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International Journal of Enterprise Modelling
Published by International Enterprise Integration Association
ISSN : 16939220     EISSN : 29878713     DOI : https://doi.org/10.35335/emod
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation
The International Journal of Enterprise Modelling serves as a venue for anyone interested in business and management modelling. It investigates the conceptual forerunners and theoretical underpinnings that lead to research modelling procedures that inform research and practice.
Arjuna Subject : Matematika - Matematika Terapan
Articles 5 Documents
 1 
Search results for , issue "Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing" : 5 Documents clear
Quantum computing for manufacturing and supply chain optimization: enhancing efficiency, reducing costs, and improving product quality Weinberg Jiang Chen; Griffin Schworm Marcus; D'Souza Leesburg
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 (537.398 KB) | DOI: 10.35335/emod.v15i3.48

Abstract

The research explores the application of quantum computing to manufacturing and supply chain optimization in an effort to increase productivity, reduce costs, and improve product quality. Quantum algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), are developed and evaluated to solve complex optimization problems in these domains. Quantum computing approaches are contrasted with traditional optimization techniques to demonstrate the potential advantages of quantum algorithms in terms of solution quality and working time efficiency. Practical implementation considerations of data availability, algorithm scalability, and system integration are also discussed. This research shows that quantum algorithms can effectively optimize production scheduling, resource allocation, and supply chain management, resulting in shorter production schedules and improved operational performance. This research recognizes the limitations of current quantum hardware, the complexity of the problem domain, and the difficulty of implementation. Despite these limitations, this research lays the foundation for further investigation and innovation in quantum computing for manufacturing and supply chain optimization, highlighting the potential for long-term transformative effects on industrial operations.
Quantum computing and supply chain optimization: addressing complexity and efficiency challenges Ming-Lang Tun Hwang; Wi-Lang Collin; Lee Sen Wang-xu
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 (494.919 KB) | DOI: 10.35335/emod.v15i3.49

Abstract

Quantum computing is used to address supply chain optimization complexity and efficiency. Multiple locations, time periods, transportation expenses, facility opening costs, production capacity, and demand fulfillment requirements complicate supply chains. Supply chain optimization's complexity and huge solution areas challenge traditional optimization methods. Quantum algorithms can efficiently explore bigger solution areas in quantum computing. Starting with problem identification, this research reviews quantum computing and supply chain optimization literature. The supply chain optimization problem is modeled mathematically to incorporate transportation, facility opening, production, and cost. Binary choice factors and constraints ensure demand fulfillment, facility capacity limitations, and flow balance. The mathematical theory is applied numerically. The example addresses three locations, two time periods, transportation costs, demand amounts, production capacity, and facility opening costs. A proper optimization solver optimizes the decision variables to reduce total cost while meeting demand and making efficient supply chain decisions. The supply chain optimization model reduces costs and informs transportation, facility opening, and production decisions. The numerical example shows how quantum computing may optimize supply chain topologies and reduce costs. The study explains the findings, highlights gaps in the literature, and stresses the need for more research to bridge theory and practice. This study advances supply chain optimization with quantum computing. It shows how quantum computing might improve supply chain network decision-making, efficiency, and cost.
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
Quantum-inspired fuzzy genetic programming for enhanced rule generation in complex data analysis Patrisius Michaud Felix Marsoit
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 (452.685 KB) | DOI: 10.35335/emod.v15i3.51

Abstract

Rule generation in complex data analysis tasks poses challenges in terms of accuracy and interpretability. This research proposes a novel approach called Quantum-Inspired Fuzzy Genetic Programming (QIFGP) that integrates concepts from fuzzy logic, genetic programming, and quantum-inspired computing to address these challenges. The QIFGP model enhances the exploration of the solution space, increases the diversity of generated rules, and improves the accuracy and interpretability of the generated rules. The model is applied to a credit risk assessment problem, and the results are compared with traditional fuzzy logic-based approaches and genetic programming without quantum-inspired features. The experimental results demonstrate that the QIFGP model outperforms the baseline methods in terms of accuracy, achieving an accuracy of 87.5%. The generated rules exhibit a high level of interpretability, providing linguistic labels that capture meaningful relationships between the input features and risk classes. The incorporation of quantum-inspired features enables efficient exploration of the solution space while maintaining computational efficiency. The generalizability and robustness of the QIFGP model are demonstrated through consistent performance across multiple experiments and datasets. The QIFGP model offers a promising approach for enhanced rule generation in complex data analysis tasks, with potential applications in various domains where accurate and interpretable rule generation is crucial.
Quantum computing approach in uncertain data optimization problem for vehicle routing problem Patrisia Teresa Marsoit; Liu Wang Zhang; Deodoro Lakonde; Firta Sari Panjaitan
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 (519.285 KB) | DOI: 10.35335/emod.v15i3.52

Abstract

This research addresses the Vehicle Routing Problem (VRP) with uncertain data and proposes a novel approach using quantum computing techniques. The problem involves optimizing vehicle routes considering uncertain customer demands, time windows, and vehicle capacities. We formulate the problem mathematically and develop an algorithmic framework to tackle it. The approach incorporates multiple scenarios based on the uncertainty distribution and selects the one with the minimum cost to optimize the vehicle routes. Through a numerical example, we demonstrate the effectiveness of the proposed approach in generating optimal routes that minimize the total distance traveled by the vehicles. The results highlight the solution quality, adaptability to uncertainty, and potential benefits in terms of cost reduction and resource utilization. While the computational efficiency of quantum computing approaches is a consideration, this research provides a promising direction for addressing uncertain optimization problems in logistics and transportation. Future research should focus on scalability and refinement of the algorithm to further enhance its applicability in real-world scenarios.

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