Online Linear Optimization with Inventory Management Constraints
Online Linear Optimization with Inventory Management Constraints
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DOI：
10.1145/3379482
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发表时间：
2020-05
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影响因子：
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通讯作者：
Lin Yang;M. Hajiesmaili;R. Sitaraman;A. Wierman;Enrique Mallada;W. Wong
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文献类型：
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作者：
Lin Yang;M. Hajiesmaili;R. Sitaraman;A. Wierman;Enrique Mallada;W. Wong
本文研究了具有库存管理约束的在线线性优化问题。具体地说，我们考虑了一个在线场景，其中决策者需要满足她对某一资产单位的时变需求，无论是来自价格随时间变化的市场，还是来自她自己的库存。在每个时间段，决策者被呈现一个(线性)价格，并且必须立即决定为满足需求和/或储存在库存中以备将来使用而购买的数量。库存容量有限，可以用来低价购买和储存资产，并在价格高时覆盖需求。决策者的最终目标是满足每个时段的需求，同时将从市场购买资产的成本降至最低。我们提出了ARP，一个具有库存约束的线性规划的在线算法，以及ARPRate，一个处理进出库存的费率约束的扩展版本。ARP和ARPRate都实现了最优竞争比，这意味着没有其他在线算法可以实现更好的理论保证。为了说明结果，我们在一个案例研究中使用了所提出的算法，该案例主要关注数据中心的能源采购和存储管理策略。
This paper considers the problem of online linear optimization with inventory management constraints. Specifically, we consider an online scenario where a decision maker needs to satisfy her time-varying demand for some units of an asset, either from a market with a time-varying price or from her own inventory. In each time slot, the decision maker is presented a (linear) price and must immediately decide the amount to purchase for covering the demand and/or for storing in the inventory for future use. The inventory has a limited capacity and can be used to buy and store assets at low price and cover the demand when the price is high. The ultimate goal of the decision maker is to cover the demand at each time slot while minimizing the cost of buying assets from the market. We propose ARP, an online algorithm for linear programming with inventory constraints, and ARPRate, an extended version that handles rate constraints to/from the inventory. Both ARP and ARPRate achieve optimal competitive ratios, meaning that no other online algorithm can achieve a better theoretical guarantee. To illustrate the results, we use the proposed algorithms in a case study focused on energy procurement and storage management strategies for data centers.