Heuristic, meta-heuristic and hyper-heuristic approaches for fresh produce inventory control and shelf space allocation

R. Bai, E. K. Burke, G. Kendall

Research output: Journal PublicationArticlepeer-review

50 Citations (Scopus)


The allocation of fresh produce to shelf space represents a new decision support research area which is motivated by the desire of many retailers to improve their service due to the increasing demand for fresh food. However, automated decision making for fresh produce allocation is challenging because of the very short lifetime of fresh products. This paper considers a recently proposed practical model for the problem which is motivated by our collaboration with Tesco. Moreover, the paper investigates heuristic and meta-heuristic approaches as alternatives for the generalized reduced gradient algorithm, which becomes inefficient when the problem size becomes larger. A simpler single-item inventory problem is firstly studied and solved by a polynomial time bounded procedure. Several dynamic greedy heuristics are then developed for the multi-item problem based on the procedure for the single-item inventory problem. Experimental results show that these greedy heuristics are much more efficient and provide competitive results when compared to those of a multi-start generalized reduced gradient algorithm. In order to further improve the solution, we investigated simulated annealing, a greedy randomized adaptive search procedure and three types of hyper-heuristics. Their performance is tested and compared on a set of problem instances which are made publicly available for the research community.

Original languageEnglish
Pages (from-to)1387-1397
Number of pages11
JournalJournal of the Operational Research Society
Issue number10
Publication statusPublished - Oct 2008
Externally publishedYes


  • Fresh produce
  • Hyper-heuristics
  • Inventory
  • Meta-heuristics
  • Shelf space allocation

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modelling and Simulation
  • Strategy and Management
  • Management Science and Operations Research


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