Annals of Operations Research最新文献

{"title":"Projection methods for finding the greatest element of the intersection of max-closed convex sets","authors":"Tomáš Dlask","doi":"10.1007/s10479-024-05980-z","DOIUrl":"10.1007/s10479-024-05980-z","url":null,"abstract":"<div><p>We focus on the problem of finding the greatest element of the intersection of max-closed convex sets. For this purpose, we analyze the famous method of cyclic projections and show that, if this method is suitably initialized and applied to max-closed convex sets, it converges to the greatest element of their intersection. Moreover, we propose another projection method, called the decreasing projection, which turns out both theoretically and practically preferable to Euclidean projections in this particular case. Next, we argue that several known algorithms, such as Bellman-Ford and Floyd-Warshall algorithms for shortest paths or Gauss-Seidel variant of value iteration in Markov decision processes, can be interpreted as special cases of iteratively applying decreasing projections onto certain max-closed convex sets. Finally, we link decreasing projections (and thus also the aforementioned algorithms) to bounds consistency in constraint programming.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"340 2-3","pages":"811 - 836"},"PeriodicalIF":4.4,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-024-05980-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746229","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}

{"title":"A new mathematical model for designing and improving the performance of a home health care logistics network","authors":"Esmaeil Akhondi-Bajegani,&nbsp;F. Jolai,&nbsp;S. Ali Torabi","doi":"10.1007/s10479-024-06135-w","DOIUrl":"10.1007/s10479-024-06135-w","url":null,"abstract":"<div><p>To provide better care for the elderly, health trustees establish home health care centers in cities. These centers offer services at homes, and a logistics network is essential for providing these services. An integrated framework is developed to design such a network by which various logistical decisions are made under inherent epistemic uncertainty of the travel and service times. A new mixed integer linear programming model is developed, and its equivalent deterministic formulation is obtained using a well-applied possibilistic programming approach. An improved genetic algorithm is also devised to solve large-sized instances and various sensitivity analyses are performed on key factors. Finally, a case study is presented to validate the developed model and its solution method. The numerical results demonstrate 20% reduction in the total time of employing care givers at almost 3.5% cost savings.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"340 2-3","pages":"1189 - 1220"},"PeriodicalIF":4.4,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141642600","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}

{"title":"Simulation-based system reliability estimation of a multi-state flow network for all possible demand levels","authors":"Ping-Chen Chang,&nbsp;Ding-Hsiang Huang,&nbsp;Cheng-Fu Huang","doi":"10.1007/s10479-024-06141-y","DOIUrl":"10.1007/s10479-024-06141-y","url":null,"abstract":"<div><p>The multi-state flow network (MSFN) serves as a fundamental framework for real-life network-structured systems and various applications. The system reliability of the MSFN, denoted as <i>R</i>_<i>d</i>, is defined as the probability of successfully transmitting at least <i>d</i> units of demand from a source to a terminal. Current analytical algorithms are characterized by their computational complexity, specifically falling into the NP-hard problem to evaluate exact system reliability. Moreover, existing analytical algorithms for calculating <i>R</i>_<i>d</i> are basically designed for predetermined values of <i>d</i>. This limitation hinders the ability of decision-makers to flexibly choose the most appropriate based on the specific characteristics of the given scenarios or applications. This means that these methods are incapable of simultaneously calculating system reliability for various demand levels. Therefore, this paper develops a simulation-based algorithm to estimate system reliability for all possible demand levels simultaneously such that we can eliminate the need to rely on repeat procedures for each specified <i>d</i>. An experimental investigation was carried out on a benchmark network and a practical network to validate the effectiveness and performance of the proposed algorithm.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"340 1","pages":"117 - 132"},"PeriodicalIF":4.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614553","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}

{"title":"Positivity and convexity in incomplete cooperative games","authors":"Martin Černý,&nbsp;Jan Bok,&nbsp;David Hartman,&nbsp;Milan Hladík","doi":"10.1007/s10479-024-06082-6","DOIUrl":"10.1007/s10479-024-06082-6","url":null,"abstract":"<div><p>Incomplete cooperative games generalize the classical model of cooperative games by omitting the values of some of the coalitions. This allows for incorporating uncertainty into the model and studying the underlying games and possible payoff distributions based only on the partial information. In this paper, we conduct a systematic investigation of incomplete games, focusing on two important classes: positive and convex games. Regarding positivity, we generalize previous results from a special class of minimal incomplete games to a general setting. We characterize the non-extendability to a positive game by the existence of a certificate and provide a description of the set of positive extensions using its extreme games. These results also enable the construction of explicit formulas for several classes of incomplete games with special structures. The second part deals with convexity. We begin with the case of non-negative, minimal incomplete games. We establish the connection between incomplete games and the problem of completing partial functions and, consequently, provide a characterization of extendability and a full description of the set of symmetric convex extensions. This set serves as an approximation of the set of convex extensions.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"340 2-3","pages":"785 - 809"},"PeriodicalIF":4.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-024-06082-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614556","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}

{"title":"Requirement-driven supplier selection: a multi-criteria QFD-based approach under epistemic and stochastic uncertainties","authors":"Jian-Peng Chang,&nbsp;Heng-Xin Ren,&nbsp;Luis Martínez,&nbsp;Witold Pedrycz,&nbsp;Zhen-Song Chen","doi":"10.1007/s10479-024-06131-0","DOIUrl":"10.1007/s10479-024-06131-0","url":null,"abstract":"<div><p>Supplier selection (SS) has emerged as a critical challenge for companies aiming to enhance the operational management of their supply chains, a task that has grown in complexity with the advent of Industry 4.0 and the ongoing digital transformation. Recognizing the gaps in current literature—specifically, the lack of consideration for stakeholders' expectations in guiding SS, as well as the inadequate handling of epistemic and stochastic uncertainties—this paper introduces a multiple-criteria Quality Function Deployment (QFD)-based model for SS. To address epistemic uncertainty, we put forward a novel subjective judgment representation method, which is named as linguistic term set integrated with discrete subjective probability distribution (LTS-DSPD), to enable decision-makers to express their judgments in a manner that is both simpler and more nuanced. Furthermore, we also give the elicitation methods and computing techniques for LTS-DSPD. Then, we integrate stakeholders’ requirements, along with their preferences and expectations for these requirements to inform and guide SS. To effectively operationalize this guidance, we design the QFD-based methods to transform stakeholders' inputs into the assessment criteria for SS, the weights of criteria, and the expectations for the performances of suppliers on each criterion, respectively. To address stochastic uncertainty, we have developed an innovative methodology for characterizing it, and adopt prospect theory to quantify the overall utility of alternative suppliers. The paper concludes with a case study to demonstrate its practical application and effectiveness in streamlining SS process.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"342 2","pages":"1079 - 1128"},"PeriodicalIF":4.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614554","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}

{"title":"Queueing-inventory: analytical and simulation modeling and classical and retrial queues and inventory","authors":"Achyutha Krishnamoorthy,&nbsp;Srinivas R. Chakravarthy,&nbsp;Agassi Melikov,&nbsp;Viswanath C. Narayanan","doi":"10.1007/s10479-024-06015-3","DOIUrl":"10.1007/s10479-024-06015-3","url":null,"abstract":"<div><p>This is the PREFACE to the Special Issue “Queueing-inventory: analytical and simulation modeling and classical and retrial queues and inventory”. The year 2022 was the 30th anniversary of Queueing-inventory. It was in that connection that the guest editors requested Professor Endre Boros, the Editor-in-Chief of Annals of Operations Research, for permission to guest edit a special issue. This was granted by the Editorial Board of the journal.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"340 2-3","pages":"1221 - 1224"},"PeriodicalIF":4.4,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585209","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}

{"title":"Enabling business sustainability for stock market data using machine learning and deep learning approaches","authors":"S. Divyashree,&nbsp;Christy Jackson Joshua,&nbsp;Abdul Quadir Md,&nbsp;Senthilkumar Mohan,&nbsp;A. Sheik Abdullah,&nbsp;Ummul Hanan Mohamad,&nbsp;Nisreen Innab,&nbsp;Ali Ahmadian","doi":"10.1007/s10479-024-06118-x","DOIUrl":"10.1007/s10479-024-06118-x","url":null,"abstract":"<div><p>This paper introduces AlphaVision, an innovative decision support model designed for stock price prediction by seamlessly integrating real-time news updates and Return on Investment (ROI) values, utilizing various machine learning and deep learning approaches. The research investigates the application of these techniques to enhance the effectiveness of stock trading and investment decisions by accurately anticipating stock prices and providing valuable insights to investors and businesses. The study begins by analyzing the complexities and challenges of stock market analysis, considering factors like political, macroeconomic, and legal issues that contribute to market volatility. To address these challenges, we proposed the methodology called AlphaVision, which incorporates various machine learning algorithms, including Decision Trees, Random Forest, Naïve Bayes, Boosting, K-Nearest Neighbors, and Support Vector Machine, alongside deep learning models such as Multi-layer Perceptron (MLP), Artificial Neural Networks, and Recurrent Neural Networks. The effectiveness of each model is evaluated based on their accuracy in predicting stock prices. Experimental results revealed that the MLP model achieved the highest accuracy of approximately 92%, outperforming other deep learning models. The Random Forest algorithm also demonstrated promising results with an accuracy of around 84.6%. These findings indicate the potential of machine learning and deep learning techniques in improving stock market analysis and prediction. The AlphaVision methodology presented in this research empowers investors and businesses with valuable tools to make informed investment decisions and navigate the complexities of the stock market. By accurately forecasting stock prices based on news updates and ROI values, the model contributes to better financial management and business sustainability. The integration of machine learning and deep learning approaches offers a promising solution for enhancing stock market analysis and prediction. Future research will focus on extracting more relevant financial features to further improve the model’s accuracy. By advancing decision support models for stock price prediction, researchers and practitioners can foster better investment strategies and foster economic growth. The proposed model holds potential to revolutionize stock trading and investment practices, enabling more informed and profitable decision-making in the financial sector.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"342 1","pages":"287 - 322"},"PeriodicalIF":4.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553222","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}

{"title":"Pricing heterogeneous products to heterogeneous customers who buy sequentially","authors":"Refael Hassin,&nbsp;Justo Puerto","doi":"10.1007/s10479-024-06133-y","DOIUrl":"10.1007/s10479-024-06133-y","url":null,"abstract":"<div><p>This paper considers optimal pricing in a system with limited substitutable resources, such as certain goods or services. Prices for the different resources have to be set and then customers with heterogeneous preferences show up sequentially. Customers, of <i>n</i> types, select an item from the <i>m</i> available resources, depending on their valuations of the resources and the prices. The goal is to analyze this optimization problem, characterize a set of candidates to optimal solutions and provide methods for solving it. We prove that this problem is NP-hard to approximate within a factor <span>(O(n^{1-varepsilon }))</span> for any fixed <span>(varepsilon &gt;0)</span>. Another important contribution is to prove that, the space of prices (which in principle is a continuous domain in <span>({mathbb {R}}^m)</span>), can be reduced to a finite set of vectors of cardinality <span>(m^{m-2}n^m2^m)</span>. For a deterministic version of the problem, where the customer types are known to the firm, we provide a mathematical program that chooses the best set of prices. We report extensive computational results showing the usefulness of our exact approach to solve medium size problems with up to 200 customers and different assortments of products and customer types. We then show how to approximate the stochastic model by a small number of solutions of deterministic scenarios solved using a mixed-integer linear program.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"340 2-3","pages":"863 - 890"},"PeriodicalIF":4.4,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-024-06133-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527676","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}

{"title":"Data analytics-based auditing: a case study of fraud detection in the banking context","authors":"Jean Robert Kala Kamdjoug,&nbsp;Hyacinthe Djanan Sando,&nbsp;Jules Raymond Kala,&nbsp;Arielle Ornela Ndassi Teutio,&nbsp;Sunil Tiwari,&nbsp;Samuel Fosso Wamba","doi":"10.1007/s10479-024-06129-8","DOIUrl":"10.1007/s10479-024-06129-8","url":null,"abstract":"<div><p>For a long time, decision-making in auditing was limited to a risk-oriented recommendation and consisted of the rigorous analysis of a sample of data. The new trend in the audit decision process focuses on the use of decision support systems (DSSs) founded on data analytics (DA) to better concentrate on in-depth analysis. This study examines how DA can improve the audit decision-making approach in the banking sector. We show that DA techniques can improve the quality of audit decision-making within banks and highlight the advantages associated with mastering these techniques, which results in a more effective and efficient audit of digital banking transactions. We propose an artifact-based data analytics-driven decision support system (DA-DSS) for an automatic fraud detection system supported by DA. The proposed DA-DSS artifact with a random forest classifier at its core is a promising innovation in the field of electronic transaction fraud detection. The results show that the random forest classifier can accurately classify the data generated by this artifact with an accuracy varying from 88 to 93% using transaction data collected from 2021 to 2022. Other classifiers including k-nearest neighbors (KNN) are also used, with a classification rate ranging from 66 to 88% for the same transaction datasets. These results show that the proposed DA-DSS with random forest can significantly improve auditing by reducing the time required for data analysis and increasing the results’ accuracy. Management can use the proposed artifact to enhance and speed up the decision-making process within their organization.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"340 2-3","pages":"1161 - 1188"},"PeriodicalIF":4.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527811","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}

{"title":"Correction: Quantifying and reducing the complexity of multi-line charts as a visual aid in multi-criteria decision-making","authors":"He Huang,&nbsp;Sajid Siraj","doi":"10.1007/s10479-024-06121-2","DOIUrl":"10.1007/s10479-024-06121-2","url":null,"abstract":"","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"341 2-3","pages":"1351 - 1351"},"PeriodicalIF":4.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-024-06121-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438829","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}