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

Czerniachowska, Kateryna; Hernes, Marcin

doi:10.3390/sym13071182

Cited by 11 publications

(4 citation statements)

References 44 publications

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“…Czerniachowska, K et al 20 have addressed an interesting problem of arranging products on shelves which can be a strenuous task involving retailing decisions. Therefore, they have applied the SA procedure to handle the effective decision making of shelf allocation of products on symmetrical planograms.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

et al. 2021

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Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

et al. 2021

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“…Anic, Radas and Lim (2010), Desrochers and Nelson (2006), Elbers (2016) studied the dependencies between the product on shelf location and visual merchandising, the customers buying behaviour and product categorisation. Czerniachowska and Hernes (2021) proposed horizontal and vertical product grouping based on product sales potential, so that the products could be placed on the shelf not lower than their sales potential.…”

Section: Introductionmentioning

confidence: 99%

A linearization approach in solving a non-linear shelf space allocation problem with vertical categorisation of sales potential levels

Czerniachowska,

Hernes,

Lutosławski

2024

Argumenta Oeconomica

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Retail is a profit-driven, highly competitive industry. Customers expect retailers to provide appropriate assortment sets and excellent product visibility. The aim of this study was to develop and examine two models for optimising category-level shelf space management that maximise a retailer’s profit. The authors developed two shelf space allocation problem models. The first model combines three sets of constraints: shelf, product, and product group constraints, while the second model enlarges the first with the multi-shelves constraints. The study showed that this approach gives an optimal solution in a very short time (approximately 3 seconds on average and less than a second in 93 of the 134 instances for the first, and in 84 of the 146 instances in the second problem) for large-scale instances, which in most of the test cases is even less than a second. First, non-linear formulations of both problems were presented. Next, the authors proposed to use and adjust linearization techniques which allow transforming both problems into linear ones, thus obtaining the optimal solutions. Finally, both problems were solved using the CPLEX solver, the computational results were provided.

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“…Likewise, Garza-Santisteban et al proposed a high-level solver based on SA for selecting the best heuristic sequence that fulfills the requirements of multiple instances of Job Shop Scheduling problems [23]. Moreover, several hybrid approaches incorporating SA-based processes are easily found in the literature [24], [25], [26]. For example, Mosadegh et al presented a hyper-heuristic based on Q-Learning and Simulated Annealing for dealing with the mixed-model sequencing problem, including stochastic processing times in a multi-station assembly line [27].…”

Section: Introductionmentioning

confidence: 99%

Automatic Design of Metaheuristics for Practical Engineering Applications

et al. 2023

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It is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it is necessary to find sufficiently good tuning parameters to achieve satisfactory performance with the selected approach. In this context, the automatic design of algorithms, particularly those based on heuristics, has been increasing in popularity in the previous years due to its undoubted relevance nowadays. This paper explores a novel approach based on hyper-heuristics to carefully select population-based search operators and their tuning parameters to generate metaheuristics capable of dealing with a given practical engineering problem. The proposed strategy is assessed using three highly relevant and illustrative problems: training Artificial Neural Networks, designing PID controllers, and modeling a calorimetric phenomenon based on fractional calculus. In addition, we implement three well-known optimization metaheuristics to compare achieved solutions via the proposed hyper-heuristic strategy, namely Particle Swarm Optimization, Genetic Algorithm, and Cuckoo Search. Results from extensive numerical tests prove that the customized metaheuristics are generally superior to the three wellknown algorithms, taking only a few iterations to converge to an optimal solution. This is an excellent indicator of alleviating the effort and expertise required to choose the proper methodology when dealing with real-valued optimization problems.

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

Cited by 11 publications

References 44 publications

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

A linearization approach in solving a non-linear shelf space allocation problem with vertical categorisation of sales potential levels

Automatic Design of Metaheuristics for Practical Engineering Applications

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