Computational Management Science最新文献_第6页

Why there is no need to use a big-M in linear bilevel optimization: a computational study of two ready-to-use approaches 为什么在线性双层优化中不需要使用big-M：两种现成方法的计算研究

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Computational Management Science Pub Date : 2023-02-07 DOI: 10.1007/s10287-023-00435-5

Thomas Kleinert, Martin Schmidt

引用次数: 13

New criteria for existence of solutions for equilibrium problems 平衡问题解存在性的新判据

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Computational Management Science Pub Date : 2023-02-06 DOI: 10.1007/s10287-023-00433-7

M. Balaj, M. Castellani, M. Giuli

引用次数: 2

Wasserstein barycenter regression for estimating the joint dynamics of renewable and fossil fuel energy indices. Wasserstein重心回归用于估计可再生能源和化石燃料能源指数的联合动力学。

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Computational Management Science Pub Date : 2023-01-01 Epub Date: 2023-02-04 DOI: 10.1007/s10287-023-00436-4

Maria Elena De Giuli, Alessandro Spelta

引用次数: 1

Using machine learning prediction models for quality control: a case study from the automotive industry. 使用机器学习预测模型进行质量控制:来自汽车行业的案例研究。

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Computational Management Science Pub Date : 2023-01-01 DOI: 10.1007/s10287-023-00448-0

Mohamed Kais Msakni, Anders Risan, Peter Schütz

{"title":"Using machine learning prediction models for quality control: a case study from the automotive industry.","authors":"Mohamed Kais Msakni, Anders Risan, Peter Schütz","doi":"10.1007/s10287-023-00448-0","DOIUrl":"https://doi.org/10.1007/s10287-023-00448-0","url":null,"abstract":"This paper studies a prediction problem using time series data and machine learning algorithms. The case study is related to the quality control of bumper beams in the automotive industry. These parts are milled during the production process, and the locations of the milled holes are subject to strict tolerance limits. Machine learning models are used to predict the location of milled holes in the next beam. By doing so, tolerance violations are detected at an early stage, and the production flow can be improved. A standard neural network, a long short term memory network (LSTM), and random forest algorithms are implemented and trained with historical data, including a time series of previous product measurements. Experiments indicate that all models have similar predictive capabilities with a slight dominance for the LSTM and random forest. The results show that some holes can be predicted with good quality, and the predictions can be used to improve the quality control process. However, other holes show poor results and support the claim that real data problems are challenged by inappropriate information or a lack of relevant information.","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"20 1","pages":"14"},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9149900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with Conditional Value-at-Risk (CVaR) constraint. 非人寿保险的最低资本要求和投资组合分配：具有条件风险价值（CVaR）约束的半参数模型。

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Computational Management Science Pub Date : 2023-01-01 Epub Date: 2023-03-03 DOI: 10.1007/s10287-023-00439-1

Alessandro Staino, Emilio Russo, Massimo Costabile, Arturo Leccadito

{"title":"Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with Conditional Value-at-Risk (CVaR) constraint.","authors":"Alessandro Staino, Emilio Russo, Massimo Costabile, Arturo Leccadito","doi":"10.1007/s10287-023-00439-1","DOIUrl":"10.1007/s10287-023-00439-1","url":null,"abstract":"We present an optimization problem to determine the minimum capital requirement for a non-life insurance company. The optimization problem imposes a non-positive Conditional Value-at-Risk (CVaR) of the insurer's net loss and a portfolio performance constraint. When expressing the optimization problem in a semiparametric form, we demonstrate its convexity for any integrable random variable representing the insurer's liability. Furthermore, we prove that the function defining the CVaR constraint in the semiparametric formulation is continuously differentiable when the insurer's liability has a continuous distribution. We use the Kelley-Cheney-Goldstein algorithm to solve the optimization problem in the semiparametric form and show its convergence. An empirical analysis is carried out by assuming three different liability distributions: a lognormal distribution, a gamma distribution, and a mixture of Erlang distributions with a common scale parameter. The numerical experiments show that the choice of the liability distribution plays a crucial role since marked differences emerge when comparing the mixture distribution with the other two distributions. In particular, the mixture distribution describes better the right tail of the empirical distribution of liabilities with respect to the other two distributions and implies higher capital requirements and different assets in the optimal portfolios.","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"20 1","pages":"12"},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9898288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Lagrangian relaxation to locate hydrogen production facilities under uncertain demand: a case study from Norway. 利用拉格朗日弛豫在不确定需求下定位氢气生产设施:以挪威为例。

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Computational Management Science Pub Date : 2023-01-01 DOI: 10.1007/s10287-023-00445-3

Šárka Štádlerová, Sanjay Dominik Jena, Peter Schütz

{"title":"Using Lagrangian relaxation to locate hydrogen production facilities under uncertain demand: a case study from Norway.","authors":"Šárka Štádlerová, Sanjay Dominik Jena, Peter Schütz","doi":"10.1007/s10287-023-00445-3","DOIUrl":"https://doi.org/10.1007/s10287-023-00445-3","url":null,"abstract":"Hydrogen is considered a solution to decarbonize the transportation sector, an important step to meet the requirements of the Paris agreement. Even though hydrogen demand is expected to increase over the next years, the exact demand level over time remains a main source of uncertainty. We study the problem of where and when to locate hydrogen production plants to satisfy uncertain future customer demand. We formulate our problem as a two-stage stochastic multi-period facility location and capacity expansion problem. The first-stage decisions are related to the location and initial capacity of the production plants and have to be taken before customer demand is known. They involve selecting a modular capacity with a piecewise linear, convex short-term cost function for the chosen capacity level. In the second stage, decisions regarding capacity expansion and demand allocation are taken. Given the complexity of the formulation, we solve the problem using a Lagrangian decomposition heuristic. Our method is capable of finding solutions of sufficiently high quality within a few hours, even for instances too large for commercial solvers. We apply our model to a case from Norway and design the corresponding hydrogen infrastructure for the transportation sector.","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"20 1","pages":"10"},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9100423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Insurance premium-based shortfall risk measure induced by cumulative prospect theory 基于累积前景理论的基于保险费的缺口风险测度

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Computational Management Science Pub Date : 2022-10-01 DOI: 10.1007/s10287-022-00432-0

Sainan Zhang, Huifu Xu

引用次数: 0

An agricultural investment problem subject to probabilistic constraints 一个受概率约束的农业投资问题

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Computational Management Science Pub Date : 2022-10-01 DOI: 10.1007/s10287-022-00431-1

Kawtar El Karfi, R. Henrion, D. Mentagui

引用次数: 0

Forecasting financial time series with Boltzmann entropy through neural networks 基于Boltzmann熵的神经网络金融时间序列预测

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Computational Management Science Pub Date : 2022-09-13 DOI: 10.1007/s10287-022-00430-2

L. Grilli, D. Santoro

引用次数: 1

Online decision making for trading wind energy 风能交易的在线决策

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Computational Management Science Pub Date : 2022-09-05 DOI: 10.1007/s10287-023-00462-2

Miguel Angel Muñoz, P. Pinson, J. Kazempour

引用次数: 1