European Journal of Operational Research最新文献_第4页

Adaptive robust online portfolio selection 自适应稳健在线投资组合选择

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-09-06 DOI: 10.1016/j.ejor.2024.09.002

Man Yiu Tsang , Tony Sit , Hoi Ying Wong

{"title":"Adaptive robust online portfolio selection","authors":"Man Yiu Tsang , Tony Sit , Hoi Ying Wong","doi":"10.1016/j.ejor.2024.09.002","DOIUrl":"10.1016/j.ejor.2024.09.002","url":null,"abstract":"<div><div>The online portfolio selection (OLPS) problem differs from classical portfolio model problems, as it involves making sequential investment decisions. Many OLPS strategies described in the literature capture market movement based on various beliefs and are shown to be profitable. In this paper, we propose a robust optimization (RO)-based strategy that takes transaction costs into account. Moreover, unlike existing studies that calibrate model parameters from benchmark data sets, we develop a novel adaptive scheme that decides the parameters sequentially. With a wide range of parameters as input, our scheme captures market uptrend and protects against market downtrend while controlling trading frequency to avoid excessive transaction costs. We numerically demonstrate the advantages of our adaptive scheme against several benchmarks under various settings. Our adaptive scheme may also be useful in general sequential decision-making problems. Finally, we compare the performance of our strategy with that of existing OLPS strategies using both benchmark and newly collected data sets. Our strategy outperforms these existing OLPS strategies in terms of cumulative returns and competitive Sharpe ratios across diversified data sets, demonstrating its adaptability-driven superiority.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

A fused large language model for predicting startup success 预测初创企业成功的融合大语言模型

IF 6.4 2区管理学

European Journal of Operational Research Pub Date : 2024-09-06 DOI: 10.1016/j.ejor.2024.09.011

Abdurahman Maarouf, Stefan Feuerriegel, Nicolas Pröllochs

{"title":"A fused large language model for predicting startup success","authors":"Abdurahman Maarouf, Stefan Feuerriegel, Nicolas Pröllochs","doi":"10.1016/j.ejor.2024.09.011","DOIUrl":"https://doi.org/10.1016/j.ejor.2024.09.011","url":null,"abstract":"Investors are continuously seeking profitable investment opportunities in startups and, hence, for effective decision-making, need to predict a startup’s probability of success. Nowadays, investors can use not only various fundamental information about a startup (e.g., the age of the startup, the number of founders, and the business sector) but also textual description of a startup’s innovation and business model, which is widely available through online venture capital (VC) platforms such as Crunchbase. To support the decision-making of investors, we develop a machine learning approach with the aim of locating successful startups on VC platforms. Specifically, we develop, train, and evaluate a tailored, fused large language model to predict startup success. Thereby, we assess to what extent self-descriptions on VC platforms are predictive of startup success. Using 20,172 online profiles from Crunchbase, we find that our fused large language model can predict startup success, with textual self-descriptions being responsible for a significant part of the predictive power. Our work provides a decision support tool for investors to find profitable investment opportunities.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Benders decomposition for bi-objective linear programs 双目标线性程序的本德斯分解

IF 6.4 2区管理学

European Journal of Operational Research Pub Date : 2024-09-06 DOI: 10.1016/j.ejor.2024.09.004

Andrea Raith, Richard Lusby, Ali Akbar Sohrabi Yousefkhan

{"title":"Benders decomposition for bi-objective linear programs","authors":"Andrea Raith, Richard Lusby, Ali Akbar Sohrabi Yousefkhan","doi":"10.1016/j.ejor.2024.09.004","DOIUrl":"https://doi.org/10.1016/j.ejor.2024.09.004","url":null,"abstract":"In this paper, we develop a new decomposition technique for solving bi-objective linear programming problems. The proposed methodology combines the bi-objective simplex algorithm with Benders decomposition and can be used to obtain a complete set of efficient extreme solutions, and the corresponding set of extreme non-dominated points, for a bi-objective linear programme. Using a Benders-like reformulation, the decomposition approach decouples the problem into a bi-objective master problem and a bi-objective subproblem, each of which is solved using the bi-objective parametric simplex algorithm. The master problem provides candidate efficient solutions that the subproblem assesses for feasibility and optimality. As in standard Benders decomposition, optimality and feasibility cuts are generated by the subproblem and guide the master problem solve. This paper discusses bi-objective Benders decomposition from a theoretical perspective, proves the correctness of the proposed reformulation and addresses the need for so-called weighted optimality cuts. Furthermore, we present an algorithm to solve the reformulation and discuss its performance for three types of bi-objective optimisation problems.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Moderate exponential-time quantum dynamic programming across the subsets for scheduling problems 针对调度问题的跨子集适度指数时间量子动态编程

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-09-05 DOI: 10.1016/j.ejor.2024.09.005

Camille Grange , Michael Poss , Eric Bourreau , Vincent T’kindt , Olivier Ploton

{"title":"Moderate exponential-time quantum dynamic programming across the subsets for scheduling problems","authors":"Camille Grange , Michael Poss , Eric Bourreau , Vincent T’kindt , Olivier Ploton","doi":"10.1016/j.ejor.2024.09.005","DOIUrl":"10.1016/j.ejor.2024.09.005","url":null,"abstract":"<div><div>Grover Search is currently one of the main quantum algorithms leading to hybrid quantum–classical methods that reduce the worst-case time complexity for some combinatorial optimization problems. Specifically, the combination of Quantum Minimum Finding (obtained from Grover Search) with dynamic programming has proved particularly efficient in improving the complexity of NP-hard problems currently solved by classical dynamic programming. For these problems, the classical dynamic programming complexity in <span><math><mrow><msup><mrow><mi>O</mi></mrow><mrow><mo>∗</mo></mrow></msup><mrow><mo>(</mo><msup><mrow><mi>c</mi></mrow><mrow><mi>n</mi></mrow></msup><mo>)</mo></mrow></mrow></math></span>, where <span><math><msup><mrow><mi>O</mi></mrow><mrow><mo>∗</mo></mrow></msup></math></span> denotes that polynomial factors are ignored, can be reduced by a hybrid algorithm to <span><math><mrow><msup><mrow><mi>O</mi></mrow><mrow><mo>∗</mo></mrow></msup><mrow><mo>(</mo><msubsup><mrow><mi>c</mi></mrow><mrow><mi>q</mi><mi>u</mi><mi>a</mi><mi>n</mi><mi>t</mi></mrow><mrow><mi>n</mi></mrow></msubsup><mo>)</mo></mrow></mrow></math></span>, with <span><math><mrow><msub><mrow><mi>c</mi></mrow><mrow><mi>q</mi><mi>u</mi><mi>a</mi><mi>n</mi><mi>t</mi></mrow></msub><mo><</mo><mi>c</mi></mrow></math></span>. In this paper, we provide a bounded-error hybrid algorithm that achieves such an improvement for a broad class of NP-hard single-machine scheduling problems for which we give a generic description. Moreover, we extend this algorithm to tackle the 3-machine flowshop problem. Our algorithm reduces the exponential-part complexity compared to the best-known classical algorithm, sometimes at the cost of an additional pseudo-polynomial factor.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Prelim p. 2; First issue - Editorial Board 预览第 2 页；第一期 - 编辑委员会

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-09-04 DOI: 10.1016/S0377-2217(24)00675-1

引用次数: 0

Measures of stochastic non-dominance in portfolio optimization 投资组合优化中的随机非优势度量

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-09-03 DOI: 10.1016/j.ejor.2024.08.029

Jana Junová, Miloš Kopa

{"title":"Measures of stochastic non-dominance in portfolio optimization","authors":"Jana Junová, Miloš Kopa","doi":"10.1016/j.ejor.2024.08.029","DOIUrl":"10.1016/j.ejor.2024.08.029","url":null,"abstract":"<div><div>Stochastic dominance rules are well-characterized and widely used. This work aims to describe and better understand the situations when they do not hold by developing measures of stochastic non-dominance. They quantify the error caused by assuming that one random variable dominates another one when it does not. To calculate them, we search for a hypothetical random variable that satisfies the stochastic dominance relationship and is as close to the original one as possible. The Wasserstein distance between the optimal hypothetical random variable and the original one is considered as the measure of stochastic non-dominance. Depending on the conditions imposed on the probability distribution of the hypothetical random variable, we distinguish between general and specific measures of stochastic non-dominance. We derive their exact values for random variables with uniform, normal, and exponential distributions. We present relations to almost first-order stochastic dominance and to tractable almost stochastic dominance. Using monthly returns of twelve assets captured by the German stock index DAX, we solve portfolio optimization problems with the first-order and second-order stochastic dominance constraints. The measures of stochastic non-dominance allow us to compare the optimal portfolios with respect to different orders of stochastic dominance from a new angle. We also defined the closest dominating and closest approximately dominating portfolios. They brought a better understanding of the relationship between the two types of optimal portfolios. Using moving window analysis, the relationship of the in-sample measure of stochastic non-dominance to out-of-sample performance was studied, too.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Quadratic horizontally elastic not-first/not-last filtering algorithm for cumulative constraint 针对累积约束的四次横向弹性非先/非后过滤算法

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-09-03 DOI: 10.1016/j.ejor.2024.09.003

Roger Kameugne , Sévérine Fetgo Betmbe , Thierry Noulamo

{"title":"Quadratic horizontally elastic not-first/not-last filtering algorithm for cumulative constraint","authors":"Roger Kameugne , Sévérine Fetgo Betmbe , Thierry Noulamo","doi":"10.1016/j.ejor.2024.09.003","DOIUrl":"10.1016/j.ejor.2024.09.003","url":null,"abstract":"<div><div>The not-first/not-last rule is a pendant of the edge finding rule, generally embedded in the <span>cumulative</span> constraint during constraint-based scheduling. It is combined with other filtering rules for more pruning of the tree search. In this paper, the <em>Profile</em> data structure in which tasks are scheduled in a horizontally elastic way is used to strengthen the classic not-first/not-last rule. Potential not-first task intervals are selected using criteria (specified later in the paper), and the <em>Profile</em> data structure is applied to selected task intervals. We prove that this new rule subsumes the classic not-first rule. A quadratic filtering algorithm is proposed for the new rule, thus improving the complexity of the horizontally elastic not-first/not-last algorithm from <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>3</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> to <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span>. The fixed part of external tasks that overlap with the selected task intervals is considered during the computation of the earliest completion time of task intervals. This improvement increases the filtering power of the algorithm while remaining quadratic. Experimental results, on a well-known suite of benchmark instances of Resource-Constrained Project Scheduling Problems (RCPSPs), show that the propounded algorithms are competitive with the state-of-the-art not-first algorithms in terms of tree search and running time reduction.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

End-to-end, decision-based, cardinality-constrained portfolio optimization 端到端、基于决策、数量受限的投资组合优化

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-09-02 DOI: 10.1016/j.ejor.2024.08.030

Hassan T. Anis, Roy H. Kwon

{"title":"End-to-end, decision-based, cardinality-constrained portfolio optimization","authors":"Hassan T. Anis, Roy H. Kwon","doi":"10.1016/j.ejor.2024.08.030","DOIUrl":"10.1016/j.ejor.2024.08.030","url":null,"abstract":"<div><div>Portfolios employing a (factor) risk model are usually constructed using a two step process: first, the risk model parameters are estimated, then the portfolio is constructed. Recent works have shown that this decoupled approach may be improved using an integrated framework that takes the downstream portfolio optimization into account during parameter estimation. In this work we implement an integrated, end-to-end, predict-&-optimize framework to the cardinality-constrained portfolio optimization problem. To the best of our knowledge, we are the first to implement the framework to a nonlinear mixed integer programming problem. Since the feasible region of the problem is discontinuous, we are unable to directly differentiate through it. Thus, we compare three different continuous relaxations of increasing tightness to the problem which are placed as an implicit layers in a neural network. The parameters of the factor model governing the problem’s covariance matrix structure are learned using a loss function that directly corresponds to the decision quality made based on the factor model’s predictions. Using real world financial data, our proposed end-to-end, decision based model is compared to two decoupled alternatives. Results show significant improvements over the traditional decoupled approaches across all cardinality sizes and model variations while highlighting the need of additional research into the interplay between experimental design, problem size and structure, and relaxation tightness in a combinatorial setting.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Explainable profit-driven hotel booking cancellation prediction based on heterogeneous stacking-based ensemble classification 基于异构堆积集合分类的可解释利润驱动型酒店预订取消预测

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-08-31 DOI: 10.1016/j.ejor.2024.08.026

Zhenkun Liu , Koen W. De Bock , Lifang Zhang

{"title":"Explainable profit-driven hotel booking cancellation prediction based on heterogeneous stacking-based ensemble classification","authors":"Zhenkun Liu , Koen W. De Bock , Lifang Zhang","doi":"10.1016/j.ejor.2024.08.026","DOIUrl":"10.1016/j.ejor.2024.08.026","url":null,"abstract":"<div><div>The goal of hotel booking cancellation prediction in the hospitality industry is to identify potential cancellations from a large customer base and improve the efficiency of customer retention and capacity management efforts. Whilst prior research has shown that the predictive performance of hotel booking cancellation prediction can be further enhanced by integrating multiple classifiers, the explainability of such models is limited due to low interpretability and limited alignment with company goals. To address this limitation, we propose a novel heterogeneous linear stacking ensemble classifier for profit-driven hotel booking cancellation prediction. It enhances classifier explainability by (1) making models more accountable by axing model training towards profitability and (2) complementing models by global post-hoc model interpretation strategies. Through experiments based on real-world datasets, our proposed classification framework is demonstrated to lead to greater profits than other profit-oriented predictive models. Moreover, an in-depth interpretability analysis demonstrates the framework's ability to identify critical factors significantly impacting hotel cancellations, providing valuable insights for retention campaigns.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Industry return prediction via interpretable deep learning 通过可解释深度学习进行行业回报预测

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-08-31 DOI: 10.1016/j.ejor.2024.08.032

Lazaros Zografopoulos , Maria Chiara Iannino , Ioannis Psaradellis , Georgios Sermpinis

引用次数: 0