طراحی و مدلسازی سیستم‌های خودتطبیق کسب و کار الکترونیک با بکارگیری محاسبات ارگانیک
محورهای موضوعی : مدیریت کسب و کار
محبعلی رهدار
1
(استادیار گروه مهندسی صنایع، دانشکده مهندسی نیکبخت، دانشگاه سیستان و بلوچستان، سیستان و بلوچستان، ایران.)
مصطفی درخشیده
2
(مهندسی صنایع، مهندسی شهید نیکبخت، دانشگاه سیستان و بلوچستان، زاهدان، ایران)
کلید واژه: کسب و کار الکترونیک, محاسبات ارگانیک, خودتطبیقی, زیر ساخت‌های چند عاملی,
چکیده مقاله :
توسعه روزافزون فناوری اطلاعات و شکل‌گیری اقتصاد دیجیتالی، باعث شده تا اینترنت به عنوان بستری موفق برای پوشش همگانی کسب و کار محسوب گردد. با توجه به دامنه گسترده اینترنت و افزایش پیچیدگی سیستم‌ها، محاسبات و کنترل کسب و کار به صورت مکانیزم انسانی با خطا و اتلاف زمان روبرو شده است. هدف این پژوهش برطرف کردن پیچیدگی‌های کسب و کار الکترونیک است که انسان کمترین دخالت را در آن داشته باشد. روش تحقیق طراحی و مدلسازی مکانیزم‌های خودتطبیقی بوسیله نرم افزار انی‌لاجیک می‌باشد. محاسبات ارگانیک و عامل‌های هوشمند برای هماهنگی بین اجزای کسب وکار الکترونیک جهت افزایش کارایی و تعامل بهتر بکار گرفته شده و همچنین برای پیاده‌سازی خودتطبیقی از سیستم تحت نظارت/ کنترل توزیع شده استفاده شده است. جامعه آماری و دادهای مربوط از شرکت فروشگاه مجازی فروش محصول بهداشتی جمع‌آوری شده که این اطلاعات شامل میزان درخواست‌ها، میزان موجودی، ظرفیت انبار، هزینه‌های مربوط به تولید و فروش می‌باشد. تعداد 500 نمونه به روش تصادفی از کل درخواست محصول انتخاب شده و داده‌ها به روش میانگین تصادفی تجزیه و تحلیل گردید. در این تحقیق کسب و کار الکترونیک از طرق دو سناریوی خودتطبیق و غیرخودتطبیق توسط نرم افزار انی‌لاجیک که توسط شرکت هیولت پاکارد در سال 2000 منتشر شد شبیه‌سازی شده و نتایج آن از حیث هزینه با هم مقایسه شد که در سناریوی خودتطبیق نتایج بهتری نسبت به مدل دیگر بدست آمد. برتری سیستم‌های ارگانیک در برابر بقیه مدل‌ها، ویژگی‌های بلادرنگ و خودمختار آن می‌باشد که در این مدل به خوبی نشان داده شده است.
The increasing development of information technology and the formation of the digital economy have made the Internet a successful platform for universal business coverage. Due to the vast scope of the Internet and the increasing complexity of systems, computing and business control as a human mechanism has faced errors and wasted time. The purpose of this study is to solve the complexities of e-business in which human beings have the least involvement. The research method is design and modeling of self-adaptive mechanisms by Eni Logic software. Organic calculations and intelligent agents are used to coordinate the components of e-business to increase efficiency and better interaction, and also to implement self-adaptation of the system under Distributed monitoring / control is used. Statistical community and related data were collected from the virtual store company selling health products, which includes the amount of requests, inventory, storage capacity, production and sales costs. A total of 500 samples were randomly selected from the total product request and the data were analyzed by random sampling method. In this study, e-business was simulated through two scenarios of self-adaptation and non-adaptation by Eni Logic software published by Hewlett-Packard in 2000, and the results were compared in terms of cost. Obtained in another model. The superiority of organic systems over other models is its real-time and autonomous features, which are well demonstrated in this model.
Azar, A., Saranj, A., Sadeghi Moghadam, A., Rajabzadeh, A., & Moazzez, H. (2018). The Agent-based modeling of stockholders’ behavior in Iranian capital market. . Financial Research Journal, 20(2), 130-150. (In Persian)
Bulbulian, M., & Ghodsi, A. (2017). Familiarity with software and review of some case studies Hakim Sabzevari University]. (In Persian)
Cardoso, H. L., Schaefer, M., & Oliveira, E. (1999). A multi-agent system for electronic commerce including adaptive strategic behaviours In Portuguese Conference on Artificial Intelligence, Berlin, Heidelberg.
Chaharsooqi, S. K., & Taheri, Z. (2016). Providing a Negotiation Mechanism for Multi-Broker Systems in Automated Electronic Exchanges: Based on Methods of Analyzing Buyer-Seller Behavior in Microeconomics. Journal of Modeling in Engineering, 14(46). (In Persian)
Dai, F. T., Teo, S., & Yuan Wang, K. (2016). Network Marketing Businesses and Chinese Ethnicity Immigrants in Australia, . Journal of Small Business Management, 1-16. https://doi.org/10.1111/jsbm.12244
Fakhrzad, M. B., & Rahdar, M. A. (2016). Optimization of hybrid robot control system using artificial hormones and fuzzy logic. Journal of Intelligent & Fuzzy Systems, 30(3), 1403-1410.
Groenewald, D., & Van Vuuren, J. J. (2007). A critical analysis of the influence of start-up factors in small businesses and entrepreneurial ventures in SA. Professional Accountant, 7(1), 269-280.
Haghighi Nasab, M., & Taghavi, S. S. (2012). Factors affecting the spread of e-business in Iranian organizations. Information Technology Management, 4(10), 25-40. (In Persian)
Haghighi Nasab, M., & Taqwa, S. S. (2012). Factors affecting the spread of e-business in Iranian organizations. Information Technology Management, 4(10), 25-40. (In Persian)
Hamichi, S., Guessoum, Z., & Mangalagiu, D. (2009). A multi-agent system for adaptive production networks. Springer.
Helal, M. (2017). An investigation of the use of social media for e-commerce amongst small businesses in Saudi Arabia [Ph.D. Thesis The University of Salford]. Salford, UK.
Hoor Ali, M., Montazeri, A., & Fathian, M. (2011). Designing a Smart Business Agent in Supply Chain Management in E-Commerce The First National Conference of Computer and Information Technology Scholars, Tabriz. (In Persian)
Hosseini, M. (2001). Maintenance planning and inventory control system, process and forecasting models: Introduction to PMS. Fresh Air Publications. (In Persian)
Jacyno, M. (2010). Self-organising Agent Communities for Autonomic Computing University of Southampton].
Khaloo, P., Mahan, F., Khosh Ahwal, A., & Khatibi, R. (2011). Designing a Smart Business Agent in Supply Chain Management in E-Commerce The First National Conference of Computer and Information Technology Scholars, Tabriz, University of Tabriz. (In Persian)
Khatami Firoozabadi, S. M. A., Askari Mehr, M., & Mortaz Hijri, F. (2018). E-business development strategies in the context of facilitating and enhancing the business environment. Quarterly Journal of Economic Research and Policy, 18(68), 253-290. (In Persian)
Koberg, E., & Longoni, A. (2019). A systematic review of sustainable supply chain management in global supply chains. Journal of Cleaner Production, 207, 1084-1098.
Lamshoft, K., Altschaffel, R., & Dittmann, J. (2017). Adapting Organic Computing Architectures to an Automotive Environment to Increase Safety & Security. Automotive-Safety & Security Sicherheitund Zuverlässigkeit für automobile Informationstechnik.
Memari, M., & Amerian, A. (2010). Intelligence of various e-commerce processes. Bimonthly of Artificial Intelligence and Instruments, 4(1). (In Persian)
Mir Vahadi, S., Toghraei, M. T., & Astaneh, M. (2020). Designing a gaming model in web-based entrepreneurial businesses. Scientific Journal of Intelligent Business Management Studies, 9(33), 39-60. (In Persian)
Mohammadian, M., Rouhani, A. R., Hashemzehi, A., & Karimian, M. (2013). Factors influencing the selection of small and medium e-business models in Iran. Quarterly Journal of Information Technology Management Studies, 3(12), 97-122. (In Persian)
Mousavi, F., Fathian, M., & Memari, M. (2006). Smart Factors in E-Commerce. Tadbir Monthly, 17(177). (In Persian)
Omidvar, A. (2014). Provide a framework for deploying email-based marketing in e-business [Master Thesis, Shahid Beheshti University]. (In Persian)
Rahmanzadeh, A., & Nazemi, I. (2013). Review of proposed models for self-organized organizations in multi-factor systems 8th Annual Conference of Science and Technology Association, Mashhad, Khavaran Institute of Higher Education. (In Persian)
Razalli, N., Sin, M., & Aizat, M. (2018). Internal and external key success factors of Business Process Re-Engineering (BPR): effects on the Islamic banks performance. International Journal of Engineering & Technology, 458-461.
Reichhuber, S. (2019). Knowledge Self-Adaptive Multi-Agent Learning. Knowledge Self-Adaptive Multi-Agent Learning, 507-515. (In: Draude, C., Lange, M. & Sick, B. (Hrsg.), INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge). Bonn: Gesellschaft für Informatik e.V.)
Reza, M. G., Hassani Saadat, H., & Erfanian Khanzadeh, H. (2015). Identifying Key Success Factors of Knowledge Management System. Technology Development Quarterly, 12(45), 26-35. (In Persian)
Seljughi, T. (2004). Presenting a Conceptual Model for Measuring the Performance of Electronic Supply Chain Based on Cloud Processing [M.Sc. Thesis], Mehr Alborz Educational Institute. (In Persian)
Tomforde, S. (2017). Technical Systems for Survival in the Real World (1st ed.). Cham Springer International Publishing, Birkhäuser.
Yari Nejad, M. (2012). Presenting a model of achieving self-adaptation in the customer relationship management system [Master Thesis, Shahid Beheshti University]. (In Persian)
Zhang, Y., Qian, C., Lv, J., & Liu, Y. (2017). Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor. IEEE Transactions on Industrial Informatics, 13(2), 737-747.
Zhu, Z., & Bush, A. (2020). The effects of e-business process in supply chain operations: Process component and value creation mechanisms. International journal of information management, 50, 273-285.
Zijm, H., Klumpp, M., Heragu, S., & Regattieri, A. (2019). Operations, Logistics and Supply Chain Management: Definitions and Objectives. Springer International Publishing, Operations, Logistics and Supply Chain Management
_||_Azar, A., Saranj, A., Sadeghi Moghadam, A., Rajabzadeh, A., & Moazzez, H. (2018). The Agent-based modeling of stockholders’ behavior in Iranian capital market. . Financial Research Journal, 20(2), 130-150. (In Persian)
Bulbulian, M., & Ghodsi, A. (2017). Familiarity with software and review of some case studies Hakim Sabzevari University]. (In Persian)
Cardoso, H. L., Schaefer, M., & Oliveira, E. (1999). A multi-agent system for electronic commerce including adaptive strategic behaviours In Portuguese Conference on Artificial Intelligence, Berlin, Heidelberg.
Chaharsooqi, S. K., & Taheri, Z. (2016). Providing a Negotiation Mechanism for Multi-Broker Systems in Automated Electronic Exchanges: Based on Methods of Analyzing Buyer-Seller Behavior in Microeconomics. Journal of Modeling in Engineering, 14(46). (In Persian)
Dai, F. T., Teo, S., & Yuan Wang, K. (2016). Network Marketing Businesses and Chinese Ethnicity Immigrants in Australia, . Journal of Small Business Management, 1-16. https://doi.org/10.1111/jsbm.12244
Fakhrzad, M. B., & Rahdar, M. A. (2016). Optimization of hybrid robot control system using artificial hormones and fuzzy logic. Journal of Intelligent & Fuzzy Systems, 30(3), 1403-1410.
Groenewald, D., & Van Vuuren, J. J. (2007). A critical analysis of the influence of start-up factors in small businesses and entrepreneurial ventures in SA. Professional Accountant, 7(1), 269-280.
Haghighi Nasab, M., & Taghavi, S. S. (2012). Factors affecting the spread of e-business in Iranian organizations. Information Technology Management, 4(10), 25-40. (In Persian)
Haghighi Nasab, M., & Taqwa, S. S. (2012). Factors affecting the spread of e-business in Iranian organizations. Information Technology Management, 4(10), 25-40. (In Persian)
Hamichi, S., Guessoum, Z., & Mangalagiu, D. (2009). A multi-agent system for adaptive production networks. Springer.
Helal, M. (2017). An investigation of the use of social media for e-commerce amongst small businesses in Saudi Arabia [Ph.D. Thesis The University of Salford]. Salford, UK.
Hoor Ali, M., Montazeri, A., & Fathian, M. (2011). Designing a Smart Business Agent in Supply Chain Management in E-Commerce The First National Conference of Computer and Information Technology Scholars, Tabriz. (In Persian)
Hosseini, M. (2001). Maintenance planning and inventory control system, process and forecasting models: Introduction to PMS. Fresh Air Publications. (In Persian)
Jacyno, M. (2010). Self-organising Agent Communities for Autonomic Computing University of Southampton].
Khaloo, P., Mahan, F., Khosh Ahwal, A., & Khatibi, R. (2011). Designing a Smart Business Agent in Supply Chain Management in E-Commerce The First National Conference of Computer and Information Technology Scholars, Tabriz, University of Tabriz. (In Persian)
Khatami Firoozabadi, S. M. A., Askari Mehr, M., & Mortaz Hijri, F. (2018). E-business development strategies in the context of facilitating and enhancing the business environment. Quarterly Journal of Economic Research and Policy, 18(68), 253-290. (In Persian)
Koberg, E., & Longoni, A. (2019). A systematic review of sustainable supply chain management in global supply chains. Journal of Cleaner Production, 207, 1084-1098.
Lamshoft, K., Altschaffel, R., & Dittmann, J. (2017). Adapting Organic Computing Architectures to an Automotive Environment to Increase Safety & Security. Automotive-Safety & Security Sicherheitund Zuverlässigkeit für automobile Informationstechnik.
Memari, M., & Amerian, A. (2010). Intelligence of various e-commerce processes. Bimonthly of Artificial Intelligence and Instruments, 4(1). (In Persian)
Mir Vahadi, S., Toghraei, M. T., & Astaneh, M. (2020). Designing a gaming model in web-based entrepreneurial businesses. Scientific Journal of Intelligent Business Management Studies, 9(33), 39-60. (In Persian)
Mohammadian, M., Rouhani, A. R., Hashemzehi, A., & Karimian, M. (2013). Factors influencing the selection of small and medium e-business models in Iran. Quarterly Journal of Information Technology Management Studies, 3(12), 97-122. (In Persian)
Mousavi, F., Fathian, M., & Memari, M. (2006). Smart Factors in E-Commerce. Tadbir Monthly, 17(177). (In Persian)
Omidvar, A. (2014). Provide a framework for deploying email-based marketing in e-business [Master Thesis, Shahid Beheshti University]. (In Persian)
Rahmanzadeh, A., & Nazemi, I. (2013). Review of proposed models for self-organized organizations in multi-factor systems 8th Annual Conference of Science and Technology Association, Mashhad, Khavaran Institute of Higher Education. (In Persian)
Razalli, N., Sin, M., & Aizat, M. (2018). Internal and external key success factors of Business Process Re-Engineering (BPR): effects on the Islamic banks performance. International Journal of Engineering & Technology, 458-461.
Reichhuber, S. (2019). Knowledge Self-Adaptive Multi-Agent Learning. Knowledge Self-Adaptive Multi-Agent Learning, 507-515. (In: Draude, C., Lange, M. & Sick, B. (Hrsg.), INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge). Bonn: Gesellschaft für Informatik e.V.)
Reza, M. G., Hassani Saadat, H., & Erfanian Khanzadeh, H. (2015). Identifying Key Success Factors of Knowledge Management System. Technology Development Quarterly, 12(45), 26-35. (In Persian)
Seljughi, T. (2004). Presenting a Conceptual Model for Measuring the Performance of Electronic Supply Chain Based on Cloud Processing [M.Sc. Thesis], Mehr Alborz Educational Institute. (In Persian)
Tomforde, S. (2017). Technical Systems for Survival in the Real World (1st ed.). Cham Springer International Publishing, Birkhäuser.
Yari Nejad, M. (2012). Presenting a model of achieving self-adaptation in the customer relationship management system [Master Thesis, Shahid Beheshti University]. (In Persian)
Zhang, Y., Qian, C., Lv, J., & Liu, Y. (2017). Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor. IEEE Transactions on Industrial Informatics, 13(2), 737-747.
Zhu, Z., & Bush, A. (2020). The effects of e-business process in supply chain operations: Process component and value creation mechanisms. International journal of information management, 50, 273-285.
Zijm, H., Klumpp, M., Heragu, S., & Regattieri, A. (2019). Operations, Logistics and Supply Chain Management: Definitions and Objectives. Springer International Publishing, Operations, Logistics and Supply Chain Management