We study a new class of inventory control problems, the recruitment stocking problem (RSP), applicable to general recruitment systems and products with limited supplies. Recruitment stocking occurs routinely in many organizations with the goal of identifying qualified candidates rapidly and cost effectively. This activity may take place at multiple sites simultaneously to shorten the recruitment time. Examples include recruiting patients in clinical trials, enlisting personnel in the military, and recruiting customers for product sampling in market testing and promotion. RSP differs from the extant inventory management literature in that it stipulates a finite recruitment target, so that recruitment is terminated as soon as the total number of recruits across all locations reaches that prescribed target. This distinctive feature of RSP calls for the development of new stochastic models to evaluate and optimize system performance. Thus, we present a novel methodology of relaxation and decomposition to characterize the probability distribution of rejections in RSP (number of arrivals to an empty inventory). This method provides a basis for efficient and accurate evaluation of the Type 2 service level and expected recruitment time. We also leverage the attendant computational efficiency to develop optimization algorithms to compute the optimal stocking quantities.

中文翻译：

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招聘备货问题分析与优化

我们研究了一类新的库存控制问题，即招聘库存问题 (RSP)，适用于一般招聘系统和供应有限的产品。招聘储备在许多组织中经常发生，目的是快速、经济高效地确定合格的候选人。此活动可能会在多个地点同时进行，以缩短招聘时间。例子包括在临床试验中招募患者，招募人员参军，以及在市场测试和推广中招募客户进行产品抽样。RSP 与现有库存管理文献的不同之处在于，它规定了一个有限的招聘目标，一旦所有地点的招聘总数达到规定的目标，招聘就会终止。RSP 的这一显着特征要求开发新的随机模型来评估和优化系统性能。因此，我们提出了一种新的松弛和分解方法来表征 RSP（空库存的到达数量）中拒绝的概率分布。该方法为高效、准确地评估 2 类服务水平和预期招聘时间提供了基础。我们还利用随之而来的计算效率来开发优化算法来计算最佳库存数量。该方法为高效、准确地评估 2 类服务水平和预期招聘时间提供了基础。我们还利用随之而来的计算效率来开发优化算法来计算最佳库存数量。该方法为高效、准确地评估 2 类服务水平和预期招聘时间提供了基础。我们还利用随之而来的计算效率来开发优化算法来计算最佳库存数量。