A restricted neighbourhood Tabu Search for Storage Location Assignment Problem

Abstract

The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of picking the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the stateof-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.

Evolving Self-Adaptive Tabu Search Algorithm for Storage Location Assignment Problems

“…Each method runs for 30 times. Table 1 shows the results from A1, A2 and RNTS [5]. The best results are in bold.…”

“…This method was then combined with two sampling techniques to scale up the solutions for larger problems [4]. In a recent study, a Restricted Neighbourhood Tabu Search algorithm was proposed [5] that was found to be the most efficient algorithm for solving this problem. The design of the algorithm was based on the intuitive understanding and the theoretical analysis of the problem characteristics.…”

Optimizing the Cargo Location Assignment of Retail E-Commerce Based on an Artificial Fish Swarm Algorithm

“…Yang et al discussed the Container Stacking Position Determination Problem, specifically focusing on the storage space allocation problem in container terminals [10]. Xie et al developed an efficient Restricted Neighbourhood Tabu Search algorithm to solve the storage location assignment problem with grouping constraints [11]. Flamand et al investigated retail assortment planning along with store-wide shelf space allocation in a manner that maximizes the overall store profit [12].…”

The Optimization of the Location of the Cargo in Three-Dimension Shelf: Employing the FP-Tree and the Artificial Fish Swarm Algorithms

“…e allocation issue of the cargo location has received higher attention concerning some criteria such as the turnover efficiency of the cargo, the shelf stability, the picking routes, and the storage strategy of the warehouse. Xie et al [2] proposed an operative method called the Restricted Neighborhood Tabu Search algorithm to resolve the assignment problem of the storage location by using the Grouping Constraints. Elisa and Cristiano Alexandre [3] studied a class-based storage process employing a cube-perorder index.…”