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

Abstract

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.

An Interaction-Oriented Model of Customer Behavior for the Simulation of Supermarkets

“…dedicated diagrams) have been widely used in early analogical simulations to anticipate, in an empirical way, the consequences of product placement on the behavior of consumers [7]. They are usually produced by many professional softwares, among which some allow a 3D-rendering of the shelves and virtual visits.…”

A cooperative hyper-heuristic search framework

“…Bai et al (2008), Soubeiga (2003), and Dowsland et al (2007) introduced a simulated annealing acceptance criterion into the hyperheuristic framework. Ayob and Kendall (2003) proposed a Monte Carlo based hyperheuristic which accepts improving and non improving low level heuristics using a probabilistic framework.…”

Generalized hyper-heuristics for solving 2D Regular and Irregular Packing Problems

“…The work by Bai et al (2008) presents a combination of heuristic, meta-heuristic and hyper-heuristic approaches for inventory control and allocation in the fresh produce industry. Additional work related to hyper-heuristics, bin-packing methods, and their application to other optimization problems is reported by Dowsland et al (2007), Burke et al (2003bBurke et al ( , 2006Burke et al ( , 2007, Kendall et al (2004), and Poli et al (2007).…”