Annals of Operations Research最新文献_第9页

A modified scenario bundling method for shortest path network interdiction under endogenous uncertainty 内源不确定性下最短路径网络拦截的改进场景捆绑方法

IF 4.4 3区管理学

Annals of Operations Research Pub Date : 2024-09-23 DOI: 10.1007/s10479-024-06157-4

Somayeh Sadeghi, Abbas Seifi

{"title":"A modified scenario bundling method for shortest path network interdiction under endogenous uncertainty","authors":"Somayeh Sadeghi, Abbas Seifi","doi":"10.1007/s10479-024-06157-4","DOIUrl":"10.1007/s10479-024-06157-4","url":null,"abstract":"<div><p>We consider a shortest-path network interdiction problem under endogenous uncertainty on successful detection. Endogenous uncertainty arises from the fact that the interdictor may decide to enforce surveillance on some critical arcs, which would affect the prior probability of success on those arcs. The evader decision is formulated as a two-stage stochastic programming problem. In a “here and now situation”, he has to choose the shortest path in the network before realizing detection scenarios. Then, in the second stage, the evader tries to minimize the expected cost of being detected over all possible scenarios. We consider binary scenarios to represent whether or not the evader is detected on each path and apply a linearization method to deal with the non-linearity in the decision-dependent probability measure. A decomposition method is used to solve the proposed model for a case study of a worldwide drug trafficking network. The case study is concerned with finding the most critical arcs for interdicting drug trafficking. Numerical results show that a tiny increase in the probability of opium seizures leads to a significant change in the expected cost when the critical arcs are interdicted. Due to the exponential number of scenarios, the model could not be solved in a reasonable time. Common scenario reduction methods are designed for exogenous uncertainty. We apply an improved bundling method to reduce the number of scenarios in case of endogenous uncertainty. Computational results show that our method reduces the model size and solution time tremendously without significantly affecting the objective value.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"343 1","pages":"429 - 457"},"PeriodicalIF":4.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal scheduling on unrelated parallel machines with combinatorial auction 组合竞价下不相关并行机的最优调度

IF 4.4 3区管理学

Annals of Operations Research Pub Date : 2024-09-23 DOI: 10.1007/s10479-024-06283-z

Xue Yan, Ting Wang, Xuefei Shi

{"title":"Optimal scheduling on unrelated parallel machines with combinatorial auction","authors":"Xue Yan, Ting Wang, Xuefei Shi","doi":"10.1007/s10479-024-06283-z","DOIUrl":"10.1007/s10479-024-06283-z","url":null,"abstract":"<div><p>Outsourcing operations have become an essential factor in enhancing the competitive advantage of software development enterprises. In this work, we examine the application of combinatorial auction in technician assignment and outsourcing service procurement, which is conducted by software enterprises to minimize the total cost of developing all the software. It gives rise to an unrelated parallel machine scheduling problem incorporating combinatorial auction (<span>UPMSCA</span>). Here, the jobs represent the software to be developed, and they consume the perishable time resources of the development technicians, which can be translated into monetary costs. The objective is to schedule the jobs on parallel machines or select the bid with the lowest cost. To solve the problem, we propose an arc-flow model and a set-partitioning formulation with column-based constraints. A branch-and-price algorithm with four branching rules is proposed and utilizes an effective dynamic programming algorithm to solve the pricing subproblem in the pattern-based formulation. To speed up computation, a bidirectional search method and a dominance rule are applied. Results from extensive computational tests on 100 sets of randomly generated instances demonstrate the performance of our algorithm.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"344 2-3","pages":"937 - 963"},"PeriodicalIF":4.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prototype-based learning for real estate valuation: a machine learning model that explains prices 基于原型的房地产估价学习：一种解释价格的机器学习模型

IF 4.4 3区管理学

Annals of Operations Research Pub Date : 2024-09-23 DOI: 10.1007/s10479-024-06273-1

Jose A. Rodriguez-Serrano

{"title":"Prototype-based learning for real estate valuation: a machine learning model that explains prices","authors":"Jose A. Rodriguez-Serrano","doi":"10.1007/s10479-024-06273-1","DOIUrl":"10.1007/s10479-024-06273-1","url":null,"abstract":"<div><p>The systematic prediction of real estate prices is a foundational block in the operations of many firms and has individual, societal and policy implications. In the past, a vast amount of works have used common statistical models such as ordinary least squares or machine learning approaches. While these approaches yield good predictive accuracy, most models work very differently from the human intuition in understanding real estate prices. Usually, humans apply a criterion known as “direct comparison”, whereby the property to be valued is explicitly compared with similar properties. This trait is frequently ignored when applying machine learning to real estate valuation. In this article, we propose a model based on a methodology called <i>prototype-based learning</i>, that to our knowledge has never been applied to real estate valuation. The model has four crucial characteristics: (a) it is able to capture non-linear relations between price and the input variables, (b) it is a parametric model able to optimize any loss function of interest, (c) it has some degree of explainability, and, more importantly, (d) it encodes the notion of direct comparison. None of the past approaches for real estate prediction comply with these four characteristics simultaneously. The experimental validation indicates that, in terms of predictive accuracy, the proposed model is better or on par to other machine learning based approaches. An interesting advantage of this method is the ability to summarize a dataset of real estate prices into a few “prototypes”, a set of the most representative properties.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"344 1","pages":"287 - 311"},"PeriodicalIF":4.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-024-06273-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient iterated local search based metaheuristic approach for solving sports timetabling problems of International Timetabling Competition 2021 基于高效迭代局部搜索的元启发式方法求解2021国际排课比赛体育排课问题

IF 4.4 3区管理学

Annals of Operations Research Pub Date : 2024-09-22 DOI: 10.1007/s10479-024-06285-x

I. Gusti Agung Premananda, Aris Tjahyanto, Ahmad Mukhlason

{"title":"Efficient iterated local search based metaheuristic approach for solving sports timetabling problems of International Timetabling Competition 2021","authors":"I. Gusti Agung Premananda, Aris Tjahyanto, Ahmad Mukhlason","doi":"10.1007/s10479-024-06285-x","DOIUrl":"10.1007/s10479-024-06285-x","url":null,"abstract":"<div><p>Sports timetabling is a complex and challenging problem. The latest open benchmark dataset for the sport timetabling problem is from the International Timetabling Competition (ITC) 2021. Due to its complexity, only a few approaches have successfully generated feasible solutions for the problems in this dataset, as reported in scientific literature. To the best of our knowledge, there is only one study in the literature that has successfully generated feasible solutions for all 45 problems in the dataset. In this paper, we propose our novel efficient algorithm based on the Iterated Local Search algorithm to solve the ITC 2021 benchmark dataset. Unlike prior successful approaches that combined metaheuristics with an exact approach, our proposed approach is solely metaheuristic. Our contribution includes the design of strategies for both perturbation and local search phases, coupled with the integration of shuffling strategies. The experimental results show that our proposed algorithm is remarkably successful in generating feasible solutions for all 45 problems present in the ITC 2021 dataset.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"343 1","pages":"411 - 427"},"PeriodicalIF":4.4,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-agent proportionate flowshop scheduling with deadlines: polynomial-time optimization algorithms 带最后期限的双代理比例流水车间调度：多项式时间优化算法

IF 4.4 3区管理学

Annals of Operations Research Pub Date : 2024-09-21 DOI: 10.1007/s10479-024-06275-z

Kuo-Ching Ying, Pourya Pourhejazy, Chuan-En Sung

引用次数: 0

The unexpected power of linear programming: an updated collection of surprising applications 意想不到的线性规划的力量：一个令人惊讶的应用程序的更新集合

IF 4.4 3区管理学

Annals of Operations Research Pub Date : 2024-09-21 DOI: 10.1007/s10479-024-06245-5

Bruce Golden, Linus Schrage, Douglas Shier, Lida Anna Apergi

{"title":"The unexpected power of linear programming: an updated collection of surprising applications","authors":"Bruce Golden, Linus Schrage, Douglas Shier, Lida Anna Apergi","doi":"10.1007/s10479-024-06245-5","DOIUrl":"10.1007/s10479-024-06245-5","url":null,"abstract":"<div><p>Linear programming has had a tremendous impact in the modeling and solution of a great diversity of applied problems, especially in the efficient allocation of resources. As a result, this methodology forms the backbone of introductory courses in operations research. What students, and others, may not appreciate is that linear programming transcends its linear nomenclature and can be applied to an even wider range of important practical problems. The objective of this article is to present a selection, and just a selection, from this range of problems that at first blush do not seem amenable to linear programming formulation. The exposition focuses on the most basic models in these selected applications, with pointers to more elaborate formulations and extensions. Thus, our intent is to expand the modeling awareness of those first encountering linear programming. In addition, we hope this article will be of interest to those who teach linear programming and to seasoned academics and practitioners, alike.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"343 2021-2023)","pages":"573 - 605"},"PeriodicalIF":4.4,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-024-06245-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing mean–variance portfolio optimization through GANs-based anomaly detection

IF 4.4 3区管理学

Annals of Operations Research Pub Date : 2024-09-20 DOI: 10.1007/s10479-024-06293-x

Jang Ho Kim, Seyoung Kim, Yongjae Lee, Woo Chang Kim, Frank J. Fabozzi

引用次数: 0

Correction: Power utility maximization with expert opinions at fixed arrival times in a market with hidden gaussian drift 更正：在具有隐性高斯漂移的市场中，利用专家意见在固定到达时间实现电力效用最大化

IF 4.4 3区管理学

Annals of Operations Research Pub Date : 2024-09-19 DOI: 10.1007/s10479-024-06252-6

Abdelali Gabih, Hakam Kondakji, Ralf Wunderlich

引用次数: 0

Leveraging interpretable machine learning in intensive care 在重症监护中利用可解释的机器学习

IF 4.8 3区管理学

Annals of Operations Research Pub Date : 2024-09-19 DOI: 10.1007/s10479-024-06226-8

Lasse Bohlen, Julian Rosenberger, Patrick Zschech, Mathias Kraus

{"title":"Leveraging interpretable machine learning in intensive care","authors":"Lasse Bohlen, Julian Rosenberger, Patrick Zschech, Mathias Kraus","doi":"10.1007/s10479-024-06226-8","DOIUrl":"https://doi.org/10.1007/s10479-024-06226-8","url":null,"abstract":"<p>In healthcare, especially within intensive care units (ICU), informed decision-making by medical professionals is crucial due to the complexity of medical data. Healthcare analytics seeks to support these decisions by generating accurate predictions through advanced machine learning (ML) models, such as boosted decision trees and random forests. While these models frequently exhibit accurate predictions across various medical tasks, they often lack interpretability. To address this challenge, researchers have developed interpretable ML models that balance accuracy and interpretability. In this study, we evaluate the performance gap between interpretable and black-box models in two healthcare prediction tasks, mortality and length-of-stay prediction in ICU settings. We focus specifically on the family of generalized additive models (GAMs) as powerful interpretable ML models. Our assessment uses the publicly available Medical Information Mart for Intensive Care dataset, and we analyze the models based on (i) predictive performance, (ii) the influence of compact feature sets (i.e., only few features) on predictive performance, and (iii) interpretability and consistency with medical knowledge. Our results show that interpretable models achieve competitive performance, with a minor decrease of 0.2–0.9 percentage points in area under the receiver operating characteristic relative to state-of-the-art black-box models, while preserving complete interpretability. This remains true even for parsimonious models that use only 2.2 % of patient features. Our study highlights the potential of interpretable models to improve decision-making in ICUs by providing medical professionals with easily understandable and verifiable predictions.</p>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"19 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data science and decision analytics 数据科学和决策分析

IF 4.4 3区管理学

Annals of Operations Research Pub Date : 2024-09-18 DOI: 10.1007/s10479-024-06272-2

Victoria C.P. Chen, Seoung Bum Kim

引用次数: 0