A bi-level optimization model for grouping constrained storage location assignment problems

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

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)


In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu method over the random search method.

Original languageEnglish
Pages (from-to)385-398
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number1
Publication statusPublished - 1 Jan 2018


  • Grouping constraint (GC)
  • heuristics
  • optimization
  • storage location assignment problem
  • tabu search

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