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

Czerniachowska, Kateryna; Hernes, Marcin

doi:10.3390/math8111881

Cited by 5 publications

(6 citation statements)

References 29 publications

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“…A series of recent studies indicated the usability of genetic algorithm [6,10,[35][36][37][38][39]. Hwang et al [35] presented a gradient search heuristic and genetic algorithm to solve the retailer's problem, taking into account brands selling on multi-level shelves.…”

Section: Solution Approachesmentioning

confidence: 99%

“…Czerniachowska and Hernes [10] applied a genetic algorithm for solving the SSAP with vertical position and price effects. In their model, the bottom shelves were dedicated to cheaper products; the top shelves were for placing more expensive products.…”

Section: Solution Approachesmentioning

confidence: 99%

“…Constraints (8) and (9) determine the lower and upper bound of product facings. Constraints (10) and (11) represent the lower and upper bound of side cappings per position. Constraints ( 12) and ( 13) guarantee the lower and upper bound of side nestings per position.…”

Section: Product Constraintsmentioning

confidence: 99%

“…Czerniachowska and Hernes [8] compared linear and non-linear profit functions with space elasticity parameters. They showed that the resulting total profit in the retailers' SSAP with vertical position effects significantly differ in these two profit functions.…”

Section: Introductionmentioning

confidence: 99%

“…This research adopts the capping and nesting allocation parameters used in another model with vertical shelf levels based on the product prices by Czerniachowska and Hernes [10,11]. Cappings mean allocation of items on top of each other, such as light rectangular cartoons.…”

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confidence: 99%

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

Czerniachowska

Hernes

2021

Symmetry

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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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Section: Solution Approachesmentioning

confidence: 99%

Section: Solution Approachesmentioning

confidence: 99%

Section: Product Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Czerniachowska

Hernes

2021

Symmetry

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Grass Cutter Heuristics for Knapsack-Like Problems of Resource Allocation

Czerniachowska,

Krajsic

2024

Lecture Notes in Networks and Systems

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Heuristics for the shelf space allocation problem

2023

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The retailers’ goals to maximize the profit of the products in stores are realized on the planogram shelves. In this paper, we investigated a practical shelf space allocation model with a visible horizontal and vertical grouping of products into categories, which takes into account the number of facings, capping and nesting of a product. The result is four groups of constraints, such as shelf constraints, product constraints, multi-shelves constraints, and category constraints that are used in the model. We proposed 6 heuristics to solve the planogram profit maximization problem. The developed techniques on which heuristics are based may be applied to other category of management shelf space allocation problems because all of them share the same nature of the problem, i.e., the initial step of creating the allocation of products on the shelf and steps in which shelves are combined. Experiments were based on data sets generated according to contemporary real retail conditions. The efficiency of the designed heuristics has been estimated using the CPLEX solver.

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

Cited by 5 publications

References 29 publications

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

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

Grass Cutter Heuristics for Knapsack-Like Problems of Resource Allocation

Heuristics for the shelf space allocation problem

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