A genetic programming-based hyper-heuristic approach for storage location assignment problem

Jing Xie, Yi Mei, Andreas T. Ernst, Xiaodong Li, Andy Song

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

15 Citations (Scopus)

Abstract

This study proposes a method for solving real-world warehouse Storage Location Assignment Problem (SLAP) under grouping constraints by Genetic Programming (GP). Integer Linear Programming (ILP) formulation is used to define the problem. By the proposed GP method, a subset of the items is repeatedly selected and placed into the available current best location of the shelves in the warehouse, until all the items have been assigned with locations. A heuristic matching function is evolved by GP to guide the selection of the subsets of items. Our comparison between the proposed GP approach and the traditional ILP approach shows that GP can obtain near-optimal solutions on the training data within a short period of time. Moreover, the evolved heuristics can achieve good optimization results on unseen scenarios, comparable to that on the scenario used for training. This shows that the evolved heuristics have good reusability and can be directly applied for slightly different scenarios without any new search process.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3000-3007
Number of pages8
ISBN (Electronic)9781479914883
DOIs
Publication statusPublished - 16 Sep 2014
Externally publishedYes
EventIEEE Congress on Evolutionary Computation 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014
https://ieeexplore.ieee.org/xpl/conhome/6880677/proceeding (Proceedings)

Conference

ConferenceIEEE Congress on Evolutionary Computation 2014
Abbreviated titleIEEE CEC 2014
CountryChina
CityBeijing
Period6/07/1411/07/14
Internet address

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