In this paper, a mathematical negotiation mechanism is designed to minimize the negotiators' costs in a distributed procurement problem at two echelons of an automotive supply chain. The buyer's costs are procurement cost and shortage penalty in a one-period contract. On the other hand, the suppliers intend to solve a multi-period, multi-product production planning to minimize their costs. Such a mechanism provides an alignment among suppliers' production planning and order allocation, also supports the partnership with the valued suppliers by taking suppliers' capacities into account. Such a circumstance has been modeled via bi-level programming, in which the buyer acts as a leader, and the suppliers individually appear as followers in the lower level. To solve this nonlinear bi-level programming model, a hybrid algorithm by combining the particle swarm optimization (PSO) algorithm with a heuristic algorithm based on A search is proposed. The heuristic A algorithm is embedded to solve the mixed-integer nonlinear programming (MINLP) sub-problems for each supplier according to the received variable values determined by PSO system particles (buyer's request for quotations (RFQs)). The computational analyses have shown that the proposed hybrid algorithm called PSO-A outperforms PSO-SA and PSO-Greedy algorithms.


翻译:在本文中,一个数学谈判机制旨在将汽车供应链两个阶梯的分布式采购问题的谈判者成本降至最低,买方的成本是采购成本和一期合同中的短缺罚款;另一方面,供应商打算解决多期、多产品生产规划,以尽量减少成本;这种机制在供应商的生产规划和订单分配之间提供了协调,还考虑到供应商的能力,从而支持与有价供应商的伙伴关系。这种情形是通过双级程序拟定模式建立的,买方作为领先者行事,供应商单独作为下级追随者出现。为了解决这一非线性双级规划模式,一种混合算法,将粒子温优化(PSO)算法与基于搜索的超光速算法相结合。这种超理论的算法嵌入于解决混合的内向非线性编程(MINLP)子问题。根据PSO系统粒子(买方的报价请求(RFQA)和混合的PSOA系统算法分析,即拟议的PSO-SO的混合算法。

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