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Inventory model with fuzzy lead-time and dynamic demand over finite time horizon using a multi-objective genetic algorithm

机译:多目标遗传算法的有限时间模糊提前期和动态需求的库存模型

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The real-world inventory control problems are normally imprecisely defined and human interventions are often required in solving these decision-making problems. In this paper, a realistic inventory problem with an infinite rate of replenishment over a prescribed finite but imprecise time horizon is formulated considering time dependent ramp type demand, which increases with time. Lead time is also assumed as fuzzy in nature. Shortages are allowed and backlogged partially. Two models are considered depending upon the ordering policies of the decision maker (DM). The imprecise parameters are first transformed to corresponding nearest interval numbers depending upon some distance metric on fuzzy numbers and then following the interval mathematics, the objective function for total profit from the planning horizon is obtained (which is an interval function). Then interval objective decision making problem is reduced to multi-objective problems using different approaches. Finally a fast and elitist multi-objective genetic algorithm (FEMOGA) is used for solving these multi-objective models to find pareto-optimal decisions for the DM. The models are illustrated numerically. As a particular case, the results due to linear trended and constant demands have been presented.
机译:通常,对现实世界中的库存控制问题的定义不准确,解决这些决策问题通常需要人工干预。在本文中,考虑了与时间有关的坡道类型需求,该问题具有一定的有限但不精确的时间范围内的无限补充率,这是一个现实的库存问题,该问题随时间增加。提前期本质上也被认为是模糊的。允许短缺并部分积压。根据决策者(DM)的订购策略考虑两种模型。不精确的参数首先根据模糊数上的某个距离度量转换为相应的最接近的区间数,然后按照区间数学方法,获得计划范围内总利润的目标函数(这是区间函数)。然后使用不同的方法将区间目标决策问题简化为多目标问题。最后,使用快速精英的多目标遗传算法(FEMOGA)求解这些多目标模型,以找到DM的最优对等决策。模型用数字表示。作为一种特殊情况,已经提出了由于线性趋势和恒定需求而导致的结果。

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