The combination of traditional retail channel with direct channel adds a new dimension of competition to manufacturers’ distribution system. In this paper, we consider a make-to-order manufacturer with two channels of sale, sale through retailers and online direct sale. The customers are classified into different classes, based on their sensitivity to price and due date. The orders of traditional retail channel customers are fulfilled in the same period of ordering. However, price and due date are quoted to the online customers based on the available capacity as well as the other orders in the pipeline. We develop two different structures of the supply chain: centralized and decentralized dual-channel supply chain which are formulated as bilevel binary nonlinear models. The Particle Swarm Optimization algorithm is also developed to obtain a satisfactory near-optimal solution and compared to a genetic algorithm. Through various numerical analyses, we investigate the effects of the customers’ preference of a direct channel on the model’s variables.

中文翻译：

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求解多顾客双渠道供应链中定价和提前期报价的粒子群优化算法

传统零售渠道与直接渠道的结合为制造商的分销系统增加了竞争的新维度。在本文中，我们考虑具有两种销售渠道的定单制造商：通过零售商销售和在线直接销售。根据客户对价格和到期日的敏感性，将他们分为不同的类别。传统零售渠道客户的订单在订购的同时完成。但是，价格和到期日期是根据可用容量以及管道中的其他订单向在线客户报价的。我们开发了供应链的两种不同结构：集中化和分散化的双通道供应链，它们被构造为双层二进制非线性模型。还开发了粒子群优化算法，以获得令人满意的近似最优解，并将其与遗传算法进行了比较。通过各种数值分析，我们研究了客户对直接渠道的偏好对模型变量的影响。