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Multi-objective optimization matching for one-shot multi-attribute exchanges with quantity discounts in E-brokerage

机译：电子经纪中带数量折扣的一次性多属性交易的多目标优化匹配

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Electronic brokerages (E-brokerages) are Internet-based organizations that enable buyers and sellers to do business with each other. While E-brokerages have become a significant sector of E-commerce, theory and guidelines for matching the multi-attribute exchange in E-brokerage are sparse. This paper presents an approach to optimize the matching of one-shot multi-attribute exchanges with quantity discounts. Firstly, based on the conception and definition of matching degree and quantity discount, a multi-objective optimization model is proposed to maximize the matching degree and trade volume. This model belongs to a class of multi-objective nonlinear transportation problems and cannot be solved effectively by conventional methods, especially when large-scale problems are involved. Hence, secondly, a novel hybrid multi-objective meta-heuristic algorithm named multi-objective simulated annealing genetic algorithm (MOSAGA) has been developed to solve the proposed model. Finally, the computational results and analyses of some numerical problems are given to illustrate the application and performance of the proposed model and algorithm.
机译：电子经纪（E-brokerages）是基于Internet的组织，使买卖双方能够开展业务。尽管电子经纪已成为电子商务的重要领域，但与电子经纪中的多属性交易所相匹配的理论和指南却很少。本文提出了一种通过数量折扣优化单发多属性交易所匹配的方法。首先，基于匹配度和数量折扣的概念和定义，提出了一种多目标优化模型，以最大化匹配度和交易量。该模型属于一类多目标非线性运输问题，常规方法无法有效解决，特别是涉及大规模问题时。因此，第二，提出了一种新颖的混合多目标元启发式算法，称为多目标模拟退火遗传算法（MOSAGA），以解决该模型。最后，给出了计算结果并分析了一些数值问题，以说明所提出的模型和算法的应用和性能。

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