A hybrid fuzzy optimization model to minimize logistics cost

H. C. W. Lau

doi:10.1109/FSKD.2012.6233890

A hybrid fuzzy optimization model to minimize logistics cost

H. C. W. Lau

School of Business

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

1 Citation (Scopus)

Abstract

This paper presents a supply chain network in which supplier selection, lateral transshipment, and vehicle routing can be involved. We develop a Hybrid Fuzzy Optimization Model (HFOM) based on the integration of fuzzy logic and genetic algorithms to solve the problem. In order to demonstrate the effectiveness of the HFOM, several approaches including branch and bound, standard GA, simulated annealing, and tabu search, are utilized to compare with the HFOM through simulations. Results show that the HFOM outperforms other search methods.

Original language	English
Title of host publication	Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery, 29-31 May 2012, Chongqing, China
Publisher	IEEE
Pages	404-408
Number of pages	5
ISBN (Print)	9781467300254
DOIs	https://doi.org/10.1109/FSKD.2012.6233890
Publication status	Published - 2012
Event	International Conference on Fuzzy Systems and Knowledge Discovery - Duration: 29 May 2012 → …

Conference

Conference	International Conference on Fuzzy Systems and Knowledge Discovery
Period	29/05/12 → …

Keywords

business logistics
fuzzy logic
genetic algorithms
supply chain management

Access to Document

10.1109/FSKD.2012.6233890

Cite this

@inproceedings{769b7fa81e4c4179be638e2583f79752,

title = "A hybrid fuzzy optimization model to minimize logistics cost",

abstract = "This paper presents a supply chain network in which supplier selection, lateral transshipment, and vehicle routing can be involved. We develop a Hybrid Fuzzy Optimization Model (HFOM) based on the integration of fuzzy logic and genetic algorithms to solve the problem. In order to demonstrate the effectiveness of the HFOM, several approaches including branch and bound, standard GA, simulated annealing, and tabu search, are utilized to compare with the HFOM through simulations. Results show that the HFOM outperforms other search methods.",

keywords = "business logistics, fuzzy logic, genetic algorithms, supply chain management",

author = "Lau, {H. C. W.}",

year = "2012",

doi = "10.1109/FSKD.2012.6233890",

language = "English",

isbn = "9781467300254",

pages = "404--408",

booktitle = "Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery, 29-31 May 2012, Chongqing, China",

publisher = "IEEE",

note = "International Conference on Fuzzy Systems and Knowledge Discovery ; Conference date: 29-05-2012",

}

TY - GEN

T1 - A hybrid fuzzy optimization model to minimize logistics cost

AU - Lau, H. C. W.

PY - 2012

Y1 - 2012

N2 - This paper presents a supply chain network in which supplier selection, lateral transshipment, and vehicle routing can be involved. We develop a Hybrid Fuzzy Optimization Model (HFOM) based on the integration of fuzzy logic and genetic algorithms to solve the problem. In order to demonstrate the effectiveness of the HFOM, several approaches including branch and bound, standard GA, simulated annealing, and tabu search, are utilized to compare with the HFOM through simulations. Results show that the HFOM outperforms other search methods.

AB - This paper presents a supply chain network in which supplier selection, lateral transshipment, and vehicle routing can be involved. We develop a Hybrid Fuzzy Optimization Model (HFOM) based on the integration of fuzzy logic and genetic algorithms to solve the problem. In order to demonstrate the effectiveness of the HFOM, several approaches including branch and bound, standard GA, simulated annealing, and tabu search, are utilized to compare with the HFOM through simulations. Results show that the HFOM outperforms other search methods.

KW - business logistics

KW - fuzzy logic

KW - genetic algorithms

KW - supply chain management

UR - http://handle.uws.edu.au:8081/1959.7/520970

U2 - 10.1109/FSKD.2012.6233890

DO - 10.1109/FSKD.2012.6233890

M3 - Conference Paper

SN - 9781467300254

SP - 404

EP - 408

BT - Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery, 29-31 May 2012, Chongqing, China

PB - IEEE

T2 - International Conference on Fuzzy Systems and Knowledge Discovery

Y2 - 29 May 2012

ER -

A hybrid fuzzy optimization model to minimize logistics cost

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this