly complicated datasets. Evaluating the methodology on real-world datasets and comparing it to existing methods can also reveal its practicality and efficacy. This research generates accurate and interpretable rules for data-driven credit risk assessment using a hybrid method. Adaptive fuzzy network partitioning, rough set theory, and rule generation can improve decision-making across domains
Co-Authors Firta Sari Panjaitan Fristi Riandari Hengki Tamando Sihotang Jonhariono Sihotang Juliana Batubara Sonya Enjelina Gorat