3 research outputs found
Π Π΅Π΄ΡΠΊΡΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π½ΡΡ Π·Π°Π΄Π°Ρ ΠΈ Π³ΠΈΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΠΈΡ ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π² ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ
ΠΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠ°. ΠΡΠΊΡΠ»ΡΠΊΠΈ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ Π·Π°Π²ΠΆΠ΄ΠΈ Π·Π°ΡΡΠΏΠ°Ρ Π±Π°Π³Π°ΡΠΎ ΠΏΡΠ΄Ρ
ΠΎΠ΄ΡΠ² ΠΉ Π΅Π²ΡΠΈΡΡΠΈΠΊ, Π° ΡΠ°ΠΊΠΎΠΆ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠ½Ρ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ° Ρ Ρ
ΡΠ΄ ΡΠ°ΡΡ ΠΌΠΎΠΆΡΡΡ ΠΏΠΎΡΠΎΠ΄ΠΆΡΠ²Π°ΡΠΈ ΡΡΠ»Ρ ΠΏΠΎΡΠ»ΡΠ΄ΠΎΠ²Π½ΠΎΡΡΡ Π·Π°Π΄Π°Ρ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ, ΡΠΎ ΡΠΎΠ·Π³Π»ΡΠ΄Π°ΡΡΡΡΡ Π·Π°Π΄Π°ΡΠ° Π²ΡΠ°Ρ
ΡΠ²Π°Π½Π½Ρ ΠΌΠ½ΠΎΠΆΠΈΠ½Π½ΠΈΡ
ΡΡΠ°Π½ΡΠ² Ρ ΠΊΡΠΈΡΠ΅ΡΡΡΠ². ΠΠ΅ΡΠ° Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ. Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΠΌΠ΅ΡΠΎΠ΄Ρ ΡΠ΅Π΄ΡΠΊΡΡΡ Π·Π°Π³Π°Π»ΡΠ½ΠΎΡ Π·Π°Π΄Π°ΡΡ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ Π· ΠΌΠ½ΠΎΠΆΠΈΠ½Π½ΠΈΠΌΠΈ ΡΡΠ°Π½Π°ΠΌΠΈ ΠΏΠΎΡΡΠ΄ Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½ΡΠΌ ΠΌΠ½ΠΎΠΆΠΈΠ½Π½ΠΈΡ
ΠΊΡΠΈΡΠ΅ΡΡΡΠ² ΡΠ΅ΡΠ΅Π· ΡΡ
Π³ΡΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΡΡ Π΄Π»Ρ ΠΎΠ΄Π½ΠΎΠ·Π½Π°ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠ·Π²βΡΠ·Π°Π½Π½Ρ ΡΠ΄ΠΈΠ½ΠΎΡ Π·Π°Π΄Π°ΡΡ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ. ΠΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ. ΠΡΠΎΠΏΠΎΠ½ΡΡΡΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π·Π²Π΅Π΄Π΅Π½Π½Ρ ΡΠΊΡΠ½ΡΠ΅Π½Π½ΠΎΡ ΠΌΠ½ΠΎΠΆΠΈΠ½ΠΈ Π·Π°Π΄Π°Ρ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ Π΄ΠΎ ΡΠ΄ΠΈΠ½ΠΎΡ Π·Π°Π΄Π°ΡΡ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ. Π’Π°ΠΊΠΎΠΆ ΡΠΎΡΠΌΠ°Π»ΡΠ·ΡΡΡΡΡΡ Π³ΡΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΡΡ ΠΊΡΠΈΡΠ΅ΡΡΡΠ² ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ, ΡΠΊΠ° Π΄Π°Ρ Π·ΠΌΠΎΠ³Ρ ΠΎΡΡΠΈΠΌΠ°ΡΠΈ ΡΠ΄ΠΈΠ½Ρ ΠΌΠ½ΠΎΠΆΠΈΠ½Ρ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΈΡ
Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ². Π Π΅Π·ΡΠ»ΡΡΠ°ΡΠΈ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ. ΠΠ° ΠΏΡΠ°ΠΊΡΠΈΡΡ ΡΡ ΠΌΠ½ΠΎΠΆΠΈΠ½Π° ΠΌΡΡΡΠΈΡΡ Π»ΠΈΡΠ΅ ΠΎΠ΄Π½Ρ Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Ρ. Π’ΡΡ, Π·Π°Π²Π΄ΡΠΊΠΈ Π΄ΡΡ Π·Π°ΠΊΠΎΠ½Ρ Π²Π΅Π»ΠΈΠΊΠΈΡ
ΡΠΈΡΠ΅Π» (ΠΌΠ½ΠΎΠΆΠΈΠ½Π½ΠΈΡ
ΠΊΡΠΈΡΠ΅ΡΡΡΠ²), ΡΠΈΠΌ Π±ΡΠ»ΡΡΠ΅ ΡΠΈΡΠ»ΠΎ ΠΊΡΠΈΡΠ΅ΡΡΡΠ², ΡΠΎ Π·Π°Π»ΡΡΠ°ΡΡΡΡΡ Π΄ΠΎ Π³ΡΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΡΡ, ΡΠΈΠΌ Π±ΡΠ»ΡΡ Π½Π°Π΄ΡΠΉΠ½ΠΈΠΌ, Π·Π³ΡΠ΄Π½ΠΎ Π·Ρ ΡΡΠΎΡΠΌΡΠ»ΡΠΎΠ²Π°Π½ΠΈΠΌ Π²ΠΈΡΠ°Π·ΠΎΠΌ, Π²ΠΈΡ
ΠΎΠ΄ΠΈΡΡ ΡΡΡΠ΅Π½Π½Ρ. ΠΠΈΡΠ½ΠΎΠ²ΠΊΠΈ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π΄ΡΠΊΡΡΡ Π±Π°Π³Π°ΡΠΎΠΏΠΎΠ·ΠΈΡΡΠΉΠ½ΠΈΡ
Π·Π°Π΄Π°Ρ Ρ Π³ΡΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΡΡ ΠΊΡΠΈΡΠ΅ΡΡΡΠ² ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΡΡΡΡ Π΄Π»Ρ Π΄ΠΎΡΠ»ΡΠ΄Π½ΠΈΠΊΠ° ΠΎΠ΄Π½Ρ Π·Π°Π΄Π°ΡΡ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ, ΡΠΈΡΠ»ΠΎ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΈΡ
ΡΠΎΠ·Π²βΡΠ·ΠΊΡΠ² ΡΠΊΠΎΡ ΠΌΠ°Ρ Π±ΡΡΠΈ ΠΌΠ΅Π½ΡΠΈΠΌ, Π½ΡΠΆ Π·Π° Π±ΡΠ΄Ρ-ΡΠΊΠΈΠΌΠΈ ΡΠ½ΡΠΈΠΌΠΈ ΠΏΡΠ΄Ρ
ΠΎΠ΄Π°ΠΌΠΈ. Π’Π°ΠΊΠΎΠΆ ΡΠ΅ Π΄Π°Ρ Π·ΠΌΠΎΠ³Ρ ΡΠ°Π½ΠΆΡΠ²Π°ΡΠΈ Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²ΠΈ Π· Π±ΡΠ»ΡΡΠΈΠΌΠΈ Π½Π°Π΄ΡΠΉΠ½ΡΡΡΡ ΡΠ° Π΄ΠΎΡΡΠΎΠ²ΡΡΠ½ΡΡΡΡ. ΠΡΡΠΌ ΡΠΎΠ³ΠΎ, ΡΡΠ²ΠΎΡΡΡΡΡΡΡ Π½Π°Π΄ΡΠΉΠ½Ρ Π²Π°Π³ΠΈ (ΠΏΡΡΠΎΡΠΈΡΠ΅ΡΠΈ) Π΄Π»Ρ ΡΠΊΠ°Π»ΡΡΠΈΠ·Π°ΡΡΡ Π±Π°Π³Π°ΡΠΎΠΊΡΠΈΡΠ΅ΡΡΠ°Π»ΡΠ½ΠΈΡ
Π·Π°Π΄Π°Ρ.Background. Due to that decision making is always involving a great deal of approaches and heuristics, and poor statistics and time course can generate series of decision making problems, the problem of regarding multiple states and criteria is considered. Objective. The goal is to develop an approach for reducing the multiple state decision making problem along with regarding multiple criteria by their hybridization to solve disambiguously a single decision making problem. Methods. An algorithm of reducing a finite series of decision making problems to a single problem is suggested. Also a statement is formulated to hybridize decision making criteria allowing to get a single optimal alternativesβ set. Results. Practically, this set contains just a single alternative. And, owing to the law of large numbers (of multiple criteria), the greater number of criteria is involved into the hybridization, the more reliable decision by the formulated statement is. Conclusions. The represented multiple state problem reduction and decision making criteria hybridization both provide a researcher with the one decision making problem whose number of optimal solutions must be less than that by any other approaches. Besides, it allows to rank alternatives at higher reliability and validity. Furthermore, reliable weights (priorities) for scalarizing multicriteria problems are produced.ΠΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠ°. ΠΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΠΏΡΠΈΠ½ΡΡΠΈΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π²ΡΠ΅Π³Π΄Π° Π·Π°ΡΡΠ°Π³ΠΈΠ²Π°Π΅Ρ ΠΌΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΈ ΡΠ²ΡΠΈΡΡΠΈΠΊ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½Π°Ρ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ° ΠΈ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΌΠΎΠ³ΡΡ ΠΏΠΎΡΠΎΠΆΠ΄Π°ΡΡ ΡΠ΅Π»ΡΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π·Π°Π΄Π°Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ, ΡΠΎ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° ΡΡΠ΅ΡΠ° ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΠΈ ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π². Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΌΠ΅ΡΠΎΠ΄Π° ΡΠ΅Π΄ΡΠΊΡΠΈΠΈ ΠΎΠ±ΡΠ΅ΠΉ Π·Π°Π΄Π°ΡΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Ρ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΌΠΈ ΡΠΎΡΡΠΎΡΠ½ΠΈΡΠΌΠΈ Π½Π°ΡΡΠ΄Ρ Ρ ΡΡΠ΅ΡΠΎΠΌ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π² ΠΏΡΡΠ΅ΠΌ ΠΈΡ
Π³ΠΈΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΠΈΠΈ Π΄Π»Ρ ΠΎΠ΄Π½ΠΎΠ·Π½Π°ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π΅Π΄ΠΈΠ½ΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ. ΠΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΊΠΎΠ½Π΅ΡΠ½ΠΎΠ³ΠΎ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° Π·Π°Π΄Π°Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΊ Π΅Π΄ΠΈΠ½ΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ. Π’Π°ΠΊΠΆΠ΅ ΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΡΠ΅ΡΡΡ Π³ΠΈΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΠΈΡ ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π² ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ°Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡ Π΅Π΄ΠΈΠ½ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²ΠΎ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ
Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ². Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. ΠΠ° ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ ΡΡΠΎ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²ΠΎ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ Π²ΡΠ΅Π³ΠΎ Π»ΠΈΡΡ Π΅Π΄ΠΈΠ½ΡΡΠ²Π΅Π½Π½ΡΡ Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Ρ. ΠΠ΄Π΅ΡΡ, Π±Π»Π°Π³ΠΎΠ΄Π°ΡΡ Π΄Π΅ΠΉΡΡΠ²ΠΈΡ Π·Π°ΠΊΠΎΠ½Π° Π±ΠΎΠ»ΡΡΠΈΡ
ΡΠΈΡΠ΅Π» (ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π²), ΡΠ΅ΠΌ Π±ΠΎΠ»ΡΡΠ΅ ΡΠΈΡΠ»ΠΎ ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π², Π²ΠΎΠ²Π»Π΅ΠΊΠ°Π΅ΠΌΡΡ
Π² Π³ΠΈΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΠΈΡ, ΡΠ΅ΠΌ Π±ΠΎΠ»Π΅Π΅ Π½Π°Π΄Π΅ΠΆΠ½ΡΠΌ, ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΌΡ Π²ΡΡΠ°ΠΆΠ΅Π½ΠΈΡ, Π²ΡΡ
ΠΎΠ΄ΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅. ΠΡΠ²ΠΎΠ΄Ρ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΠ΅ ΡΠ΅Π΄ΡΠΊΡΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π½ΡΡ
Π·Π°Π΄Π°Ρ ΠΈ Π³ΠΈΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΠΈΡ ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π² ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡ Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΎΠ΄Π½Ρ Π·Π°Π΄Π°ΡΡ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ, ΡΠΈΡΠ»ΠΎ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΊΠΎΡΠΎΡΠΎΠΉ Π΄ΠΎΠ»ΠΆΠ½ΠΎ Π±ΡΡΡ ΠΌΠ΅Π½ΡΡΠ΅, ΡΠ΅ΠΌ ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ Π»ΡΠ±ΡΠΌ Π΄ΡΡΠ³ΠΈΠΌ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°ΠΌ. Π’Π°ΠΊΠΆΠ΅ ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠ°Π½ΠΆΠΈΡΠΎΠ²Π°ΡΡ Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Ρ Ρ Π±ΠΎΠ»ΡΡΠΈΠΌΠΈ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΡΡ ΠΈ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΡΡΡΡ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΡΠΎΠ·Π΄Π°ΡΡΡΡ Π½Π°Π΄Π΅ΠΆΠ½ΡΠ΅ Π²Π΅ΡΠ° (ΠΏΡΠΈΠΎΡΠΈΡΠ΅ΡΡ) Π΄Π»Ρ ΡΠΊΠ°Π»ΡΡΠΈΠ·Π°ΡΠΈΠΈ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡΠΈΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ
Π·Π°Π΄Π°Ρ
Fuzzy multilevel programming with a hybrid intelligent algorithm
AbstractIn order to model fuzzy decentralized decision-making problem, fuzzy expected value multilevel programming and chance-constrained multilevel programming are introduced. Furthermore, fuzzy simulation, neural network, and genetic algorithm are integrated to produce a hybrid intelligent algorithm for finding the Stackelberg-Nash equilibrium. Finally, two numerical examples are provided to illustrate the effectiveness of the hybrid intelligent algorithm
A New Hybrid Algorithm to Optimize Stochastic-fuzzy Capacitated Multi-Facility Location-allocation Problem
Facility location-allocation models are used in a widespread variety of applications to determine the number of required facility along with the relevant allocation process. In this paper, a new mathematical model for the capacitated multi-facility location-allocation problem with
probabilistic customer's locations and fuzzy customerβs demands under the Hurwicz criterion is proposed. This model is formulated as Ξ±-cost minimization model according to different criteria. Since our problem is strictly Np-hard, a new hybrid intelligent algorithm is presented to solve the stochastic-fuzzy model. The proposed algorithm is based on a vibration damping optimization (VDO) algorithm which is combined with the simplex algorithm and fuzzy simulation (SFVDO). Finally, a numerical example is presented to illustrate the capability of the proposed solving methodologies