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A solid transportation problem for an item with fixed charge, vehicle cost and price discounted varying charge using genetic algorithm

机译：使用遗传算法的固定费用，车辆成本和价格折扣可变费用物品的固体运输问题

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摘要

In this paper, discount in transportation cost on the basis of transportated amount is extended to a solid transportation problem. In a transportation model, the available discount is normally offered on items/criteria, etc. in the form AUD (all unit discount) or IQ.D (incremental quantity discount) or combination of these two. Here transportation model is considered with fixed charges and vechicle costs where AUD, IQD or combination of AUD and IQD on the price depending upon the amount is offered and varies on the choice of origin, destination and conveyance. To solve the problem, genetic algorithm (GA) based on Roulette wheel selection, arithmetic crossover and uniform mutation has been suitably developed and applied. To illustrate the models, numerical examples have been presented. Here, different types of constraints are introduced and the corresponding results are obtained. To have better customer service, the entropy function is considered and it is displayed by a numerical example. To exhibit the efficiency of GA, another method-weighted average method for multi-objective is presented, executed on a multi-objective problem and the results of these two methods are compared.
机译：在本文中，基于运输量的运输成本折扣被扩展到一个可靠的运输问题。在运输模型中，可用的折扣通常以AUD（所有单位折扣）或IQ.D（增量数量折扣）或两者的组合的形式在商品/标准等上提供。此处考虑的运输模式是固定费用和车辆成本，其中根据提供的金额提供AUD，IQD或AUD和IQD的价格组合，并且取决于来源，目的地和运输工具的选择。为了解决该问题，已经适当地开发和应用了基于轮盘赌轮选择，算术交叉和均匀变异的遗传算法。为了说明模型，已经给出了数值示例。在这里，引入了不同类型的约束，并获得了相应的结果。为了获得更好的客户服务，考虑了熵函数，并通过一个数值示例进行显示。为了展示遗传算法的效率，提出了另一种方法加权平均的多目标方法，对多目标问题执行该方法，并比较了这两种方法的结果。

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