Dual-Channel Supply Chain Inventory Optimization Using Teaching-Learning-Based Algorithm for Carbon Efficiency

機構典藏 > College of Business and Management > Graduate Institute & Department of Business Administration > Proceeding > Item 987654321/127026

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/127026

Title:	Dual-Channel Supply Chain Inventory Optimization Using Teaching-Learning-Based Algorithm for Carbon Efficiency
Authors:	Tsaur, Ruey-chyn;Lin, Nei-chih;Lu, Chi-jie;Chen, Tzu-hsuan;Yang, Chih-te
Keywords:	Inventory;supply chain;dual channel;multiple retailers;carbon cap and trade;teaching-learning-based optimization
Date:	2025-01-22
Issue Date:	2025-03-20 12:07:01 (UTC+8)
Abstract:	The impact of global climate change and shifting consumption patterns has made managing multinational supply chain inventory crucial, especially in light of net-zero carbon emission goals. The adoption of dual-channel marketing models, combining online and physical channels, adds complexity to supply chain management. A key challenge for enterprises is balancing environmental sustainability with profitability, while facing global pressure to reduce carbon footprints. In dual-channel supply chains, the profits of manufacturers and retailers offering substitutable products are interdependent, further complicating inventory management and efforts to optimize profit alongside meeting carbon reduction targets. This study proposes sustainable production-inventory models for multinational supply chains with dual channels and multiple physical retailers, incorporating collaboration on carbon reduction investments among supply chain members. The model calculates the total profit and carbon emissions of manufacturers and retailers separately, and then optimizes selling prices, material supply, production, delivery, investment strategies, and replenishment strategies to maximize overall supply chain profit under a carbon cap-and-trade policy. Due to the complexity introduced by multiple physical retailers, traditional mixed-integer nonlinear programming models become difficult to solve as the number of retailers increases. Therefore, the study employs the Teaching-Learning-Based Optimization (TLBO) algorithm to find optimal solutions effectively. Numerical and sensitivity analyses validate and illustrate the proposed models, providing insights for managers to optimize production, shipping, ordering, investing, and pricing strategies across channels while responding to national carbon reduction policies. This research offers a comprehensive framework for balancing sustainability and profitability in modern supply chain management.
Appears in Collections:	[Graduate Institute & Department of Business Administration] Proceeding

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