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Journal articles on the topic 'Multi-level programming'

Author: Grafiati

Published: 29 September 2021

Last updated: 30 July 2025

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1

SAKAWA, Masatoshi, and Ichiro NISHIZAKI. "Interactive Fuzzy Programming for Multi - Level Programming Problems." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 21, no. 6 (2009): 1018–32. http://dx.doi.org/10.3156/jsoft.21.1018.

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2

Sinha, Surabhi. "Fuzzy programming approach to multi-level programming problems." Fuzzy Sets and Systems 136, no. 2 (2003): 189–202. http://dx.doi.org/10.1016/s0165-0114(02)00362-7.

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3

Liu, Qiu-mei, and Yan-mei Yang. "Interactive programming approach for solving multi-level multi-objective linear programming problem." Journal of Intelligent & Fuzzy Systems 35, no. 1 (2018): 55–61. http://dx.doi.org/10.3233/jifs-169566.

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4

Maiti, Indrani, Tarni Mandal, and Surapati Pramanik. "Neutrosophic goal programming strategy for multi-level multi-objective linear programming problem." Journal of Ambient Intelligence and Humanized Computing 11, no. 8 (2019): 3175–86. http://dx.doi.org/10.1007/s12652-019-01482-0.

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5

Sinha, Surabhi. "Fuzzy mathematical programming applied to multi-level programming problems." Computers & Operations Research 30, no. 9 (2003): 1259–68. http://dx.doi.org/10.1016/s0305-0548(02)00061-8.

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6

Anandalingam, G., and Victor Apprey. "Multi-level programming and conflict resolution." European Journal of Operational Research 51, no. 2 (1991): 233–47. http://dx.doi.org/10.1016/0377-2217(91)90253-r.

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7

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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8

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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9

Baky, Ibrahim A. "Solving multi-level multi-objective linear programming problems through fuzzy goal programming approach." Applied Mathematical Modelling 34, no. 9 (2010): 2377–87. http://dx.doi.org/10.1016/j.apm.2009.11.004.

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10

Sakawa, Masatoshi, and Ichiro Nishizaki. "Interactive fuzzy programming for multi-level programming problems: a review." International Journal of Multicriteria Decision Making 2, no. 3 (2012): 241. http://dx.doi.org/10.1504/ijmcdm.2012.047846.

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11

Sinha, S. B., and S. Sinha. "A linear programming approach for linear multi-level programming problems." Journal of the Operational Research Society 55, no. 3 (2004): 312–16. http://dx.doi.org/10.1057/palgrave.jors.2601701.

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12

Arora, S. R., and Ritu Gupta. "An algorithm for multi-level programming problem using goal programming." OPSEARCH 45, no. 1 (2008): 1–11. http://dx.doi.org/10.1007/bf03398801.

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13

Lachhwani, Kailash. "On solving multi-level multi objective linear programming problems through fuzzy goal programming approach." OPSEARCH 51, no. 4 (2013): 624–37. http://dx.doi.org/10.1007/s12597-013-0157-y.

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14

E., O., E. Fathy, and M. A. "Fully Fuzzy Multi-Level Linear Programming Problem." International Journal of Computer Applications 155, no. 7 (2016): 18–26. http://dx.doi.org/10.5120/ijca2016912287.

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15

Pramanik, Surapati, Durga Banerjee, and B. C. Giri. "Chance Constrained Multi-Level Linear Programming Problem." International Journal of Computer Applications 120, no. 18 (2015): 1–6. http://dx.doi.org/10.5120/21324-4275.

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16

Zhu, Xuan, Chunqing Wu, Yuhua Tang, Junjie Wu, and Xun Yi. "Multi-level programming of memristor in nanocrossbar." IEICE Electronics Express 10, no. 5 (2013): 20130013. http://dx.doi.org/10.1587/elex.10.20130013.

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17

Pradhan, Avik, and M. P. Biswal. "A bi-level multi-choice programming problem." International Journal of Mathematics in Operational Research 7, no. 1 (2015): 1. http://dx.doi.org/10.1504/ijmor.2015.065945.

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18

Ramadan, Misbah, Nicolás Wainstein, Ran Ginosar, and Shahar Kvatinsky. "Adaptive programming in multi-level cell ReRAM." Microelectronics Journal 90 (August 2019): 169–80. http://dx.doi.org/10.1016/j.mejo.2019.06.004.

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19

Shih, Hsu-Shih, Young-Jou Lai, and E. Stanley Lee. "Fuzzy approach for multi-level programming problems." Computers & Operations Research 23, no. 1 (1996): 73–91. http://dx.doi.org/10.1016/0305-0548(95)00007-9.

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20

Pradhan, Avik, and M. P. Biswal. "Multi-level linear programming problem involving some multi-choice parameters." International Journal of Mathematics in Operational Research 7, no. 3 (2015): 297. http://dx.doi.org/10.1504/ijmor.2015.069150.

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21

Surapati, Pramanik* Durga Banerjee B. C. Giri. "TOPSIS APPROACH TO CHANCE CONSTRAINED MULTI - OBJECTIVE MULTI- LEVEL QUADRATIC PROGRAMMING PROBLEM." Global Journal of Engineering Science and Research Management 3, no. 6 (2016): 19–36. https://doi.org/10.5281/zenodo.55308.

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This paper presents TOPSIS approach to solve chance constrained multi – objective multi – level quadratic programming problem. The proposed approach actually combines TOPSIS and fuzzy goal programming. In the TOPSIS approach, most appropriate alternative is to be finding out among all possible alternatives based on both the shortest distance from positive ideal solution (PIS) and furthest distance from the negative ideal solution (NIS). PIS and NIS for all objective functions of each level have been determined in the solution process. Distance functions which measure distances from

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22

Mandal, B. N., and C. Koukouvinos. "Optimal multi-level supersaturated designs through integer programming." Statistics & Probability Letters 84 (January 2014): 183–91. http://dx.doi.org/10.1016/j.spl.2013.10.007.

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23

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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24

Sinha, S. B., and Surabhi Sinha. "Linear multi-level programming problems with random variables." OPSEARCH 41, no. 1 (2004): 1–15. http://dx.doi.org/10.1007/bf03398829.

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25

Lei, Wang, and John L. Meek. "Multi-level substructuring and its implementation in programming." Advances in Engineering Software 16, no. 3 (1993): 195–202. http://dx.doi.org/10.1016/0965-9978(93)90017-n.

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26

Sakawa, Masatoshi, Ichiro Nishizaki, and Yoshio Uemura. "Interactive fuzzy programming for multi-level linear programming problems with fuzzy parameters." Fuzzy Sets and Systems 109, no. 1 (2000): 3–19. http://dx.doi.org/10.1016/s0165-0114(98)00130-4.

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27

Tiryaki, Fatma. "Interactive compensatory fuzzy programming for decentralized multi-level linear programming (DMLLP) problems." Fuzzy Sets and Systems 157, no. 23 (2006): 3072–90. http://dx.doi.org/10.1016/j.fss.2006.04.001.

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28

Nayak, Suvasis, Sujit Maharana, and RASHMI OTA. "Multi-level multi-objective linear fractional programming problem: A solution approach." International Journal of Mathematical Modelling and Numerical Optimisation 1, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijmmno.2023.10049623.

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29

Lachhwani, Kailash C. "On solving multi-objective linear bi-level multi-follower programming problem." International Journal of Operational Research 31, no. 4 (2018): 442. http://dx.doi.org/10.1504/ijor.2018.090426.

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30

Lachhwani, Kailash C. "On solving multi-objective linear bi-level multi-follower programming problem." International Journal of Operational Research 31, no. 4 (2018): 442. http://dx.doi.org/10.1504/ijor.2018.10011461.

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31

Sinha, Surabhi, and S. B. Sinha. "KKT transformation approach for multi-objective multi-level linear programming problems." European Journal of Operational Research 143, no. 1 (2002): 19–31. http://dx.doi.org/10.1016/s0377-2217(01)00323-x.

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32

Nayak, Suvasis, Sujit Maharana, and Rashmi Ranjan Ota. "Multi-level multi-objective linear fractional programming problem: a solution approach." International Journal of Mathematical Modelling and Numerical Optimisation 13, no. 1 (2023): 84. http://dx.doi.org/10.1504/ijmmno.2023.127840.

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33

Aydonat, Utku, and Tarek S. Abdelrahman. "Parallelization of multimedia applications on the multi-level computing architecture." Journal of Embedded Computing 4, no. 3-4 (2011): 87–106. https://doi.org/10.3233/jec-2012-0110.

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The Multi-Level Computing Architecture (MLCA) is a novel parallel System-on-a-Chip architecture targeted for multimedia applications. It features a top level controller that automatically extracts task level parallelism using techniques similar to how instruction level parallelism is extracted by superscalar processors. This allows the MLCA to support a simple programming model that is similar to sequential programming. In order to assist programmers to easily and efficiently port multimedia applications to the MLCA programming model, a compilation environment is designed. This compilation env

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34

SAKAWA, Masatoshi, Ichiro NISHIZAKI, Yoshio UEMURA, and Masatoshi HITAKA. "Interactive Fuzzy Programming for Multi-Level Nonconvex Nonlinear Programming Problems through Genetic Algorithms." Journal of Japan Society for Fuzzy Theory and Systems 12, no. 2 (2000): 304–12. http://dx.doi.org/10.3156/jfuzzy.12.2_96.

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35

Roy, Sankar Kumar, and Sumit Kumar Maiti. "Stochastic bi level programming with multi-choice for Stackelberg game via fuzzy programming." International Journal of Operational Research 29, no. 4 (2017): 508. http://dx.doi.org/10.1504/ijor.2017.085097.

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36

Maiti, Sumit Kumar, and Sankar Kumar Roy. "Stochastic bi level programming with multi-choice for Stackelberg game via fuzzy programming." International Journal of Operational Research 29, no. 4 (2017): 508. http://dx.doi.org/10.1504/ijor.2017.10005780.

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37

Lachhwani, Kailash. "On multi-level quadratic fractional programming problem with modified fuzzy goal programming approach." International Journal of Operational Research 37, no. 1 (2020): 135. http://dx.doi.org/10.1504/ijor.2020.10025873.

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38

Lachhwani, Kailash. "On multi-level quadratic fractional programming problem with modified fuzzy goal programming approach." International Journal of Operational Research 37, no. 1 (2020): 135. http://dx.doi.org/10.1504/ijor.2020.104227.

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39

Sakawa, Masatoshi, Ichiro Nishizaki, and Masatoshi Hitaka. "Interactive fuzzy programming for multi-level 0–1 programming problems through genetic algorithms." European Journal of Operational Research 114, no. 3 (1999): 580–88. http://dx.doi.org/10.1016/s0377-2217(98)00019-8.

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40

Baky, Ibrahim A. "Fuzzy goal programming algorithm for solving decentralized bi-level multi-objective programming problems." Fuzzy Sets and Systems 160, no. 18 (2009): 2701–13. http://dx.doi.org/10.1016/j.fss.2009.02.022.

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41

Nayak, Suvasis, and Akshay Ojha. "On multi-level multi-objective linear fractional programming problem with interval parameters." RAIRO - Operations Research 53, no. 5 (2019): 1601–16. http://dx.doi.org/10.1051/ro/2018063.

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This paper develops a method to solve multi-level multi-objective linear fractional programming problem (ML-MOLFPP) with interval parameters as the coefficients of decision variables and the constants involved in both the objectives and constraints. The objectives at each level are transformed into interval-valued fractional functions and approximated by intervals of linear functions using variable transformation and Taylor series expansion. Interval analysis and weighting sum method with analytic hierarchy process (AHP), are used to determine the non-dominated solutions at each level from whi

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42

Gupta, Srikant, Prasenjit Chatterjee, Morteza Yazdani, and Ernesto D. R. Santibanez Gonzalez. "A multi-level programming model for green supplier selection." Management Decision 59, no. 10 (2021): 2496–527. http://dx.doi.org/10.1108/md-04-2020-0472.

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PurposeIndustrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.Design/methodology/approachIn this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs

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43

Anandalingam, G. "A Mathematical Programming Model of Decentralized Multi-Level Systems." Journal of the Operational Research Society 39, no. 11 (1988): 1021. http://dx.doi.org/10.2307/2583201.

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44

Anandalingam, G. "A Mathematical Programming Model of Decentralized Multi-Level Systems." Journal of the Operational Research Society 39, no. 11 (1988): 1021–33. http://dx.doi.org/10.1057/jors.1988.172.

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45

Gómez-Limón, José A., Carlos Gutiérrez-Martín, and Laura Riesgo. "Modeling at farm level: Positive Multi-Attribute Utility Programming." Omega 65 (December 2016): 17–27. http://dx.doi.org/10.1016/j.omega.2015.12.004.

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46

Forouzanfar, Mehdi, A. Doustmohammadi, Samira Hasanzadeh, and H. Shakouri G. "Transport energy demand forecast using multi-level genetic programming." Applied Energy 91, no. 1 (2012): 496–503. http://dx.doi.org/10.1016/j.apenergy.2011.08.018.

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47

Elsisy, M. A., and M. A. El Sayed. "Fuzzy rough bi-level multi-objective nonlinear programming problems." Alexandria Engineering Journal 58, no. 4 (2019): 1471–82. http://dx.doi.org/10.1016/j.aej.2019.12.002.

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48

Fathy, E., E. Ammar, and M. A. Helmy. "Fully intuitionistic fuzzy multi-level linear fractional programming problem." Alexandria Engineering Journal 77 (August 2023): 684–94. http://dx.doi.org/10.1016/j.aej.2023.07.018.

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49

Goyal, Vandana, Namrata Rani, and Deepak Gupta. "Parametric approach to quadratically constrained multi-level multi-objective quadratic fractional programming." OPSEARCH 58, no. 3 (2021): 557–74. http://dx.doi.org/10.1007/s12597-020-00497-y.

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50

Stupar, Andrija, Timothy McRae, Nenad Vukadinovic, Aleksandar Prodic, and Josh A. Taylor. "Multi-Objective Optimization of Multi-Level DC–DC Converters Using Geometric Programming." IEEE Transactions on Power Electronics 34, no. 12 (2019): 11912–39. http://dx.doi.org/10.1109/tpel.2019.2908826.

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