Fuzzy multi-objective optimization algorithms for solving multi-mode automated guided vehicles by considering machine break time and artificial neural network

Title:

Fuzzy multi-objective optimization algorithms for solving multi-mode automated guided vehicles by considering machine break time and artificial neural network

Creator:

Nabovati, Hojat , Haleh, Hassan , and Vahdani, Behnam

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:c1d0b257-c142-45eb-a9bf-e247e6ca3799
uuid:c1d0b257-c142-45eb-a9bf-e247e6ca3799
doi:10.14311/NNW.2018.28.016

Subject:

scheduling, AGV, MOIWO, MOCS, and ANN

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem. An artificial neural network approach is used to estimate machine's breakdown indexes. Since the model is strictly NP-hard and because objectives contradict each other, two developed meta-heuristic algorithms called “fuzzy multi-objective invasive weeds optimization algorithm" and “fuzzy multi-objective cuckoo search algorithm" with a new chromosome structure which guarantees the feasibility of solutions are developed to solve the proposed problem. Since there is no benchmark available on literature, three other metaheuristic algorithms are developed with a similar solution structure to validate performance of the proposed algorithms. Computational results showed that developed fuzzy multi-objective invasive weeds optimization algorithm had the best performance in terms of solving problems compared to four other algorithms.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2018 Volume:28 | Number:3

Harvested from:

CDK

Metadata only:

false