European Journal of Operational Research最新文献_第9页

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":"321 1","pages":"Pages 269-283"},"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":"320 3","pages":"Pages 505-515"},"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":"320 3","pages":"Pages 739-753"},"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":"321 1","pages":"Pages 284-301"},"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

Integration of prediction and optimization for smart stock portfolio selection 预测与优化相结合，实现智能股票投资组合选择

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-08-30 DOI: 10.1016/j.ejor.2024.08.027

Puja Sarkar , Vivekanand B. Khanapuri , Manoj Kumar Tiwari

{"title":"Integration of prediction and optimization for smart stock portfolio selection","authors":"Puja Sarkar , Vivekanand B. Khanapuri , Manoj Kumar Tiwari","doi":"10.1016/j.ejor.2024.08.027","DOIUrl":"10.1016/j.ejor.2024.08.027","url":null,"abstract":"<div><div>Machine learning (ML) algorithms pose significant challenges in predicting unknown parameters for optimization models in decision-making scenarios. Conventionally, prediction models are optimized independently in decision-making processes, whereas ML algorithms primarily focus on minimizing prediction errors, neglecting the role of decision-making in downstream optimization tasks. The pursuit of high prediction accuracy may not always align with the goal of reducing decision errors. The idea of reducing decision errors has been proposed to address this limitation. This paper introduces an optimization process that integrates predictive regression models within a mean–variance optimization setting. This innovative technique introduces a general loss function to capture decision errors. Consequently, the predictive model not only focuses on forecasting unknown optimization parameters but also emphasizes the predicted values that minimize decision errors. This approach prioritizes decision accuracy over the potential accuracy of unknown parameter prediction. In contrast to traditional ML approaches that minimize standard loss functions such as mean squared error, our proposed model seeks to minimize the objective value derived directly from the decision-making problem. Furthermore, this strategy is validated by developing an optimization-based regression tree model for predicting stock returns and reducing decision errors. Empirical evaluations of our framework reveal its superiority over conventional regression tree methods, demonstrating enhanced decision quality. The computational experiments are conducted on a stock market dataset to compare the effectiveness of the proposed framework with the conventional regression tree-based approach. Remarkably, the results confirm the strengths inherent in this holistic approach.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"321 1","pages":"Pages 243-256"},"PeriodicalIF":6.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144396","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

Asymptotically optimal energy consumption and inventory control in a make-to-stock manufacturing system 从生产到库存制造系统中的渐进最优能耗和库存控制

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-08-28 DOI: 10.1016/j.ejor.2024.08.028

Erhun Özkan , Barış Tan

{"title":"Asymptotically optimal energy consumption and inventory control in a make-to-stock manufacturing system","authors":"Erhun Özkan , Barış Tan","doi":"10.1016/j.ejor.2024.08.028","DOIUrl":"10.1016/j.ejor.2024.08.028","url":null,"abstract":"<div><p>We study a make-to-stock manufacturing system in which a single server makes the production. The server consumes energy, and its power consumption depends on the server state: a busy server consumes more power than an idle server, and an idle server consumes more power than a turned-off server. When a server is turned on, it completes a costly set-up process that lasts a while. We jointly control the finished goods inventory and the server’s energy consumption. The objective is to minimize the long-run average inventory holding, backorder, and energy consumption costs by deciding when to produce, when to idle or turn off the server, and when to turn on a turned-off server. Because the exact analysis of the problem is challenging, we consider the asymptotic regime in which the server is in the conventional heavy-traffic regime. We formulate a Brownian control problem (BCP) with impulse and singular controls. In the BCP, the impulse control appears due to server shutdowns, and the singular control appears due to server idling. Depending on the system parameters, the optimal BCP solution is either a control-band or barrier policy. We propose a simple heuristic control policy from the optimal BCP solution that can easily be implemented in the original (non-asymptotic) system. Furthermore, we prove the asymptotic optimality of the proposed control policy in a Markovian setting. Finally, we show that our proposed policy performs close to optimal in numerical experiments.</p></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"320 2","pages":"Pages 375-388"},"PeriodicalIF":6.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144398","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

Multiple financial analyst opinions aggregation based on uncertainty-aware quality evaluation 基于不确定性感知质量评估的多个金融分析师意见汇总

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-08-28 DOI: 10.1016/j.ejor.2024.08.024

Shuai Jiang , Wenjun Zhou , Yanhong Guo , Hui Xiong

{"title":"Multiple financial analyst opinions aggregation based on uncertainty-aware quality evaluation","authors":"Shuai Jiang , Wenjun Zhou , Yanhong Guo , Hui Xiong","doi":"10.1016/j.ejor.2024.08.024","DOIUrl":"10.1016/j.ejor.2024.08.024","url":null,"abstract":"<div><div>Financial analysts’ opinions are pivotal in investment decision-making, as they provide valuable expert knowledge. Aggregating these opinions offers a promising way to unlock their collective wisdom. However, existing opinion aggregation methods are hindered by their inability to effectively assess differences in opinion quality, resulting in suboptimal outcomes. This Study introduces a novel model called SmartMOA, which addresses this limitation by automatically evaluating the quality of each opinion and integrating this evaluation into the aggregation process. Our model begins with a novel Bayesian neural network that leverages the implicit knowledge embedded in the interactions between analysts and stock characteristics. This methodology produces an assessment of individual opinions that accounts for uncertainties. We then formulate a bi-objective combinatorial optimization problem to determine optimal weights for combining multiple analysts’ opinions, simultaneously minimizing the error and uncertainty of the aggregated outcome. Therefore, SmartMOA systematically highlights high-quality opinions during the aggregation process. Using a real dataset spanning eight years, we present comprehensive empirical evidence that demonstrates the superior performance of SmartMOA in heterogeneous analyst opinion aggregation.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"320 3","pages":"Pages 720-738"},"PeriodicalIF":6.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101091","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

An optimization framework for solving large scale multidemand multidimensional knapsack problem instances employing a novel core identification heuristic 利用新颖的核心识别启发式解决大规模多段多维knapsack问题实例的优化框架

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-08-28 DOI: 10.1016/j.ejor.2024.08.025

Sameh Al-Shihabi

{"title":"An optimization framework for solving large scale multidemand multidimensional knapsack problem instances employing a novel core identification heuristic","authors":"Sameh Al-Shihabi","doi":"10.1016/j.ejor.2024.08.025","DOIUrl":"10.1016/j.ejor.2024.08.025","url":null,"abstract":"<div><div>By applying the core concept to solve a binary integer program (BIP), certain variables of the BIP are fixed to their anticipated values in the optimal solution. In contrast, the remaining variables, called core variables, are used to construct and solve a core problem (CP) instead of the BIP. A new approach for identifying CP utilizing a local branching (LB) alike constraint is presented in this article. By including the LB-like constraint in the linear programming relaxation of the BIP, this method transfers batches of variables to the set of core variables by analyzing changes to their reduced costs. This approach is sensitive to problem hardness because more variables are moved to the core set for hard problems compared to easy ones. This novel core identification approach is embedded in a multi-stage framework to solve the multidemand, multidimensional knapsack problems (MDMKP), where at each stage, more variables are added to the previous stage CP. The default branch and bound of <em>CPLEX20.10</em> is used to solve the first stage, and a tabu search algorithm is used to solve subsequent stages until all variables are added to CP in the last stage. The new framework has shown equivalent to superior results compared to the state-of-the-art algorithms in solving large MDMKP instances having 500 and 1,000 variables.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"320 3","pages":"Pages 496-504"},"PeriodicalIF":6.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144399","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

The circular balancing problem 循环平衡问题

IF 6 2区管理学

European Journal of Operational Research Pub Date : 2024-08-24 DOI: 10.1016/j.ejor.2024.08.020

Myungho Lee , Kangbok Lee , Michael Pinedo

{"title":"The circular balancing problem","authors":"Myungho Lee , Kangbok Lee , Michael Pinedo","doi":"10.1016/j.ejor.2024.08.020","DOIUrl":"10.1016/j.ejor.2024.08.020","url":null,"abstract":"<div><div>We propose a balancing problem with a minmax objective in a circular setting. This balancing problem involves the arrangement of an even number of items with different weights on a circle while minimizing the maximum total weight of items arranged on any half circle. Due to its generic structure, it may have applications in fair resource allocation schemes. We show the NP-hardness of the problem and develop polynomial-time algorithms when the number of distinct weights is a fixed constant. We propose for the general case a tight <span><math><mrow><mn>7</mn><mo>/</mo><mn>6</mn></mrow></math></span>-approximation algorithm and show that it performs better than two existing algorithms designed for an equivalent problem in the literature. The worst-case performance ratio is derived through a linear combination of valid inequalities that are obtained from the problem definition, the properties of the proposed algorithm, and the optimal circular permutation structure. Furthermore, we formulate a more general problem of minimizing the maximum total weight of items on equally divided circular sectors and present its computational complexity and a tight approximation algorithm.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"321 1","pages":"Pages 41-56"},"PeriodicalIF":6.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231674","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