INFORMS journal on optimization最新文献_第5页

Strong Formulations for Distributionally Robust Chance-Constrained Programs with Left-Hand Side Uncertainty Under Wasserstein Ambiguity Wasserstein歧义下具有左手边不确定性的分布鲁棒机会约束规划的强公式

INFORMS journal on optimization Pub Date : 2020-07-14 DOI: 10.1287/ijoo.2022.0083

Nam Ho-Nguyen, F. Kılınç-Karzan, Simge Küçükyavuz, Dabeen Lee

{"title":"Strong Formulations for Distributionally Robust Chance-Constrained Programs with Left-Hand Side Uncertainty Under Wasserstein Ambiguity","authors":"Nam Ho-Nguyen, F. Kılınç-Karzan, Simge Küçükyavuz, Dabeen Lee","doi":"10.1287/ijoo.2022.0083","DOIUrl":"https://doi.org/10.1287/ijoo.2022.0083","url":null,"abstract":"Distributionally robust chance-constrained programs (DR-CCPs) over Wasserstein ambiguity sets exhibit attractive out-of-sample performance and admit big-M–based mixed-integer programming reformulations with conic constraints. However, the resulting formulations often suffer from scalability issues as problem size increases. To address this shortcoming, we derive stronger formulations that scale well with respect to the problem size. Our focus is on ambiguity sets under the so-called left-hand side uncertainty, where the uncertain parameters affect the coefficients of the decision variables in the linear inequalities defining the safety sets. The interaction between the uncertain parameters and the variable coefficients in the safety set definition causes challenges in strengthening the original big-M formulations. By exploiting the connection between nominal chance-constrained programs and DR-CCP, we obtain strong formulations with significant enhancements. In particular, through this connection, we derive a linear number of valid inequalities, which can be immediately added to the formulations to obtain improved formulations in the original space of variables. In addition, we suggest a quantile-based strengthening procedure that allows us to reduce the big-M coefficients drastically. Furthermore, based on this procedure, we propose an exponential class of inequalities that can be separated efficiently within a branch-and-cut framework. The quantile-based strengthening procedure can be expensive. Therefore, for the special case of covering and packing type problems, we identify an efficient scheme to carry out this procedure. We demonstrate the computational efficacy of our proposed formulations on two classes of problems, namely stochastic portfolio optimization and resource planning.","PeriodicalId":73382,"journal":{"name":"INFORMS journal on optimization","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48928873","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

Editorial Board 编辑委员会

INFORMS journal on optimization Pub Date : 2020-07-01 DOI: 10.1287/ijoo.2020.eb.v2n3

引用次数: 0

An Alternating Manifold Proximal Gradient Method for Sparse Principal Component Analysis and Sparse Canonical Correlation Analysis 稀疏主成分分析和稀疏典型相关分析的交替流形近端梯度方法

INFORMS journal on optimization Pub Date : 2020-07-01 DOI: 10.1287/ijoo.2019.0032

Shixiang Chen, Shiqian Ma, Lingzhou Xue, H. Zou

引用次数: 22

Mining Optimal Policies: A Pattern Recognition Approach to Model Analysis 挖掘最优策略：一种用于模型分析的模式识别方法

INFORMS journal on optimization Pub Date : 2020-05-21 DOI: 10.2139/SSRN.3069690

Fernanda Bravo, Yaron Shaposhnik

引用次数: 20

Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation 具有不精确子问题解和梯度聚合的梯度采样方法

INFORMS journal on optimization Pub Date : 2020-05-15 DOI: 10.1287/ijoo.2022.0073

Frank E. Curtis, Minhan Li

引用次数: 2

The Backhaul Profit Maximization Problem: Optimization Models and Solution Procedures 回程利润最大化问题:优化模型和求解过程

INFORMS journal on optimization Pub Date : 2020-03-29 DOI: 10.1287/ijoo.2022.0071

Yuanyuan Dong, Yulan Bai, E. Olinick, A. J. Yu

{"title":"The Backhaul Profit Maximization Problem: Optimization Models and Solution Procedures","authors":"Yuanyuan Dong, Yulan Bai, E. Olinick, A. J. Yu","doi":"10.1287/ijoo.2022.0071","DOIUrl":"https://doi.org/10.1287/ijoo.2022.0071","url":null,"abstract":"We present a compact mixed integer program (MIP) for the backhaul profit maximization problem in which a freight carrier seeks to generate profit from an empty delivery vehicle’s backhaul trip from its last scheduled delivery to its depot by allowing it to deviate from the least expensive (or fastest) route to accept pickup-and-delivery requests between various points on the route as allowed by its capacity and required return time. The MIP is inspired by a novel representation of multicommodity flow that significantly reduces the size of the constraint matrix compared with a formulation based on the classical node-arc representation. This, in turn, leads to faster solution times when using a state-of-the-art MIP solver. In an empirical study of both formulations, problem instances with 10 potential pickup/drop-off locations and up to 72 pickup-and-delivery requests were solved an average 1.44 times faster in real time with our formulation, whereas instances with 20 locations and up to 332 pickup-and-delivery requests were solved an average of 11.88 times faster. The largest instances in the comparative study had 60 locations and up to 3,267 pickup-and-delivery requests; these instances required an average of more than 54 hours of real time to solve with the node-arc–based formulation but were solved in an average of under two hours of real time using our compact formulation. We also present a heuristic algorithm based on our compact formulation that finds near optimal solutions to each of the 60-location instances within 22 minutes of real time and near optimal solutions to instances with up to 80 locations within four and a half hours of real time.","PeriodicalId":73382,"journal":{"name":"INFORMS journal on optimization","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43126578","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: 1

Editorial Board 编辑委员会

INFORMS journal on optimization Pub Date : 2020-01-01 DOI: 10.1287/ijoo.2020.eb.v2n2

引用次数: 0

Editorial Board 编辑委员会

INFORMS journal on optimization Pub Date : 2020-01-01 DOI: 10.1287/ijoo.2020.eb.v2n1

引用次数: 0

Introduction to the Issue 问题简介

INFORMS journal on optimization Pub Date : 2019-12-16 DOI: 10.1287/ijoo.2019.0028

D. Bertsimas

引用次数: 0

Smooth and Flexible Dual Optimal Inequalities 光滑和柔性对偶最优不等式

INFORMS journal on optimization Pub Date : 2019-12-01 DOI: 10.1287/ijoo.2021.0057

Naveed Haghani, Claudio Contardo, Julian Yarkony

{"title":"Smooth and Flexible Dual Optimal Inequalities","authors":"Naveed Haghani, Claudio Contardo, Julian Yarkony","doi":"10.1287/ijoo.2021.0057","DOIUrl":"https://doi.org/10.1287/ijoo.2021.0057","url":null,"abstract":"We address the problem of accelerating column generation (CG) for set-covering formulations via dual optimal inequalities (DOIs). We study two novel classes of DOIs, which are referred to as Flexible DOIs (F-DOIs) and Smooth-DOIs (S-DOIs), respectively (and jointly as SF-DOIs). F-DOIs provide rebates for covering items more than necessary. S-DOIs describe the payment of a penalty to permit the undercoverage of items in exchange for the overinclusion of other items. Unlike other classes of DOIs from the literature, the S-DOIs and F-DOIs rely on very little problem-specific knowledge and, as such, have the potential to be applied to a vast number of problem domains. In particular, we discuss the application of the new DOIs to three relevant problems: the single-source capacitated facility location problem, the capacitated p-median problem, and the capacitated vehicle-routing problem. We provide computational evidence of the strength of the new inequalities by embedding them within a column-generation solver for these problems. Substantial speedups can be observed as when compared with a nonstabilized variant of the same CG procedure to achieve the linear-relaxation lower bound on problems with dense columns and structured assignment costs.","PeriodicalId":73382,"journal":{"name":"INFORMS journal on optimization","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42987731","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: 11