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Autor
Hau Bui Minh (Korea Maritime and Ocean University, Republic of Korea), You Sam-Sang (Korea Maritime and Ocean University, Republic of Korea), Cho Gyu-Sung (Tongmyong University, Republic of Korea), Yeon Jeong-Hum (Busan Port Authority, Republic of Korea), Kim Hwan-Seong (Korea Maritime and Ocean University, Republic of Korea)
Tytuł
Circular Supply Chain Management with Blockchain Integration
Źródło
LogForum, 2023, vol. 19, nr 4, s. 515-533, rys., tab., wykr., bibliogr. 23 poz.
Słowa kluczowe
Zarządzanie łańcuchem dostaw, Gospodarka zamkniętego obiegu, Technologia blockchain, Zarządzanie zapasami, Optymalizacja
Supply Chain Management (SCM), Circular economy, Blockchain technology, Inventory management, Optimalization
Uwagi
summ.
Abstrakt
Background: Circular supply chain management encourages manufacturers to take advantage of used materials and industrial wastes, ensuring social and economic benefits of enhanced environmental sustainability in production. With the development of digital technologies, transactions can be recorded within distributed or decentralized ledger technology. To improve the reliability of digital transactions on the internet, manufacturers need to apply security software such as blockchain technology across a network. Blockchain technology in the supply chains brings transparency, traceability, and security to online transactions, increasing customer attractiveness to the used product and recycling process. Secure transactions might bring significant benefits to the future of the retail industry. The manufacturer exploits the retailer as an online store. Consumers access an online store for purchase, return, and comment on the manufacturer's products. Methods: This research presents a novel meta-heuristic algorithm, specifically, adaptive particle swarm optimization (adaptive PSO), to determine the optimal policy for product price and quantity of materials imported from suppliers for maximizing total profits with sustainability. The adaptive PSO algorithm evaluates the fitness function involving discrete random variables. The supply chain model captures realistic conditions by considering the inherent uncertainties of customer demand. Two scenarios in business settings have been tested on the same customer demand. The proposed framework is intended to yield a more sustainable, resilient, and regenerative system, securing the data of the shared community. Results: The swarm intelligence algorithm implements profit optimization to determine the unit price of the product and the number of materials to be imported. The numerical experiments are conducted to demonstrate the efficacy of the optimal strategy and to evaluate the effect of the number of used products returned to the recycling center. An effective circular supply chain management is realized based on a secure database shared across a network of participants. Key findings significantly contribute to the intelligent decision support system for optimizing inventory management under various stochastic scenarios. Conclusions: Sharing, reusing, repairing, and remanufacturing help companies transition to a circular economy, minimize waste, diversify sources of supply, and maintain production continuity. Transactions on the blockchain framework become transparent and traceable. Based on the circular economy and blockchain platform, this study deals with an adaptive particle swarm optimization algorithm (APSO) to determine the optimal policy for product unit price and quantity of materials imported from suppliers to maximize total profits. The presented methodology can provide better supply chain visibility and traceability under highly realistic stochastic environments. (original abstract)
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Bibliografia
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  11. Kumar J.S., Singhal D., 2023, Optimizing the Competitive Sustainable Process and Pricing Decision of Digital Supply Chain: A Power-Balance Perspective, Computers & Industrial Engineering, 177, 109054, https://doi.org/10.1016/j.cie.2023.109054
  12. Li S., Zhang J., Tang W., 2015, Joint Dynamic Pricing and Inventory Control Policy for a Stochastic Inventory System with Perishable Products, International Journal of Production Research, 53 (10), 2937-2950, https://doi.org/10.1080/00207543.2014.961206
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  22. Varga T., Király A., Abonyi J., 2013, 19 - Improvement of PSO Algorithm by Memory-Based Gradient Search-Application in Inventory Management, Swarm Intelligence and Bio-Inspired Computation, 403-422, https:/doi.org/10.1016/B978-0-12-405163-8.00019-3
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Cytowane przez
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ISSN
1895-2038
Język
eng
URI / DOI
http://dx.doi.org/10.17270/J.LOG.2023.930
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