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Optimal order quantity for deterministic demand with piecewise amount discount by genetic algorithm

机译:通过遗传算法的分段金额折扣的确定性需求的最佳订单数量

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With regards to the features of supply chain components in a sales company, the paper proposes the problem of allocating the replenishment orders of multiple products in several periods with deterministic demands of downstream customers. The optimization model aims to minimize the operational costs considering constraints of purchase quantity, inventory capacity and current capitals, i.e., an Economic Order Quantity optimization model based on the piece-wise discounts with respect to total purchase amount. To solve the model, a Genetic Algorithm is designed in terms of its characteristics; the chromosome coding with purchase quantity of periods. A heuristic method is employed to initialize the evolutionary population; the interchange-oriented crossover and the heuristic mutation are designed based on single-point or genes segment; and infeasible chromosomes are revised by means of a special repair strategy. The simulation experiments are implemented with practical data of a sales company and the results verified the effectiveness and efficiency of the proposed model and the solving algorithm.
机译:至于在销售公司的供应链组成部分的功能,提出了与下游客户的需求,确定若干个周期分配多个产品的补货订单的问题。优化模型的目标是最小化考虑购买数量,库存容量和当前资金,即限制了操作成本的基础上,分段折扣相对于总购买量的经济订货量优化模型。为了解决这个模型中,遗传算法被设计在它的特性;染色体与时间的采购量编码。采用的启发式方法来初始化进化群体;面向交换-交叉和启发式突变是基于单点或基因片段而设计的;和不可行的染色体是由一种特殊的修复策略的方式修改。仿真实验与销售公司的实际数据来实现和结果验证的有效性和该模型的效率和求解算法。

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