A Hybrid Fuzzy Optimization Model to minimize logistics cost

Author

Lau, HCW

Author_Institution

Sch. of Manage., Univ. of Western, Sydney, NSW, Australia

fYear

2012

fDate

29-31 May 2012

Firstpage

404

Lastpage

408

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

costing; fuzzy set theory; genetic algorithms; logistics; search problems; simulated annealing; supply chain management; HFOM; fuzzy logic; genetic algorithms; hybrid fuzzy optimization model; lateral transshipment; minimize logistics cost; simulated annealing; supplier selection; supply chain network; tabu search; vehicle routing; Fuzzy logic; Genetic algorithms; Hafnium compounds; Optimization; Routing; Supply chains; Vehicles; Fuzzy logic model; genetic algorithms; stochastic demand; supply chain management;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on

Conference_Location

Sichuan

Print_ISBN

978-1-4673-0025-4

Type

conf

DOI

10.1109/FSKD.2012.6233890

Filename

6233890