A Learning-based Iterated Local Search Algorithm for Order Batching and Sequencing Problems

Lijie Zhou; Chengran Lin; Qian Ma; Zhengcai Cao

doi:10.1109/CASE49997.2022.9926486

A Learning-based Iterated Local Search Algorithm for Order Batching and Sequencing Problems

Lijie Zhou
, Chengran Lin
, Qian Ma
, Zhengcai Cao^*

^*Corresponding author for this work

Beijing University of Chemical Technology

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

2 Scopus citations

Abstract

An order batching and sequencing problem in a warehouse is studied in this work. The problem is proved to be an NP-hard problem. A mathematical programming model is formulated to describe it clearly. To minimize tardiness, an improved iterated local search algorithm based on reinforcement learning is proposed. An operator selecting scheme, which aims to automatically select local search operator combinations instead of simply performing all the operators in each iteration, is designed to reduce the computational cost greatly. Besides, an adaptive perturbation mechanism is designed to improve its global search ability. Extensive simulation experimental results and comparisons with the state of the art demonstrate the high effectiveness and efficiency of the proposed approach.

Original language	English
Title of host publication	2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
Publisher	IEEE Computer Society
Pages	1741-1746
Number of pages	6
ISBN (Electronic)	9781665490429
DOIs	https://doi.org/10.1109/CASE49997.2022.9926486
State	Published - 2022
Externally published	Yes
Event	18th IEEE International Conference on Automation Science and Engineering, CASE 2022 - Mexico City, Mexico Duration: 20 Aug 2022 → 24 Aug 2022

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2022-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Country/Territory	Mexico
City	Mexico City
Period	20/08/22 → 24/08/22

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10.1109/CASE49997.2022.9926486

Cite this

Zhou, L., Lin, C., Ma, Q., & Cao, Z. (2022). A Learning-based Iterated Local Search Algorithm for Order Batching and Sequencing Problems. In 2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022 (pp. 1741-1746). (IEEE International Conference on Automation Science and Engineering; Vol. 2022-August). IEEE Computer Society. https://doi.org/10.1109/CASE49997.2022.9926486

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title = "A Learning-based Iterated Local Search Algorithm for Order Batching and Sequencing Problems",

abstract = "An order batching and sequencing problem in a warehouse is studied in this work. The problem is proved to be an NP-hard problem. A mathematical programming model is formulated to describe it clearly. To minimize tardiness, an improved iterated local search algorithm based on reinforcement learning is proposed. An operator selecting scheme, which aims to automatically select local search operator combinations instead of simply performing all the operators in each iteration, is designed to reduce the computational cost greatly. Besides, an adaptive perturbation mechanism is designed to improve its global search ability. Extensive simulation experimental results and comparisons with the state of the art demonstrate the high effectiveness and efficiency of the proposed approach.",

author = "Lijie Zhou and Chengran Lin and Qian Ma and Zhengcai Cao",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 18th IEEE International Conference on Automation Science and Engineering, CASE 2022 ; Conference date: 20-08-2022 Through 24-08-2022",

year = "2022",

doi = "10.1109/CASE49997.2022.9926486",

language = "英语",

series = "IEEE International Conference on Automation Science and Engineering",

publisher = "IEEE Computer Society",

pages = "1741--1746",

booktitle = "2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022",

address = "美国",

}

Zhou, L, Lin, C, Ma, Q & Cao, Z 2022, A Learning-based Iterated Local Search Algorithm for Order Batching and Sequencing Problems. in 2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022. IEEE International Conference on Automation Science and Engineering, vol. 2022-August, IEEE Computer Society, pp. 1741-1746, 18th IEEE International Conference on Automation Science and Engineering, CASE 2022, Mexico City, Mexico, 20/08/22. https://doi.org/10.1109/CASE49997.2022.9926486

A Learning-based Iterated Local Search Algorithm for Order Batching and Sequencing Problems. / Zhou, Lijie; Lin, Chengran; Ma, Qian et al.
2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022. IEEE Computer Society, 2022. p. 1741-1746 (IEEE International Conference on Automation Science and Engineering; Vol. 2022-August).

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

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N2 - An order batching and sequencing problem in a warehouse is studied in this work. The problem is proved to be an NP-hard problem. A mathematical programming model is formulated to describe it clearly. To minimize tardiness, an improved iterated local search algorithm based on reinforcement learning is proposed. An operator selecting scheme, which aims to automatically select local search operator combinations instead of simply performing all the operators in each iteration, is designed to reduce the computational cost greatly. Besides, an adaptive perturbation mechanism is designed to improve its global search ability. Extensive simulation experimental results and comparisons with the state of the art demonstrate the high effectiveness and efficiency of the proposed approach.

AB - An order batching and sequencing problem in a warehouse is studied in this work. The problem is proved to be an NP-hard problem. A mathematical programming model is formulated to describe it clearly. To minimize tardiness, an improved iterated local search algorithm based on reinforcement learning is proposed. An operator selecting scheme, which aims to automatically select local search operator combinations instead of simply performing all the operators in each iteration, is designed to reduce the computational cost greatly. Besides, an adaptive perturbation mechanism is designed to improve its global search ability. Extensive simulation experimental results and comparisons with the state of the art demonstrate the high effectiveness and efficiency of the proposed approach.

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A Learning-based Iterated Local Search Algorithm for Order Batching and Sequencing Problems

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