Probability maximization model of 0-1 knapsack problem with random fuzzy variables

Takashi Hasuike; Hideki Katagiri; Hiroaki Ishii

doi:10.1109/FUZZY.2008.4630422

Probability maximization model of 0-1 knapsack problem with random fuzzy variables

Takashi Hasuike^*, Hideki Katagiri, Hiroaki Ishii

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

This paper considers a new model of 0-1 knapsack problem including probabilistic coefficients with ambiguous expected returns assumed as random fuzzy variables. Since the random fuzzy 0-1 knapsack problem is not well-defined integer programming problem due to involve random fuzzy variables, it is hard to construct the efficient solution method to solve this problem directly. In this paper, using chance constraints, possibility measure and fuzzy goal based on both stochastic and fuzzy programming approaches, the main problem is transformed into a deterministic equivalent quadratic integer programming. Then, the efficient solution method to find a strict optimal solution based on dynamic programming is constructed.

Original language	English
Title of host publication	2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
Pages	548-554
Number of pages	7
DOIs	https://doi.org/10.1109/FUZZY.2008.4630422
Publication status	Published - 2008 Nov 7
Externally published	Yes
Event	2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 - Hong Kong, China Duration: 2008 Jun 1 → 2008 Jun 6

Publication series

Name	IEEE International Conference on Fuzzy Systems
ISSN (Print)	1098-7584

Other

Other	2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
Country/Territory	China
City	Hong Kong
Period	08/6/1 → 08/6/6

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

Access to Document

10.1109/FUZZY.2008.4630422

Cite this

Hasuike, T, Katagiri, H & Ishii, H 2008, Probability maximization model of 0-1 knapsack problem with random fuzzy variables. in 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008., 4630422, IEEE International Conference on Fuzzy Systems, pp. 548-554, 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008, Hong Kong, China, 08/6/1. https://doi.org/10.1109/FUZZY.2008.4630422

@inproceedings{95b1008b2a09497f8268131701667977,

title = "Probability maximization model of 0-1 knapsack problem with random fuzzy variables",

abstract = "This paper considers a new model of 0-1 knapsack problem including probabilistic coefficients with ambiguous expected returns assumed as random fuzzy variables. Since the random fuzzy 0-1 knapsack problem is not well-defined integer programming problem due to involve random fuzzy variables, it is hard to construct the efficient solution method to solve this problem directly. In this paper, using chance constraints, possibility measure and fuzzy goal based on both stochastic and fuzzy programming approaches, the main problem is transformed into a deterministic equivalent quadratic integer programming. Then, the efficient solution method to find a strict optimal solution based on dynamic programming is constructed.",

author = "Takashi Hasuike and Hideki Katagiri and Hiroaki Ishii",

year = "2008",

month = nov,

day = "7",

doi = "10.1109/FUZZY.2008.4630422",

language = "English",

isbn = "9781424418190",

series = "IEEE International Conference on Fuzzy Systems",

pages = "548--554",

booktitle = "2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008",

note = "2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 ; Conference date: 01-06-2008 Through 06-06-2008",

}

TY - GEN

T1 - Probability maximization model of 0-1 knapsack problem with random fuzzy variables

AU - Hasuike, Takashi

AU - Katagiri, Hideki

AU - Ishii, Hiroaki

PY - 2008/11/7

Y1 - 2008/11/7

N2 - This paper considers a new model of 0-1 knapsack problem including probabilistic coefficients with ambiguous expected returns assumed as random fuzzy variables. Since the random fuzzy 0-1 knapsack problem is not well-defined integer programming problem due to involve random fuzzy variables, it is hard to construct the efficient solution method to solve this problem directly. In this paper, using chance constraints, possibility measure and fuzzy goal based on both stochastic and fuzzy programming approaches, the main problem is transformed into a deterministic equivalent quadratic integer programming. Then, the efficient solution method to find a strict optimal solution based on dynamic programming is constructed.

AB - This paper considers a new model of 0-1 knapsack problem including probabilistic coefficients with ambiguous expected returns assumed as random fuzzy variables. Since the random fuzzy 0-1 knapsack problem is not well-defined integer programming problem due to involve random fuzzy variables, it is hard to construct the efficient solution method to solve this problem directly. In this paper, using chance constraints, possibility measure and fuzzy goal based on both stochastic and fuzzy programming approaches, the main problem is transformed into a deterministic equivalent quadratic integer programming. Then, the efficient solution method to find a strict optimal solution based on dynamic programming is constructed.

UR - http://www.scopus.com/inward/record.url?scp=55249103399&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=55249103399&partnerID=8YFLogxK

U2 - 10.1109/FUZZY.2008.4630422

DO - 10.1109/FUZZY.2008.4630422

M3 - Conference contribution

AN - SCOPUS:55249103399

SN - 9781424418190

T3 - IEEE International Conference on Fuzzy Systems

SP - 548

EP - 554

BT - 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008

T2 - 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008

Y2 - 1 June 2008 through 6 June 2008

ER -

Probability maximization model of 0-1 knapsack problem with random fuzzy variables

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this