Gotowe bibliografie tematyczne / Multi-objective fractional programming

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Części książek
  4. Streszczenia konferencji

Gotowa bibliografia na temat „Multi-objective fractional programming”

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

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Artykuły w czasopismach na temat "Multi-objective fractional programming"

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Sulaiman, Nejmaddin A., and Maher A. Nawkhass. "Using Standard Division to Solve Multi- Objective Quadratic Fractional Programming Problems." Journal of Zankoy Sulaimani - Part A 18, no. 3 (2016): 157–64. http://dx.doi.org/10.17656/jzs.10544.

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M., S. Osman, E. Emam O., and A. El Sayed M. "Multi-level Multi-objective Quadratic Fractional Programming Problem with Fuzzy Parameters: A FGP Approach." Asian Research Journal of Mathematics 5, no. 3 (2017): 1–19. https://doi.org/10.9734/ARJOM/2017/34864.

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The motivation behind this paper is to present multi-level multi-objective quadratic fractional programming (ML-MOQFP) problem with fuzzy parameters in the constraints. ML-MOQFP problem is an important class of non-linear fractional programming problem. These type of problems arise in many fields such as production planning, financial and corporative planning, health care and hospital planning. Firstly, the concept of the -cut and fuzzy partial order relation are applied to transform the set of fuzzy constraints into a common crisp set. Then, the quadratic fractional objective functions in eac
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Acharya, Srikumar, Berhanu Belay, and Rajashree Mishra. "MULTI-OBJECTIVE PROBABILISTIC FRACTIONAL PROGRAMMING PROBLEM INVOLVING TWO PARAMETERS CAUCHY DISTRIBUTION." Mathematical Modelling and Analysis 24, no. 3 (2019): 385–403. http://dx.doi.org/10.3846/mma.2019.024.

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The paper presents the solution methodology of a multi-objective probabilistic fractional programming problem, where the parameters of the right hand side constraints follow Cauchy distribution. The proposed mathematical model can not be solved directly. The solution procedure is completed in three steps. In first step, multi-objective probabilistic fractional programming problem is converted to deterministic multi-objective fractional mathematical programming problem. In the second step, it is converted to its equivalent multi-objective mathematical programming problem. Finally, ε -constraint
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Chakraborty, M., and Sandipan Gupta. "Fuzzy mathematical programming for multi objective linear fractional programming problem." Fuzzy Sets and Systems 125, no. 3 (2002): 335–42. http://dx.doi.org/10.1016/s0165-0114(01)00060-4.

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CHARLES, V., and D. DUTTA. "IDENTIFICATION OF REDUNDANT OBJECTIVE FUNCTIONS IN MULTI-OBJECTIVE STOCHASTIC FRACTIONAL PROGRAMMING PROBLEMS." Asia-Pacific Journal of Operational Research 23, no. 02 (2006): 155–70. http://dx.doi.org/10.1142/s0217595906000863.

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Redundancy in constraints and variables are usually studied in linear, integer and non-linear programming problems. However, main emphasis has so far been given only to linear programming problems. In this paper, an algorithm that identifies redundant objective functions in multi-objective stochastic fractional programming problems is provided. A solution procedure is also illustrated. This reduces the number of objective functions in cases where redundant objective functions exist.
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Youness, E. A., O. E. Emam, and M. S. Hafez. "Fuzzy Bi-Level Multi-Objective Fractional Integer Programming." Applied Mathematics & Information Sciences 8, no. 6 (2014): 2857–63. http://dx.doi.org/10.12785/amis/080622.

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Charles, V., and D. Dutta. "Extremization of multi-objective stochastic fractional programming problem." Annals of Operations Research 143, no. 1 (2006): 297–304. http://dx.doi.org/10.1007/s10479-006-7389-7.

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Baky, Ibrahim. "Fuzzy Goal Programming Procedures for Multi-Level Multi-Objective Linear Fractional Programming Problems." International Conference on Mathematics and Engineering Physics 6, no. 6 (2012): 1–20. http://dx.doi.org/10.21608/icmep.2012.29744.

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Ţigan, Ş., and I. M. Stancu-Minasian. "Efficiency and generalized concavity for multi-objective set-valued programming." Journal of Numerical Analysis and Approximation Theory 32, no. 2 (2003): 235–42. http://dx.doi.org/10.33993/jnaat322-752.

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The purpose of this paper is to give sufficient conditions of generalized concavity type for a local (weakly) efficient solution to be a global (weakly) efficient solution for a vector maximization set-valued programming problem. In the particular case of the vector maximization set-valued fractional programming problem, we derive some characterizations properties of efficient and properly efficient solutions based on a parametric procedure associated to the fractional problem.
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Nahar, Samsun, Md Asadujjaman, Khadiza Begum,   Mahede-Ul-Hassan, and Md Abdul Alim. "Characteristics of Multi-Objective Linear Programming Problem and Multi-Objective Linear Fractional Programming Problem Taking Maximum Value of Multi-Objective Functions." Applied Mathematics 15, no. 01 (2024): 22–32. http://dx.doi.org/10.4236/am.2024.151003.

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Rozprawy doktorskie na temat "Multi-objective fractional programming"

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Jitprapaikulsarn, Suradet. "An Optimization-Based Treatment Planner for Gamma Knife Radiosurgery." Case Western Reserve University School of Graduate Studies / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=case1109959500.

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Części książek na temat "Multi-objective fractional programming"

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Belay, Berhanu, and Srikumar Acharya. "Multi-choice Multi-objective Fractional Probabilistic Programming Problem." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5411-7_12.

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Belay, Berhanu, and Adane Abebaw. "Solving Multi-objective Chance Constraint Quadratic Fractional Programming Problem." In Springer Proceedings in Mathematics & Statistics. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-9307-7_36.

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Arbaiy, Nureize. "Weighted Linear Fractional Programming for Possibilistic Multi-objective Problem." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13153-5_9.

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Nayak, Suvasis, and Akshay Kumar Ojha. "Multi-objective Linear Fractional Programming Problem with Fuzzy Parameters." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_6.

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Suvasis Nayak and A. K. Ojha. "An Approach to Solve Multi-objective Linear Fractional Programming Problem." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0448-3_59.

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Veeramani, C., and M. Sumathi. "Solving Multi-Objective Linear Fractional Programming Problem - First Order Taylor's Series Approximation Approach." In Advances in Intelligent Systems and Computing. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1680-3_38.

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Maiti, Indrani, Tarni Mandal, and Surapati Pramanik. "FGP Approach Based on Stanojevic’s Normalization Technique for Multi-level Multi-objective Linear Fractional Programming Problem with Fuzzy Parameters." In Recent Advances in Intelligent Information Systems and Applied Mathematics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34152-7_30.

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Bajaj, Rakesh Kumar, Saurabh Srivastava, and Abhishek Guleria. "Solving Multi-objective Linear Fractional Programming Problem Utilizing ($$\alpha , \beta )$$-Cut in Triangular Intuitionistic Fuzzy Setup." In Real Life Applications of Multiple Criteria Decision Making Techniques in Fuzzy Domain. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4929-6_17.

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Dubey, Ramu, Teekam Singh, Vrince Vimal, and Bhaskar Nautiyal. "Multi-objective Symmetric Fractional Programming Problem and Duality Relations Under $$(C,G_{f},\alpha ,\rho ,d)$$-Invexity over Cone Constraints." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49345-5_26.

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"Development of Fuzzy Multi-Objective Stochastic Fractional Programming Models." In Multi-Objective Stochastic Programming in Fuzzy Environments. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8301-1.ch004.

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In this chapter, two methodologies for solving multi-objective linear fractional stochastic programming problems containing fuzzy numbers (FNs) and fuzzy random variables (FRVs) associated with the system constraints are developed. In the model formulation process, the fuzzy probabilistic constraints are converted into equivalent fuzzy constraints by applying chance constrained programming (CCP) technique in a fuzzily defined probabilistic decision-making situation. Then two techniques, -cut and defuzzification methods, are used to convert the model into the corresponding deterministic model.
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Streszczenia konferencji na temat "Multi-objective fractional programming"

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Santhamoorthy, Pooja Zen, and Selen Cremaschi. "Mathematical Optimization of Separator Network Design for Sand Management." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.154881.

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Sand produced along with well-production fluids accumulates in the surface facilities over time, taking valuable space, while the sand carried with the fluids damages downstream equipment. Thus, sand is separated from the fluid in the sand traps and separators and removed during periodic clean-ups. But at high sand productions, the probability of unscheduled facilities shutdowns increases. Such extreme production conditions can be handled by strategic planning and optimal design of the separator network to enable maximum sand separation at minimal equipment cost while ensuring the accumulation
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Roy, Debasish, and Rajib Dasgupta. "Multi objective fractional programming by genetic algorithm." In 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2016. http://dx.doi.org/10.1109/icrcicn.2016.7813644.

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Singh, Pitam. "Multi-objective linear fractional programming: A fuzzy efficient interactive goal programming method." In 2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, 2013. http://dx.doi.org/10.1109/ifuzzy.2013.6825446.

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De, P. K., and Moumita Deb. "Using goal programming approach to solve fuzzy multi-objective linear fractional programming problems." In 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2016. http://dx.doi.org/10.1109/iccic.2016.7919589.

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Rani, Rozy, Vandana Goyal, and Deepak Gupta. "M-TOPSIS approach for solving bilevel multi-objective quadratic fractional programming." In APPLIED DATA SCIENCE AND SMART SYSTEMS. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0177841.

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Veeramani, C., and S. Sharanya. "Analyzing the Performance Measures of Multi-Objective Water Cycle Algorithm for Multi-Objective Linear Fractional Programming Problem." In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. http://dx.doi.org/10.1109/iccons.2018.8662923.

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De, P. K., and Moumita Deb. "Solution of multi objective linear fractional programming problem by Taylor series approach." In 2015 International Conference on Man and Machine Interfacing (MAMI). IEEE, 2015. http://dx.doi.org/10.1109/mami.2015.7456582.

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Wu, Kuang-Yao. "Taylor Series Approach to Max-Ordering Solutions in Multi-Objective Linear Fractional Programming." In 2009 International Conference on Information Management, Innovation Management and Industrial Engineering. IEEE, 2009. http://dx.doi.org/10.1109/iciii.2009.485.

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Revathi, A. N., and S. Mohanaselvi. "A new solution approach to the uncertain multi-objective linear fractional programming problems." In 1ST INTERNATIONAL CONFERENCE ON MATHEMATICAL TECHNIQUES AND APPLICATIONS: ICMTA2020. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0025256.

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Samad, Abdus, Kwang-Yong Kim, Tushar Goel, Raphael T. Haftka, and Wei Shyy. "Shape Optimization of Turbomachinery Blade Using Multiple Surrogate Models." In ASME 2006 2nd Joint U.S.-European Fluids Engineering Summer Meeting Collocated With the 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/fedsm2006-98368.

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Performances of multiple surrogate models are evaluated in a turbomachinery blade shape optimization. The basic models, i.e., Response Surface Approximation, Kriging and Radial Basis Neural Network models as well as weighted average models are tested for shape optimization. Global data based errors for each surrogates are used to calculate the weights. These weights are multiplied with the respective surrogates to get the final weighted average models. Sequential Quadratic Programming is used to search the optimal point from these constructed surrogates. Use of multiple surrogates via weighted
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