A multi-objective supply chain optimisation using enhanced Bees Algorithm with adaptive neighbourhood search and site abandonment strategy

B. Yuce, Ernesto Mastrocinque, A. Lambiase, M. S. Packianather, D. T. Pham

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic Bees Algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic Bees Algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.
Original languageEnglish
Pages (from-to)71-82
JournalSwarm and Evolutionary Computation
Volume18
Early online date26 Apr 2014
DOIs
Publication statusPublished - Oct 2014
Externally publishedYes

Fingerprint

Neighborhood Search
Supply Chain
Supply chains
Optimization
Pareto Solutions
Ant colony optimization
Enhancement
Lead
Strategy
Minimise
Configuration
Costs
Experiment
Experiments

Bibliographical note

The full text is currently unavailable on the repository.

Keywords

  • Supply chain management
  • Multi-objective optimisation
  • Swarm-based optimisation
  • Bees Algorithm
  • Adaptive neighbourhood search
  • Site abandonment

Cite this

A multi-objective supply chain optimisation using enhanced Bees Algorithm with adaptive neighbourhood search and site abandonment strategy. / Yuce, B.; Mastrocinque, Ernesto; Lambiase, A.; Packianather, M. S.; Pham, D. T.

In: Swarm and Evolutionary Computation, Vol. 18, 10.2014, p. 71-82.

Research output: Contribution to journalArticle

@article{4e579a2dd49d4057b1b63dd7c6427b72,
title = "A multi-objective supply chain optimisation using enhanced Bees Algorithm with adaptive neighbourhood search and site abandonment strategy",
abstract = "In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic Bees Algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic Bees Algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.",
keywords = "Supply chain management, Multi-objective optimisation, Swarm-based optimisation, Bees Algorithm, Adaptive neighbourhood search, Site abandonment",
author = "B. Yuce and Ernesto Mastrocinque and A. Lambiase and Packianather, {M. S.} and Pham, {D. T.}",
note = "The full text is currently unavailable on the repository.",
year = "2014",
month = "10",
doi = "10.1016/j.swevo.2014.04.002",
language = "English",
volume = "18",
pages = "71--82",
journal = "Evolutionary Computation",
issn = "2210-6502",
publisher = "MIT Press",

}

TY - JOUR

T1 - A multi-objective supply chain optimisation using enhanced Bees Algorithm with adaptive neighbourhood search and site abandonment strategy

AU - Yuce, B.

AU - Mastrocinque, Ernesto

AU - Lambiase, A.

AU - Packianather, M. S.

AU - Pham, D. T.

N1 - The full text is currently unavailable on the repository.

PY - 2014/10

Y1 - 2014/10

N2 - In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic Bees Algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic Bees Algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.

AB - In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic Bees Algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic Bees Algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.

KW - Supply chain management

KW - Multi-objective optimisation

KW - Swarm-based optimisation

KW - Bees Algorithm

KW - Adaptive neighbourhood search

KW - Site abandonment

U2 - 10.1016/j.swevo.2014.04.002

DO - 10.1016/j.swevo.2014.04.002

M3 - Article

VL - 18

SP - 71

EP - 82

JO - Evolutionary Computation

JF - Evolutionary Computation

SN - 2210-6502

ER -