受调制过程影响的移动生产库存系统的管理
Managing mobile production-inventory systems influenced by a modulation process
摘要：
我们调查的潜在附加值，能够搬迁生产能力，相对于固定的生产能力，在一个网络的多个，地理分布的制造基地。人们对可以在地理上重新定位的生产能力越来越感兴趣;例如，用于医药中间体的模块化单元。它显示了在不牺牲客户服务水平或总系统弹性的情况下，通过减少具有固定生产能力的分布式网络的总库存和总生产能力来实现分布式网络的快速实现的前景。在考虑转运的情况下，建立了一个具有L个生产基地和Y个可搬迁生产能力单元的生产-库存系统模型，并针对动态搬迁和多基地库存控制问题，提出了高效的启发式求解方法，分析了潜在的附加值。我们描述的（L，Y）问题作为一个问题的顺序决策下的不确定性，以确定转运，移动生产能力搬迁，并在每个决策时期的补充决策。为了增强模型的真实性，我们使用一个部分可观察的随机过程，即调制过程，来模拟外源的和部分可观察的力（例如，宏观经济影响需求。然后，我们模型的（L，Y）问题作为一个部分观察到的马尔可夫决策过程。由于精确求解该模型的计算挑战相当大，我们提出了两个高效，高质量的算法。我们显示了一个实例集与五个位置，生产能力的流动性和转运，相对于固定的生产能力的情况下，可以提高系统的性能，平均高达41%，超过无灵活性的情况下，生产能力的流动性可以产生高达10%以上的节省相比，只有转运是允许的。
英文摘要：
We investigate the potential added value of being able to relocate production capacity, relative to fixed production capacity, in a network of multiple, geographically distributed manufacturing sites. There is a growing interest in production capacity that can be geographically relocated; e.g., modular units for pharmaceutical intermediates. It shows promise for enabling the fast fulfillment of a distributed network with a reduction in the total inventory and total production capacity of a distributed network with fixed production capacity without sacrificing customer service levels or total system resilience. Allowing also for transshipment, we model a production-inventory system with L production sites and Y units of relocatable production capacity, develop efficient and effective heuristic solution methods for dynamic relocation and multi-location inventory control, and analyze the potential added value. We describe the (L, Y) problem as a problem of sequential decision making under uncertainty to determine transshipment, mobile production capacity relocation, and replenishment decisions at each decision epoch. To enhance model realism, we use a partially observed stochastic process, the modulation process, to model the exogenous and partially observable forces (e.g., the macro-economy) that affect demand. We then model the (L, Y) problem as a partially observed Markov decision process. Due to the considerable computational challenges of solving this model exactly, we propose two efficient, high quality heuristics. We show for an instance set with five locations that production capacity mobility and transshipment, relative to the fixed production capacity case, can improve systems performance by as much as 41\% on average over the no-flexibility case and that production capacity mobility can yield as much as 10\% more savings compared to when only transshipment is permitted.
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