Gotowe bibliografie tematyczne / Fuzzy Linear Programming Problem

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Fuzzy Linear Programming Problem”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Fuzzy Linear Programming Problem"

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ZANGIABADI, M., and H. R. MALEKI. "A METHOD FOR SOLVING LINEAR PROGRAMMING PROBLEMS WITH FUZZY PARAMETERS BASED ON MULTIOBJECTIVE LINEAR PROGRAMMING TECHNIQUE." Asia-Pacific Journal of Operational Research 24, no. 04 (2007): 557–73. http://dx.doi.org/10.1142/s0217595907001395.

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In the real-world optimization problems, coefficients of the objective function are not known precisely and can be interpreted as fuzzy numbers. In this paper we define the concepts of optimality for linear programming problems with fuzzy parameters based on those for multiobjective linear programming problems. Then by using the concept of comparison of fuzzy numbers, we transform a linear programming problem with fuzzy parameters to a multiobjective linear programming problem. To this end, we propose several theorems which are used to obtain optimal solutions of linear programming problems wi
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Jyoti, Arora, and Sharma Surbhi. "Solving Trapezoidal Fuzzy Linear Programming Problem using Modified Big-M." Journal of Applied Mathematics and Statistical Analysis 4, no. 3 (2023): 1–5. https://doi.org/10.5281/zenodo.10165868.

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<i>The fuzzy logic and fuzzy numbers have been applied in many areas of Mathematical Programming. Optimization under uncertainty is one of the most important problem in Mathematical Programming.&nbsp;This paper proposes a Modified Big M method to solve fully fuzzy Trapezoidal linear programming problem with fuzzy decision variables and fuzzy parameters.&nbsp;</i>
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Someshwar, Siddi* Dr. Y. Raghunatha Reddy. "SOLUTION OF INTEGER LINEAR PROGRAMMING PROBLEMS WITH TRIANGULAR FUZZY NUMBERS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 3 (2017): 325–28. https://doi.org/10.5281/zenodo.400958.

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Fuzzy Integer Linear Programming problem is an application of fuzzy set theory in linear decision problems and most of these problems are related to linear programming with fuzzy variables. In this paper, we proposed a method for Integer linear programming problems with fuzzy variables. Two numerical examples were illustrated with the help of the proposed method. This method is a simple tool for the best solution to a variety of Integer linear programming problems.
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Towfik, Zaki, and Sabiha Jawad. "Proposed Method for Optimizing Fuzzy linear programming Problems by using Two-Phase Technique." Iraqi Journal for Electrical and Electronic Engineering 6, no. 2 (2010): 89–96. http://dx.doi.org/10.37917/ijeee.6.2.2.

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Fuzzy linear programming (FLP ) is an application of fuzzy set theory in linear decision making problems and most of these problems are related to linear programming contains fuzzy constrains or crisp objectives function or contains crisp constrains with fuzzy objectives function, which called fuzzy linear programming (FLP) with triplet fuzzy numbers consist a hybrid fuzzy. The crisp constrains used in the problems of types (= or ≥) with a proposed optimization fuzzy objectives and fuzzy constrains. In this paper proposed method for solving fuzzy linear programming problem by using Two-phase t
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SN, Mohamed Assarudeen, and Devi M. "A Study on A Solution Approach to Fuzzy Linear Frctional Programming Problems." Journal of Computational Mathematica 7, no. 1 (2023): 110–19. http://dx.doi.org/10.26524/cm166.

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In this paper, we propose a method of solving the fully fuzzy linear fractional programming problems, Express all the parameters and variables are triangular fuzzy numbers. Convert all the triangular fuzzy numbers in their parametric form, we convert the fractional programming problem in to a single objective linear programming problem in parametric form. We put new fuzzy arithmetic and fuzzy ranking, we obtain the optimal solution the given fully fuzzy linear fractional programming problem without converting to its equivalent crisp linear programming problem. A numerical example is provided t
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Cheng, Haifang, Weilai Huang, and Jianhu Cai. "Solving a Fully Fuzzy Linear Programming Problem through Compromise Programming." Journal of Applied Mathematics 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/726296.

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In the current literatures, there are several models of fully fuzzy linear programming (FFLP) problems where all the parameters and variables were fuzzy numbers but the constraints were crisp equality or inequality. In this paper, an FFLP problem with fuzzy equality constraints is discussed, and a method for solving this FFLP problem is also proposed. We first transform the fuzzy equality constraints into the crisp inequality ones using the measure of the similarity, which is interpreted as the feasibility degree of constrains, and then transform the fuzzy objective into two crisp objectives b
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Das, Krishnapada. "On Fuzzy Linear Programming Problems." International Journal of Science and Social Science Research 2, no. 3 (2024): 27–31. https://doi.org/10.5281/zenodo.13917823.

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In this paper, we concentrate on solving Fuzzy Linear Programming Problems (FLPP) in which the costcoefficients, the right-hand side vector and the technological coefficients are fuzzy numbers by defining a kind offuzzy inequality between two fuzzy numbers and then compare the results obtained by solving fuzzy linearprogramming problems (FLPP) with the ranking function method of solving FLPP. In this paper we discuss thecase of triangular fuzzy numbers.&nbsp;
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Archana, D. "Fuzzy Linear Fractional Programming Problem Using Trapezoidal Fuzzy Numbers." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 3483–88. https://doi.org/10.22214/ijraset.2025.67956.

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Abstract: In order to solve Fully Fuzzy Linear Fractional Programming Problems (FFLFPP) with trapezoidal fuzzy integers as the objective function and constraints, this study aims to develop a novel trapezoidal fuzzy number ranking function. The simplex method and crisp linear fractional programming serve as the foundation for the suggested approach. With the aid of a recently proposed ranking function, we first converted FFLFPP into a Crisp Linear Programming Problem. The resulting problem was then transformed into LPP. The suggested method is demonstrated using a numerical example.
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Princy Flora, M. "Solving Fuzzy Linear Programming as Multi Objective Linear Programming Problem." Asian Journal of Science and Applied Technology 5, no. 1 (2016): 28–32. http://dx.doi.org/10.51983/ajsat-2016.5.1.2545.

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The constraints and the objective function of the fuzzy linear programming problem are converted into the multi-objective optimization problem (i.e.,) into an equivalent crisp linear problem.Finally, the multi-objective linear programming problem is converted into the weighted linear programming problem to show that they are independent of weights and obtained the complete optimal solution.
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Das, Krishnapada. "On Fully Fuzzy Linear Programming Problems." International Journal of Science and Social Science Research 2, no. 3 (2024): 72–76. https://doi.org/10.5281/zenodo.14010593.

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The fuzzy linear programming problem has been used as an important tool in different disciplines such as&nbsp;engineering, business, economics, commerce, defence etc.&nbsp;Fully Fuzzy Linear Programming Problems (FFLP) are those in which all the parameters that is the cost coefficients, the technological coefficients, the right-hand side of the constraints, and the decision variable are fuzzy numbers. Ezzati et al. [1] suggested an algorithm to solve fully fuzzy linear programming problems. In this paper, we propose two methods to find the optimal solution of fully fuzzy linear programming pro
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Rozprawy doktorskie na temat "Fuzzy Linear Programming Problem"

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GANDOLPHO, ANDRE ALVES. "METHODOLOGY FOR SOLVING FUZZY LINEAR PROGRAMMING PROBLEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8070@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Esta tese propõe uma metodologia para obter uma solução para problemas de programação linear fuzzy. A metodologia aqui descrita apresenta um conjunto de soluções em que tanto os valores das variáveis quanto o valor ótimo para a função de custo, ou função objetivo, possuem uma faixa de valores possíveis. Assim, é possível fornecer um conjunto de soluções factíveis que atendam a diferentes cenários, além de fornecer ao tomador de decisões uma ferramenta de análise mais útil, permitindo que sejam analisadas outras soluções p
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Mehmood, Rashid. "Fuzzy linear programming problems solved with Fuzzy decisive set method." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1201.

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In the thesis, there are two kinds of fuzzy linear programming problems, one of them is a linear programming problem with fuzzy technological coefficients and the second is linear programming problem in which both the right-hand side and the technological coefficients are fuzzy numbers. I solve the fuzzy linear programming problems with fuzzy decisive set method.
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Remias, Michael George. "Computational studies of some fuzzy mathematical problems." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1147.

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In modelling and optimizing real world systems and processes, one usually ends up with a linear or nonlinear programming problem, namely maximizing one or more objective functions subject to a set of constraint equations or inequalities. For many cases, the constraints do not need to be satisfied exactly, and the coefficients involved in the model are imprecise in nature and have to be described by fuzzy numbers to reflect the real world nature. The resulting mathematical programming problem is referred to as a fuzzy mathematical programming problem.Over the past decades, a great deal of work
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IUNG, ANDERSON MITTERHOFER. "APPLICATION OF FUZZY LINEAR PROGRAMMING TO THE PROBLEM OF PLANNING UNDER UNCERTAINTY THE EXPANSIUM OF THE TRANSMITION SYSTEM." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2000. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7459@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CENTRO DE PESQUISA DE ENERGIA ELÉTRICA<br>Esta dissertação apresenta um aplicação de programação linear fuzzy para o problema de planejamento da expansão de redes de transmissão de potência sob considerações de incertezas. A partir da modelagem dos conceitos vagos e imprecisos, inerentes ao problema de planejamento, com a utilização da teoria da lógica fuzzy é possível incorporar as incertezas dentro do modelo do problema. Os conceitos de programação linear fuzzy são utilizados para transformar tanto a função objetivo como
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Amaral, Wanessa Machado do. "Teoria de jogos nebulosos na resolução de problemas de decisão e conflito de interesses." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259057.

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Orientador: Fernando Antonio Campos Gomide<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-08T23:48:55Z (GMT). No. of bitstreams: 1 Amaral_WanessaMachadodo_M.pdf: 2424347 bytes, checksum: 390879b70ad2ca4dc593f415471fea5c (MD5) Previous issue date: 2007<br>Resumo: A teoria de jogos é um ramo da teoria da decisão que modela e trata matematicamente situações de conflito de interesses entre entidades, onde o objetivo principal é escolher a melhor estratégia para cada uma delas, ou seja, aquela
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Silva, Neto Gustavo Cunha da. "Um método para solução de problemas de otimização multiobjetivo em ambiente fuzzy." Universidade Federal do Amazonas, 2011. http://tede.ufam.edu.br/handle/tede/3689.

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Made available in DSpace on 2015-04-22T22:16:14Z (GMT). No. of bitstreams: 1 Gustavo Cunha.pdf: 1000425 bytes, checksum: fc84dd761ea9f58fc46869f1324d8e1d (MD5) Previous issue date: 2011-09-02<br>CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>This paper presents a model for solving multiobjective linear programming problems based on the calculation of the minimum fuzzy distance. The theoretical principles outline some elements of the fuzzy logic as well as crisp multiobjective optimization elements needed for the formulation of the model. Moreover, some classical mu
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Ciappina, Jussara Rodrigues. "Decomposição de Dantzig-Wolfe e heurística aplicados a problemas de fluxo multiproduto Fuzzy." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260597.

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Orientadores: Akebo Yamakami, Ricardo Coelho Silva<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-19T06:43:21Z (GMT). No. of bitstreams: 1 Ciappina_JussaraRodrigues_D.pdf: 6752680 bytes, checksum: 3dcb7910cc5098e44936e76045792069 (MD5) Previous issue date: 2011<br>Resumo: Este trabalho apresenta dois métodos baseados no método clássico de decomposição de Dantzig-Wolfe e um método heurístico, os quais resolvem problemas com incertezas nos parâmetros utilizando a teoria dos conjuntos fuzzy. O prim
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Kaseke, Evans. "Fuzzy linear programming and reservoir management." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26708.

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The presence of imprecision in parameter specification of water resources management problems leads to the formulation of fuzzy programming models. This thesis presents the formulation of a two-reservoir system problem as a fuzzy L.P. model. The aim is to determine if larger monetary benefits, over and above the usual benefits, can be obtained from the system. The other aim is to determine if desired industrial and domestic water allocations, as well as outflows for selected periods can be achieved. The problem is formulated as a conventional L.P. model. Then selected water allocations and
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Sharma, Vivek Narain. "Linear programming and quadratic programming approach for graduation in fuzzy environment." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ57577.pdf.

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Espinoza, Daniel G. "On Linear Programming, Integer Programming and Cutting Planes." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10482.

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In this thesis we address three related topic in the field of Operations Research. Firstly we discuss the problems and limitation of most common solvers for linear programming, precision. We then present a solver that generate rational optimal solutions to linear programming problems by solving a succession of (increasingly more precise) floating point approximations of the original rational problem until the rational optimality conditions are achieved. This method is shown to be (on average) only 20% slower than the common pure floating point approach, while returning true optimal solutions
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Książki na temat "Fuzzy Linear Programming Problem"

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Kaur, Jagdeep, and Amit Kumar. An Introduction to Fuzzy Linear Programming Problems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31274-3.

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Nasseri, Seyed Hadi, Ali Ebrahimnejad, and Bing-Yuan Cao. Fuzzy Linear Programming: Solution Techniques and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17421-7.

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Sakawa, Masatoshi, Hitoshi Yano, and Ichiro Nishizaki. Linear and Multiobjective Programming with Fuzzy Stochastic Extensions. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-9399-0.

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1929-, Hwang C. L., ed. Fuzzy mathematical programming: Methods and applications. Springer-Verlag, 1992.

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service), SpringerLink (Online, ed. Linear Programming and Generalizations: A Problem-based Introduction with Spreadsheets. Springer Science+Business Media, LLC, 2011.

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Rommelfanger, Heinrich. PC software FULPAL 2.0: An interactive algorithm for solving multicriteria fuzzy linear programs controlled by aspiration levels. Johann Wolfgang Goethe-Universität Frankfurt, Fachbereich Wirtschaftswissenschaften, 1995.

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Kozlov, M. V. Primenenie t︠s︡elochislennogo lineĭnogo programmirovanii︠a︡ s posledovatelʹnym iskli︠u︡cheniem t︠s︡iklov dli︠a︡ reshenii︠a︡ zadachi kommivoi︠a︡zhera. Vychislitelʹnyĭ T︠S︡entr im. A.A. Dorodnit︠s︡yna Rossiĭskoĭ akademii nauk, 2012.

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Li, Deng-Feng. Linear Programming Models and Methods of Matrix Games with Payoffs of Triangular Fuzzy Numbers. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48476-0.

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Wolf, Jochen. Lineare Fuzzy-Modelle zur Unterstützung der Investitionsentscheidung: Modellierung und Lösung von Investitionsproblemen mittels der Theorie unscharfer Mengen. P. Lang, 1988.

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Ben-Ayed, Omar. Solving a real world highway network design problem using bilevel linear programming. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1988.

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Części książek na temat "Fuzzy Linear Programming Problem"

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Ahmad, Firoz, and Ahmad Yusuf Adhami. "Spherical Fuzzy Linear Programming Problem." In Decision Making with Spherical Fuzzy Sets. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45461-6_19.

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Ahmad, Firoz, and Ahmad Yusuf Adhami. "Spherical Fuzzy Multiobjective Linear Programming Problem." In Decision Making with Spherical Fuzzy Sets. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45461-6_20.

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Farnam, Madineh, and Majid Darehmiraki. "Hesitant Fuzzy Linear Fractional Programming Problem." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66501-2_70.

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Parra, Arenas, M. Mar, Rodriguez Uria, and M. Victoria. "A Formulation of a Fuzzy Linear Goal Programming Problem with Fuzzy Constraints and Fuzzy Target Values." In Multi-Objective Programming and Goal Programming. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-87561-8_18.

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Das, Sapan Kumar, and Seyyed Ahmad Edalatpanah. "Application of Linear Programming in Diet Problem Under Pythagorean Fuzzy Environment." In Pythagorean Fuzzy Sets. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1989-2_13.

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Tadesse, Admasu, M. M. Acharya, Manoranjan Sahoo, and Srikumar Acharya. "Multi-objective fuzzy linear programming problem with fuzzy decision variables." In Modeling and Applications in Operations Research. CRC Press, 2023. http://dx.doi.org/10.1201/9781003462422-4.

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Arenas, M., A. Bilbao, M. V. Rodríguez, and M. Jiménez. "Fuzzy Parameters Multiobjective Linear Programming Problem: Solution Analysis." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-45772-2_11.

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Inuiguchi, Masahiro. "Approaches to Linear Programming Problems with Interactive Fuzzy Numbers." In Fuzzy Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_7.

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Tripathi, Shubham Kumar, and Ranjan Kumar. "A short literature on linear programming problem under uncertainty." In Data-Driven Modelling with Fuzzy Sets. CRC Press, 2024. http://dx.doi.org/10.1201/9781003487029-8.

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Herrera, F., and J. L. Verdegay. "Approaching Fuzzy Integer Linear Programming Problems." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-45700-5_9.

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Streszczenia konferencji na temat "Fuzzy Linear Programming Problem"

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Kasperski, Adam, and Paweł Zieliński. "Recoverable Possibilistic Linear Programming." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10612017.

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Veeramani, C., and M. Sumathi. "Fuzzy Mathematical programming approach for solving Fuzzy Linear Fractional Programming problem." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622568.

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Davoudi, Niloofar, Farhad Hamidi, and Hassan Mishmast Nehi. "Triangular fuzzy bilevel linear programming problem." In 2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS). IEEE, 2020. http://dx.doi.org/10.1109/cfis49607.2020.9238756.

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Arbaiy, Nureize Binti, and Junzo Watada. "Linear Fractional Programming for Fuzzy Random Based Possibilistic Programming Problem." In 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE, 2012. http://dx.doi.org/10.1109/cimsim.2012.42.

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Nucci, F., C. Cavallo, and A. Grieco. "A fuzzy linear programming approach to mix product selection problem." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295606.

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Masoud, Mahmoud, H. A. Khalifa, Shi Qiang Liu, Mohammed Elhenawy, and Peng Wu. "A fuzzy goal programming approach for solving fuzzy multi-objective stochastic linear programming problem." In 2019 International Conference on Industrial Engineering and Systems Management (IESM). IEEE, 2019. http://dx.doi.org/10.1109/iesm45758.2019.8948204.

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Yuping Lan, Xuanli Lv, and Weiguo Zhang. "A Linear Programming Model of Fuzzy Portfolio Selection Problem." In 2007 IEEE International Conference on Control and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icca.2007.4376935.

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Dewanto, Tedi, Karyati, Emut, and Agus Maman Abadi. "Solving the generalized trapezoidal fuzzy number linear programming problem." In PROCEEDINGS OF THE 4TH INTERNATIONAL SEMINAR ON INNOVATION IN MATHEMATICS AND MATHEMATICS EDUCATION (ISIMMED) 2020: Rethinking the role of statistics, mathematics and mathematics education in society 5.0: Theory, research, and practice. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0111269.

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Assarudeen, S. N. Mohamed, and S. Abdul Hakkeem. "Solving fuzzy fractional linear programming problem using iteration method." In INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES AND APPLICATIONS (ICSTA 2022). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0163201.

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Duraisamy, P., and M. Kaliyappan. "Solving fuzzy linear programming problem, fuzzy transportation and fuzzy assignment problem using MATLAB: A new approach." In 3RD INTERNATIONAL CONFERENCE ON MATHEMATICAL TECHNIQUES AND APPLICATIONS (e-ICMTA-2022). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0164530.

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Raporty organizacyjne na temat "Fuzzy Linear Programming Problem"

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Osipov, Gennadij Sergeevich. The simplest method of solving a fuzzy linear problem EXCHANGE. КультИнформПресс, 2017. http://dx.doi.org/10.18411/spbcsa-2017-4.

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Khan, Mahreen. Lessons from Adaptive Programming. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.142.

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Streszczenie:
The aim of adaptive programming (AP) is to produce adaptive, flexible, iterative, responsive, problem-driven, politically smart, locally led programmes which are effective and efficient and meet donor requirements for accountability. This is a rapid desk review of recent literature on AP including academic and grey sources. Section 2 covers the main challenges and barriers to successful implementation of AP. Key success factors are covered in Section 3. Selecting the appropriate monitoring and evaluation tools such as outcome harvesting or adapted versions of Value for Money to assist in measu
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Liebchen, Christian. An Integer Programming Model to Assign Train Drivers to Good Positions in Basic Turni. Technische Hochschule Wildau, 2024. http://dx.doi.org/10.15771/1935.

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We are reporting on a project on duty rostering of a train operating company in Germany. There, in the past, rostering took place on a purely individual basis. As the goal of the project, the train drivers should work according to a set of 20 essentially fixed basic turni. These plans took into effect from January 2024 on. In this extended abstract, we are focusing on a specific task that had to be resolved within the transition process. In particular, the fixed turni are intended to repeat periodically, most of them after 17 weeks. In particular, for that one turnus is equipped with train dri
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Ratmanski, Kiril, and Sergey Vecherin. Resilience in distributed sensor networks. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45680.

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With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we present a new mathematical framework for efficient and resilient sensor network installation. Specifically, in this work we formulate and solve a sensor selection and placement problem when network resilience is also a factor in the optimization problem. Our approach is based on the binary linear programming problem. The generic formulation is probabilistic and applicable to any sensor
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