Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem

This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules.

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Simulated Annealing Hyper-Heuristic for a Shelf Space Allocation on Symmetrical Planograms Problem

Symmetry ◽

10.3390/sym13071182 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1182

Author(s):

Kateryna Czerniachowska ◽

Marcin Hernes

Keyword(s):

Simulated Annealing ◽

Simulated Annealing Algorithm ◽

Profit Maximization ◽

Point Of View ◽

Maximization Problem ◽

Shelf Space ◽

Allocation Model ◽

Space Allocation ◽

Effective Decision Making ◽

Shelf Space Allocation

The allocation of products on shelves is an important issue from the point of view of effective decision making by retailers. In this paper, we investigate a practical shelf space allocation model which takes into account the number of facings, capping, and nesting of a product. We divide the shelf into the segments of variable size in which the products of the specific types could be placed. The interconnections between products are modelled with the help of categorizing the products into specific types as well as grouping some of them into clusters. This results in four groups of constraints—shelf constraints, shelf type constraints, product constraints, position allocation constraints—that are used in the model for aesthetic symmetry of a planogram. We propose a simulated annealing algorithm with improvement and reallocation procedures to solve the planogram profit maximization problem. Experiments are based on artificial data sets that have been generated according to real-world conditions. The efficiency of the designed algorithm has been estimated using the CPLEX solver. The computational tests demonstrate that the proposed algorithm gives valuable results in an acceptable time.

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A Genetic Algorithm for the Shelf-Space Allocation Problem with Vertical Position Effects

Mathematics ◽

10.3390/math8111881 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1881

Author(s):

Kateryna Czerniachowska ◽

Marcin Hernes

Keyword(s):

Genetic Algorithm ◽

Operation Management ◽

Vertical Position ◽

Experimental Phase ◽

Shelf Space ◽

Retail Environment ◽

Position Effects ◽

Allocation Model ◽

Space Allocation ◽

Shelf Space Allocation

The shelf-space on which products are displayed is one of the most important resources in the retail environment. Therefore, decisions about shelf-space allocation and optimization are critical in retail operation management. This paper addresses the problem of a retailer who sells various products by displaying them on the shelf at stores. We present a practical shelf-space allocation model, based on a genetic algorithm, with vertical position effects with the objective of maximizing the retailer’s profit. The validity of the model is illustrated with example problems and compared to the CPLEX solver. The results obtained from the experimental phase show the suitability of the proposed approach.

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Shelf Space Allocation and Profit Maximization in Mass Retailing

Journal of Marketing ◽

10.1177/002224297303700308 ◽

1973 ◽

Vol 37 (3) ◽

pp. 54-60 ◽

Cited By ~ 13

Author(s):

Ronald C. Curhan

Keyword(s):

Conceptual Models ◽

Empirical Studies ◽

Profit Maximization ◽

Shelf Space ◽

For Profit ◽

Space Allocation ◽

Practical Management ◽

Shelf Space Allocation ◽

Relationship Of ◽

The Relationship

Conceptual models and empirical studies of the relationship of shelf space allocation to unit sales are reviewed in this article. This knowledge is organized to support specific recommendations for the practical management of shelf space for profit maximization.

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Shelf Space Allocation and Profit Maximization in Mass Retailing

Journal of Marketing ◽

10.2307/1249947 ◽

1973 ◽

Vol 37 (3) ◽

pp. 54 ◽

Cited By ~ 43

Author(s):

Ronald C. Curhan

Keyword(s):

Profit Maximization ◽

Shelf Space ◽

Space Allocation ◽

Shelf Space Allocation

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A Heuristic Approach to Shelf Space Allocation Decision Support Including Facings, Capping, and Nesting

Symmetry ◽

10.3390/sym13020314 ◽

2021 ◽

Vol 13 (2) ◽

pp. 314

Author(s):

Kateryna Czerniachowska ◽

Marcin Hernes

Keyword(s):

Decision Support ◽

Time Interval ◽

Short Time Interval ◽

Allocation Decision ◽

Shelf Space ◽

Retail Environment ◽

Allocation Model ◽

Space Allocation ◽

Shelf Space Allocation ◽

Short Time

Shelf space on which products are exhibited is a scarce resource in the retail environment. Retailers regularly make decisions related to allocating products to their outlets’ limited shelf space. The aim of the paper was to develop a practical shelf space allocation model offering the possibility of horizontal and vertical product grouping, representing an item (product) with facings, capping, and nesting, with the objective of maximizing the retailer’s profit. Because real category-management problems address a lot of retailer’s rules, we expanded the basic shelf space allocation model, using shelf constraints, product constraints, multi-shelves constraints, and category constraints. To solve the problem, we proposed two adjustable methods that allowed us to achieve good results within a short time interval. The validity of algorithms was estimated, using the CPLEX solver and illustrated with example problems. Experiments were performed on data generated on the basis of real retail values. To estimate the performance of the proposed approach, 45 cases were tested. Among them, the proposed approach found solutions in 34 cases, while CPLEX found solutions only in 23 cases. The profit ratio of the proposed approach is, on average, 94.57%, with minimal and maximal values of 86.80% and 99.84%, accordingly.

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