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All Journal International Journal of Electrical and Computer Engineering Register: Jurnal Ilmiah Teknologi Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ComTech: Computer, Mathematics and Engineering Applications Jurnal Mantik Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) INFOKUM Journal of Computer Networks, Architecture and High Performance Computing International Journal of Basic and Applied Science Jurnal Media Informatika Journal Basic Science and Technology Jurnal Mandiri IT CEBONG Journal Journal of Computer Science and Research International Journal of Enterprise Modelling

Hengki Tamando Sihotang

Universitas Putra Abadi Langkat

Author-ID : 3432620

Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences 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

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

Data envelopment analysis for stochastic production and supply chain planning Hengki Tamando Sihotang; Patrisia Teresa Marsoit; Kouvelis Geovany Ortizan
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research presents a stochastic Data Envelopment Analysis (DEA) model for production and supply chain planning. The objective is to evaluate the efficiency of decision-making units (DMUs) in a system considering the stochastic nature of inputs and outputs. The proposed model incorporates uncertainty by assuming normal distributions for the stochastic variables. The model formulates a linear programming problem to maximize the efficiency scores of DMUs subject to constraints that ensure the efficiency of the system. The weights assigned to DMUs and input variables provide insights into their relative importance. A numerical example is presented to demonstrate the application of the model, and the results highlight the efficiency scores and weights for the DMUs. The findings contribute to improving decision-making in production and supply chain systems under uncertain conditions. The developed model offers a practical tool for evaluating efficiency and identifying areas for improvement in real-world systems. Further research can explore extensions and variations of the model to enhance its applicability in different contexts

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty Sihotang, Hengki Tamando; Michaud, Patrisius; Teresa, Patrys
International Journal of Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty is a crucial research area that addresses the challenge of making optimal decisions in the presence of distributional ambiguity. This research explores the development of methodologies and algorithms to handle uncertainty in variational inequalities, incorporating a distributionally robust framework that considers a range of possible distributions or uncertainty sets. By minimizing the worst-case expected performance across these distributions, the proposed approaches ensure robustness and optimality in decision-making under uncertainty. The research encompasses theoretical analysis, algorithm development, and empirical evaluations to demonstrate the effectiveness of the proposed methodologies in various domains, such as portfolio optimization and supply chain management. The outcomes of this research contribute to the advancement of robust optimization techniques, enabling decision-makers to make reliable and robust decisions in complex real-world systems

Co-Authors Achiriani, Tri Wahyuningtiyas Agustina Simangunsong Aisyah Alesha Aisyah Alesha Alrasyid, Wildan Anthoni Anggrawan Anthony Anggrawan Bambang Saras Yulistiawan Bosker Sinaga Budi Arif Dermawan Calvin Berkat Iman Hulu Chandra, Suherman Dadang Pyanto Delano, Aldrich Desi Vinsensia Dini Anggraini Dwiki Rivaldo Naidu Efendi, Syahril Elpridawati Purba Endang Mistaorina Laia Erwin Panggabean Fadiel Rahmad Hidayat Firmansyah Firmansyah Fransisco alexander Simbolon Fristi Riandari Guntur Syahputra Harapan Lumbantoruan Harapan Lumbantoruan Harpingka Fitria Br. Sibarani Harpingka Fitriai Br. Sibaran Hasugian , Paska Marto Herlina Zebua Herman Mawengkang Husain Husain Hutahaean, Harvei Desmon Jacob, Halburt Jane Irma Sari Jelita Sari Simanungkalit Jijon Raphita Sagala Joan De Mathew Jonhariono Sihotang Jonhariono Sihotang Judijanto, Loso Kouvelis Geovany Ortizan Laia, Endang Mistaorina Lemos, Sgarbossa Carlo Maria Santauli Siboro Martinus Ndruru Melda Agustina Nababan Michaud, Patrisius Mochamad Wahyudi Muhammad Rafli Muhammad Zarlis Mulianingtyas, RR Octanty Murni Marbun Normi Verawati Marbun Panjaitan, Firta Sari Patricius Michaud Felix Patrisia Teresa Marsoit Pilisman Buulolo Pujiastuti, Lise R. Mahdalena Simanjorang Rasenda, Rasenda Rifka Widyastuti, Rifka Ririn Pebrina Br. Marpaung Rizky A, Galih Prakoso Rizky, Galih Prakoso Rohit Gautama Roma Sinta Simbolon Rosulastri Purba Santiwati Sihotang Santoso, Heroe Sethu Ramen Sihotang , Jonhariono Sihotang, Jonhariono Sim, Lee Choi Simbolon, Agata Putri Handayani Simbolon, Roma Sinta Simbolon, Romasinta Siringoringo, Rimmar Siskawati Amri Sitio, Arjon Samuel Song , Jiang Lou Sri Devi Sulindawaty, Sulindawaty Tarisa Tarigan Teresa, Patrys Vina Winda Sari Vinsensia, Desi

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