Ant colony optimization for automated storage and retrieval system

Bo Xing; Wen Jing Gao; Fulufhelo V. Nelwamondo; Kimberly Battle; Tshilidzi Marwala

doi:10.1109/CEC.2010.5586237

Ant colony optimization for automated storage and retrieval system

Bo Xing
, Wen Jing Gao
, Fulufhelo V. Nelwamondo
, Kimberly Battle
, Tshilidzi Marwala

Faculty of Engineering and the Built Environment

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

13 Citations (Scopus)

Abstract

In this work, we first propose an NP-hard combinatorial problem, that is, the storage and retrieve (S/R) machine travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas such inventory items in logistics and work-in-process storage in manufacturing systems. And then, we investigate the feasibility of using ant colony optimization (ACO) meta-heuristics to address the proposed problem. Simulation tests are executed separately based on two ACO algorithms. Finally, the S/R machine operating performance measure index such as total travel distance and total travel time are employed to evaluate the experimental results achieved by different ACO algorithms. Experimental case study demonstrates the effectiveness and applicability of the selected ACO approaches to our proposed BOP problem.

Original language	English
Title of host publication	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOIs	https://doi.org/10.1109/CEC.2010.5586237
Publication status	Published - 2010
Event	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain Duration: 18 Jul 2010 → 23 Jul 2010

Publication series

Name	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Conference

Conference	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Country/Territory	Spain
City	Barcelona
Period	18/07/10 → 23/07/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

ASJC Scopus subject areas

Computational Theory and Mathematics
Applied Mathematics

Access to Document

10.1109/CEC.2010.5586237

Cite this

Xing, B., Gao, W. J., Nelwamondo, F. V., Battle, K., & Marwala, T. (2010). Ant colony optimization for automated storage and retrieval system. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 Article 5586237 (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010). https://doi.org/10.1109/CEC.2010.5586237

Xing, Bo ; Gao, Wen Jing ; Nelwamondo, Fulufhelo V. et al. / Ant colony optimization for automated storage and retrieval system. 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010).

@inproceedings{571e0215bb51431fbb9af15b15477c74,

title = "Ant colony optimization for automated storage and retrieval system",

abstract = "In this work, we first propose an NP-hard combinatorial problem, that is, the storage and retrieve (S/R) machine travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas such inventory items in logistics and work-in-process storage in manufacturing systems. And then, we investigate the feasibility of using ant colony optimization (ACO) meta-heuristics to address the proposed problem. Simulation tests are executed separately based on two ACO algorithms. Finally, the S/R machine operating performance measure index such as total travel distance and total travel time are employed to evaluate the experimental results achieved by different ACO algorithms. Experimental case study demonstrates the effectiveness and applicability of the selected ACO approaches to our proposed BOP problem.",

author = "Bo Xing and Gao, \{Wen Jing\} and Nelwamondo, \{Fulufhelo V.\} and Kimberly Battle and Tshilidzi Marwala",

year = "2010",

doi = "10.1109/CEC.2010.5586237",

language = "English",

isbn = "9781424469109",

series = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010",

booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010",

note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 ; Conference date: 18-07-2010 Through 23-07-2010",

}

Xing, B, Gao, WJ, Nelwamondo, FV, Battle, K & Marwala, T 2010, Ant colony optimization for automated storage and retrieval system. in 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010., 5586237, 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18/07/10. https://doi.org/10.1109/CEC.2010.5586237

Ant colony optimization for automated storage and retrieval system. / Xing, Bo; Gao, Wen Jing; Nelwamondo, Fulufhelo V. et al.
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. 5586237 (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010).

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

TY - GEN

T1 - Ant colony optimization for automated storage and retrieval system

AU - Xing, Bo

AU - Gao, Wen Jing

AU - Nelwamondo, Fulufhelo V.

AU - Battle, Kimberly

AU - Marwala, Tshilidzi

PY - 2010

Y1 - 2010

N2 - In this work, we first propose an NP-hard combinatorial problem, that is, the storage and retrieve (S/R) machine travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas such inventory items in logistics and work-in-process storage in manufacturing systems. And then, we investigate the feasibility of using ant colony optimization (ACO) meta-heuristics to address the proposed problem. Simulation tests are executed separately based on two ACO algorithms. Finally, the S/R machine operating performance measure index such as total travel distance and total travel time are employed to evaluate the experimental results achieved by different ACO algorithms. Experimental case study demonstrates the effectiveness and applicability of the selected ACO approaches to our proposed BOP problem.

AB - In this work, we first propose an NP-hard combinatorial problem, that is, the storage and retrieve (S/R) machine travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas such inventory items in logistics and work-in-process storage in manufacturing systems. And then, we investigate the feasibility of using ant colony optimization (ACO) meta-heuristics to address the proposed problem. Simulation tests are executed separately based on two ACO algorithms. Finally, the S/R machine operating performance measure index such as total travel distance and total travel time are employed to evaluate the experimental results achieved by different ACO algorithms. Experimental case study demonstrates the effectiveness and applicability of the selected ACO approaches to our proposed BOP problem.

UR - https://www.scopus.com/pages/publications/79959403527

U2 - 10.1109/CEC.2010.5586237

DO - 10.1109/CEC.2010.5586237

M3 - Conference contribution

AN - SCOPUS:79959403527

SN - 9781424469109

T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Y2 - 18 July 2010 through 23 July 2010

ER -

Xing B, Gao WJ, Nelwamondo FV, Battle K, Marwala T. Ant colony optimization for automated storage and retrieval system. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. 5586237. (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010). doi: 10.1109/CEC.2010.5586237

Ant colony optimization for automated storage and retrieval system

Abstract

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UN SDGs

ASJC Scopus subject areas

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