Robust knapsack ordering for a partially-informed newsvendor with budget constraint

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2024-11-08 DOI:10.1016/j.orl.2024.107202

Guus Boonstra , Wouter J.E.C. van Eekelen , Johan S.H. van Leeuwaarden

引用次数: 0

Abstract

This paper studies a robust version of the multi-item newsvendor problem with limited budget. The demand distribution belongs to an ambiguity set that contains all distributions that share the same range, mean and mean absolute deviation. The resulting optimization problem turns out to be solvable by a method reminiscent of the greedy algorithm for continuous knapsack problems, purchasing items in order of marginal effect on the total cost until the budget is spent.

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有预算约束的部分知情新闻供应商的稳健knapsack排序

本文研究的是预算有限的多项目新闻供应商问题的稳健版本。需求分布属于一个模糊集，其中包含所有具有相同范围、平均值和平均绝对偏差的分布。由此产生的优化问题可以用一种类似于连续背包问题贪婪算法的方法来解决，即按照对总成本的边际效应顺序购买物品，直到花完预算为止。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.