Pub Date : 2023-10-07DOI: 10.1007/s11081-023-09855-3
Jörg Lampe, Steffen Menz
{"title":"Optimized operational strategy of a solar reactor for thermochemical hydrogen generation","authors":"Jörg Lampe, Steffen Menz","doi":"10.1007/s11081-023-09855-3","DOIUrl":"https://doi.org/10.1007/s11081-023-09855-3","url":null,"abstract":"","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135254747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-29DOI: 10.1007/s11081-023-09849-1
Vrinda Dhingra, Amita Sharma, Shiv K. Gupta
{"title":"Sectoral portfolio optimization by judicious selection of financial ratios via PCA","authors":"Vrinda Dhingra, Amita Sharma, Shiv K. Gupta","doi":"10.1007/s11081-023-09849-1","DOIUrl":"https://doi.org/10.1007/s11081-023-09849-1","url":null,"abstract":"","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-28DOI: 10.1007/s11081-023-09850-8
F. Javier Toledo, Vicente Galiano, Victoria Herranz, Jose M. Blanes, Efstratios Batzelis
Abstract In this paper we describe and compare the methods for the calculation of all the key points of the photovoltaic single-diode model. These include the short-circuit point, the open-circuit point, the maximum power point, the mean slope point, the maximum curvature point, and the jerk point. The main contribution of this paper is a new algorithm to obtain the maximum power point which is based on reducing its computation to solve a single-variable equation. Its unique solution leads to an explicit expression of the point by using a recent parametrization of the single-diode model current–voltage curve. In the numerical resolution of the previous equation, we will use as starting point the mean slope point which has been proved to be close to the maximum power point. Previously, we will provide for the first time in the literature an exact and explicit expression of the mean slope point. The new algorithm proposed reaches the accuracy of the best known numerical methods, but it is much faster, almost reaching the execution times of explicit formulas.
{"title":"A comparison of methods for the calculation of all the key points of the PV single-diode model including a new algorithm for the maximum power point","authors":"F. Javier Toledo, Vicente Galiano, Victoria Herranz, Jose M. Blanes, Efstratios Batzelis","doi":"10.1007/s11081-023-09850-8","DOIUrl":"https://doi.org/10.1007/s11081-023-09850-8","url":null,"abstract":"Abstract In this paper we describe and compare the methods for the calculation of all the key points of the photovoltaic single-diode model. These include the short-circuit point, the open-circuit point, the maximum power point, the mean slope point, the maximum curvature point, and the jerk point. The main contribution of this paper is a new algorithm to obtain the maximum power point which is based on reducing its computation to solve a single-variable equation. Its unique solution leads to an explicit expression of the point by using a recent parametrization of the single-diode model current–voltage curve. In the numerical resolution of the previous equation, we will use as starting point the mean slope point which has been proved to be close to the maximum power point. Previously, we will provide for the first time in the literature an exact and explicit expression of the mean slope point. The new algorithm proposed reaches the accuracy of the best known numerical methods, but it is much faster, almost reaching the execution times of explicit formulas.","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-27DOI: 10.1007/s11081-023-09852-6
Adrian Diaz, Nathaniel Morgan, John Bernardin
{"title":"Parallel 3D topology optimization with multiple constraints and objectives","authors":"Adrian Diaz, Nathaniel Morgan, John Bernardin","doi":"10.1007/s11081-023-09852-6","DOIUrl":"https://doi.org/10.1007/s11081-023-09852-6","url":null,"abstract":"","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-27DOI: 10.1007/s11081-023-09834-8
Luca Mencarelli, Julien Floquet, Frédéric Georges
Abstract In this paper, we propose two novel matheuristic algorithms, i.e., heuristics based on mathematical formulations of the problem, in order to find a good feasible solution to the satellite constellation design problem for discontinuous coverage with a constrained revisit time. This problem consists in searching for a constellation able to periodically observe several targets at the Earth surface with the smallest number of satellites achievable. A Feasibility Pump approach is described: we specifically adapt the Feasibility Pump procedure to our design problem and we report results highlighting the benefits of this approach compared to the base Mixed Integer Nonlinear Programming (MINLP) algorithm it is derived from. Then, we propose a second matheuristic based on the discretized Mixed Integer Linear Programming (MILP) formulation of the problem, which outperforms the plain MILP formulation.
{"title":"Matheuristics approaches for the satellite constellation design problem","authors":"Luca Mencarelli, Julien Floquet, Frédéric Georges","doi":"10.1007/s11081-023-09834-8","DOIUrl":"https://doi.org/10.1007/s11081-023-09834-8","url":null,"abstract":"Abstract In this paper, we propose two novel matheuristic algorithms, i.e., heuristics based on mathematical formulations of the problem, in order to find a good feasible solution to the satellite constellation design problem for discontinuous coverage with a constrained revisit time. This problem consists in searching for a constellation able to periodically observe several targets at the Earth surface with the smallest number of satellites achievable. A Feasibility Pump approach is described: we specifically adapt the Feasibility Pump procedure to our design problem and we report results highlighting the benefits of this approach compared to the base Mixed Integer Nonlinear Programming (MINLP) algorithm it is derived from. Then, we propose a second matheuristic based on the discretized Mixed Integer Linear Programming (MILP) formulation of the problem, which outperforms the plain MILP formulation.","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.1007/s11081-023-09846-4
Nabil Nahas, Haifa Rekik, Safa Bhar Layeb, Mohammed Abouheaf, Ibrahim Najum
{"title":"A multi-vendor multi-buyer integrated production-inventory model with greenhouse gas emissions","authors":"Nabil Nahas, Haifa Rekik, Safa Bhar Layeb, Mohammed Abouheaf, Ibrahim Najum","doi":"10.1007/s11081-023-09846-4","DOIUrl":"https://doi.org/10.1007/s11081-023-09846-4","url":null,"abstract":"","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-23DOI: 10.1007/s11081-023-09836-6
Sajad Fathi Hafshejani, Daya Gaur, Shahadat Hossain, Robert Benkoczi
{"title":"A fast non-monotone line search for stochastic gradient descent","authors":"Sajad Fathi Hafshejani, Daya Gaur, Shahadat Hossain, Robert Benkoczi","doi":"10.1007/s11081-023-09836-6","DOIUrl":"https://doi.org/10.1007/s11081-023-09836-6","url":null,"abstract":"","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135967080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-23DOI: 10.1007/s11081-023-09838-4
M. Rosecký, J. Pluskal, R. Šomplák
Abstract Most of the supporting tools developed for logistic optimization and processing infrastructure planning are based on the network flow problem. The real-world application of these instruments can provide great insight and help to ensure long-term sustainability. The main limitation of these tools lies in great computing demand when there is the necessity of solving large-scale tasks in great detail. It means that the ability to find the optimal solution for real-world problems is limited. Thus, the detail of infrastructure is often reduced by data aggregation or heuristic approaches are used to find a suboptimal solution. This paper proposes a machine learning classification model to reduce the number of variables for an exact solution algorithm. First, the design of experiments is used to create a set of smaller problems that are possible to solve exactly. Artificial data are used at this stage, while domain knowledge is used to set appropriate distribution and parameters. Second, the classification model estimates the probability of the presence of each arc in the optimal solution. Features, which are related to costs and capacity, of each arc are utilized in the classification model. Models created on a subset of generated problems are then tested on the other problems. Finally, the proposed framework is applied to the waste management problem in the Czech Republic. The results of the verification show, that it is possible to remove 95% of arcs without impact on strategic decisions and without significant change of an objective function. The computing time of the reduced problem takes only 7% of the original task.
{"title":"Network flow problem heuristic reduction using machine learning","authors":"M. Rosecký, J. Pluskal, R. Šomplák","doi":"10.1007/s11081-023-09838-4","DOIUrl":"https://doi.org/10.1007/s11081-023-09838-4","url":null,"abstract":"Abstract Most of the supporting tools developed for logistic optimization and processing infrastructure planning are based on the network flow problem. The real-world application of these instruments can provide great insight and help to ensure long-term sustainability. The main limitation of these tools lies in great computing demand when there is the necessity of solving large-scale tasks in great detail. It means that the ability to find the optimal solution for real-world problems is limited. Thus, the detail of infrastructure is often reduced by data aggregation or heuristic approaches are used to find a suboptimal solution. This paper proposes a machine learning classification model to reduce the number of variables for an exact solution algorithm. First, the design of experiments is used to create a set of smaller problems that are possible to solve exactly. Artificial data are used at this stage, while domain knowledge is used to set appropriate distribution and parameters. Second, the classification model estimates the probability of the presence of each arc in the optimal solution. Features, which are related to costs and capacity, of each arc are utilized in the classification model. Models created on a subset of generated problems are then tested on the other problems. Finally, the proposed framework is applied to the waste management problem in the Czech Republic. The results of the verification show, that it is possible to remove 95% of arcs without impact on strategic decisions and without significant change of an objective function. The computing time of the reduced problem takes only 7% of the original task.","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135959419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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