An Evolutionary Algorithm for the Product to Shelf Allocation Problem

Esparcia-Alcázar, Anna I.; Lluch-Revert, Lidia; Albarractn-Guillem, J.M.; Palmer-Gato, Marta E.; Sharman, Ken

doi:10.1109/cec.2006.1688714

Cited by 5 publications

(9 citation statements)

References 9 publications

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“…We are currently studying this issue and, in particular, other ways of representing the affinities in a nonnumerical manner. We have introduced a two-stage method to solve the LISSAP which differs from the single stage approach of our previous work [5] [6]. In the first stage an allocation algorithm determines the number of modules per group in agreement with the standard shop requirements; the second stage uses an Evolutionary Strategy (μ + λ)-ES to fulfill the requirement of affinity between groups.…”

Section: Resultsmentioning

confidence: 99%

“…This means that, if there are enough modules in the shop, the number of modules to allocate to a group will be at lest equal to the standard, given by the first column of the SS matrix 3 . For this reason and even though in previous work [5][6] we used an evolutionary algorithm which included both requirements in the fitness function, here we have adopted a different approach. We propose to solve the LiSSAP in two stages; the first one assigns numbers of modules to the groups and the second allocates specific modules.…”

Section: B Objectivementioning

confidence: 99%

“…In previous work [5] and [6], we gave the first steps towards the development of such a tool by employing evolutionary algorithms. Here we show different issues involved in the various choices that can be made within the tool.…”

Section: Introductionmentioning

confidence: 99%

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A new two-stage approach to solve the Linear Shelf Space Allocation Problem

Lluch-Revert

Esparcia-Alcázar

Albarracín-Guillem

et al. 2007

2007 IEEE Congress on Evolutionary Computation

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In previous work we gave the first steps towards the development of a commercially viable tool that used evolutionary computation to address the Linear Shelf Space Allocation Problem (LiSSAP). This consists on allocating lengths of shelves in a given shop to specific products or groups of products.Here we present a procedure to solve the problem based on two stages. The first one relies on finding the "ideal" number of modules for every group of products. The second one uses an evolutionary algorithm (μ + λ)-ES to fulfill groups affinity requirements. Moreover, a new distance measure between modules is also introduced, which has an important effect on the fitness function.Finally, we analyse different choices that can be made in the evolutionary algorithm and how they affect the global performance of the algorithm. These involve the chromosome encoding and the λ parameter. The different combinations described are tested on two real problem configurations.

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Section: Resultsmentioning

confidence: 99%

Section: B Objectivementioning

confidence: 99%

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A new two-stage approach to solve the Linear Shelf Space Allocation Problem

Lluch-Revert

Esparcia-Alcázar

Albarracín-Guillem

et al. 2007

2007 IEEE Congress on Evolutionary Computation

Self Cite

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“…Very rarely did a situation occur where store obstructions or inconvenient parts of the shelf decreased the shelf width. The prepared nine product sets included 10,15,20,25,30,35,40,45, and 50 products with different widths, weights, and heights. The shelves widths were 250, 375, 500, 625, and 750 cm.…”

Section: Resultsmentioning

confidence: 99%

“…• -product of the defined category and subcategory can be placed on a shelf of the defined category and subcategory. The higher sales-potential subcategory (10,20,30) is, the darker is the color. Different main colors (blue, green, red) represent different categories (A, B, C).…”

Section: Problem Definition and Methodsmentioning

confidence: 99%

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

Czerniachowska

Hernes

2021

Symmetry

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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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