Operations Research eJournal最新文献_第3页

Analysis of Optimal Control Problems for Hybrid Systems with One State Variable 单状态变量混合系统的最优控制问题分析

Operations Research eJournal Pub Date : 2020-01-01 DOI: 10.2139/ssrn.3424771

P. V. Reddy, J. Schumacher, J. Engwerda

引用次数: 5

Discrete Convex Analysis and Its Applications in Operations: A Survey 离散凸分析及其在操作中的应用综述

Operations Research eJournal Pub Date : 2019-12-03 DOI: 10.2139/ssrn.3549628

Xin Chen, Menglong Li

{"title":"Discrete Convex Analysis and Its Applications in Operations: A Survey","authors":"Xin Chen, Menglong Li","doi":"10.2139/ssrn.3549628","DOIUrl":"https://doi.org/10.2139/ssrn.3549628","url":null,"abstract":"Discrete convexity, in particular, L-natural-convexity and M-natural-convexity, provides a critical opening to attack several classical problems in inventory theory, as well as many other operations problems that arise from more recent practices, for instance, appointment scheduling and bike-sharing. As a powerful framework, discrete convex analysis is becoming increasingly popular in the literature. This review will survey the landscape of the approach. We start by introducing several key concepts, namely, L-natural-convexity and M-natural-convexity and their variants, followed by a discussion of some fundamental properties that are most useful for studying operations models. We then illustrate various applications of these concepts and properties. Examples include network flow problem, stochastic inventory control, appointment scheduling, game theory, portfolio contract, discrete choice model, and bike-sharing. We focus our discussion on demonstrating how discrete convex analysis can shed new insights on existing problems, and/or bring about much simpler analyses and algorithm developments than previous methods in the literature. We also present several results and analyses that are new to the literature.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132529303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Online Pricing with Offline Data: Phase Transition and Inverse Square Law 基于离线数据的在线定价:相变与平方反比律

Operations Research eJournal Pub Date : 2019-10-17 DOI: 10.2139/ssrn.3471501

Jinzhi Bu, D. Simchi-Levi, Yunzong Xu

{"title":"Online Pricing with Offline Data: Phase Transition and Inverse Square Law","authors":"Jinzhi Bu, D. Simchi-Levi, Yunzong Xu","doi":"10.2139/ssrn.3471501","DOIUrl":"https://doi.org/10.2139/ssrn.3471501","url":null,"abstract":"This paper investigates the impact of pre-existing offline data on online learning in the context of dynamic pricing. We study a single-product dynamic pricing problem over a selling horizon of T periods. The demand in each period is determined by the price of the product according to a linear demand model with unknown parameters. We assume that before the start of the selling horizon, the seller already has some pre-existing offline data. The offline data set contains n samples, each of which is an input-output pair consisting of a historical price and an associated demand observation. The seller wants to use both the pre-existing offline data and the sequentially revealed online data to minimize the regret of the online learning process. We characterize the joint effect of the size, location, and dispersion of the offline data on the optimal regret of the online learning process. Specifically, the size, location, and dispersion of the offline data are measured by the number of historical samples, the distance between the average historical price and the optimal price, and the standard deviation of the historical prices, respectively. For both single-historical-price setting and multiple-historical-price setting, we design a learning algorithm based on the “Optimism in the Face of Uncertainty” principle, which strikes a balance between exploration and exploitation and achieves the optimal regret up to a logarithmic factor. Our results reveal surprising transformations of the optimal regret rate with respect to the size of the offline data, which we refer to as phase transitions. In addition, our results demonstrate that the location and dispersion of the offline data also have an intrinsic effect on the optimal regret, and we quantify this effect via the inverse-square law. This paper was accepted by Omar Besbes, revenue management and market analytics.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134056974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Tailored Base-Surge Policies in Dual-Sourcing Inventory Systems with Demand Learning 基于需求学习的双源库存系统的定制基数激增策略

Operations Research eJournal Pub Date : 2019-09-19 DOI: 10.2139/ssrn.3456834

Boxiao Chen, Cong Shi

{"title":"Tailored Base-Surge Policies in Dual-Sourcing Inventory Systems with Demand Learning","authors":"Boxiao Chen, Cong Shi","doi":"10.2139/ssrn.3456834","DOIUrl":"https://doi.org/10.2139/ssrn.3456834","url":null,"abstract":"We consider a periodic-review dual-sourcing inventory system, in which the expedited supplier is faster and more costly, while the regular supplier is slower and cheaper. Under full demand distributional information, it is well-known that the optimal policy is extremely complex but the celebrated Tailored Base-Surge (TBS) policy performs near optimally. Under such a policy, a constant order is placed at the regular source in each period, while the order placed at the expedited source follows a simple order-up-to rule. In this paper, we assume that the firm does not know the demand distribution a priori, and makes adaptive inventory ordering decisions in each period based only on the past sales (a.k.a. censored demand) data. The standard performance measure is regret, which is the cost difference between a feasible learning algorithm and the clairvoyant (full-information) benchmark. When the benchmark is chosen to be the (full-information) optimal Tailored Base-Surge policy, we develop the first nonparametric learning algorithm that admits a regret bound of O(T^{1/2} (log T)^{3} loglog T), which is provably tight up to a logarithmic factor. Leveraging the structure of this problem, our approach combines the power of bisection search and stochastic gradient descent and also involves a delicate high probability coupling argument between our and the clairvoyant optimal system dynamics. We also develop several technical results that are of independent interest.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129595653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

A Binary Search Method for the General Coupled Task Scheduling Problem 一般耦合任务调度问题的二叉搜索方法

Operations Research eJournal Pub Date : 2019-09-04 DOI: 10.2139/ssrn.3451871

M. Khatami, A. Salehipour

引用次数: 0

An Empirical Analysis of Intra-Firm Product Substitutability in Fashion Retailing 服装零售业企业内部产品可替代性的实证分析

Operations Research eJournal Pub Date : 2019-08-28 DOI: 10.2139/ssrn.3444467

Elcin Ergin, M. Gumus, N. Yang

{"title":"An Empirical Analysis of Intra-Firm Product Substitutability in Fashion Retailing","authors":"Elcin Ergin, M. Gumus, N. Yang","doi":"10.2139/ssrn.3444467","DOIUrl":"https://doi.org/10.2139/ssrn.3444467","url":null,"abstract":"This study offers an empirical investigation of inventory and sales dynamics in a large-scale retail network setting. We infer the impact of product shortages on sales in neighboring outlets using unique data from a large fast fashion retailing chain. Since the product shortages do not occur at the same time, we conduct a novel Difference-in-Differences (DiD) methodology to align the stock-out periods across the stores in the network. In addition, we stratify data based on the periods in which stock-out is observed and apply pairwise DiD analyses to validate the robustness of our results. Our analysis reveals that sales for a particular item at a focal store increases when that same item experiences stock-outs in neighboring stores, especially so for neighboring stores that are physically closer to the focal store. These findings suggest that there is substitutability across stores, and that this substitutability is the strongest in the period when the stock-out is observed for the first time, and decreases in the time since the stock-out and dissipates with physical distance. In order to assess the value of considering the impact of stock-outs on inventory allocation, we develop an optimization model and calibrate it by using parameters estimated via DiD analysis. The simulation analysis confirms that revenues markedly improve on average by 2.2% when neighboring stock-out information is taken into account for sales forecasting when optimizing inventory allocations. Finally, we conduct sensitivity analysis to evaluate how this effect changes with problem parameters such as product price, and inventory.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123248853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An Algorithm to Create Test Data for Large-Scale Railway Network Revenue Management Models with Customer Choice 基于客户选择的大型铁路网收益管理模型测试数据生成算法

Operations Research eJournal Pub Date : 2019-08-19 DOI: 10.2139/ssrn.3439270

Simon Hohberger, C. Schoen

引用次数: 0

Optimal Priority-Based Allocation Mechanisms 基于优先级的最优分配机制

Operations Research eJournal Pub Date : 2019-07-25 DOI: 10.2139/ssrn.3425348

Peng Shi

引用次数: 17

The Traveling Salesman Problem With One Truck and Multiple Drones 一辆卡车和多架无人机的旅行推销员问题

Operations Research eJournal Pub Date : 2019-05-01 DOI: 10.2139/ssrn.3389306

K. Seifried