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Automated Optimal Layout Generator for Animal Shelters: A framework based on Genetic Algorithm, TOPSIS and Graph Theory

Arghavan Jalayer, Masoud Jalayer, Mehdi Khakzand, Mohsen Faizi

TL;DR

This work tackles automated cage-layout design for animal shelters by integrating a Genetic Algorithm, a graph-based accessibility analysis, and TOPSIS for multi-criteria ranking. The framework optimizes four criteria—AC, LSP, ASP, and CF—to balance shelter capacity, ease of access, and noise reduction, while allowing management to weight criteria according to priorities. A graph-theoretic distance and a CF metric quantify welfare-relevant factors, and TOPSIS ranks layouts within GA iterations to drive Pareto-like trade-offs. Demonstrations on cat and dog kennels show feasible runtimes and practical layouts that adapt to varying priorities, underscoring the framework's value as a decision-support tool for shelter management.

Abstract

Overpopulation in animal shelters contributes to increased disease spread and higher expenses on animal healthcare, leading to fewer adoptions and more shelter deaths. Additionally, one of the greatest challenges that shelters face is the noise level in the dog kennel area, which is physically and physiologically hazardous for both animals and staff. This paper proposes a multi-criteria optimization framework to automatically design cage layouts that maximize shelter capacity, minimize tension in the dog kennel area by reducing the number of cages facing each other, and ensure accessibility for staff and visitors. The proposed framework uses a Genetic Algorithm (GA) to systematically generate and improve layouts. A novel graph theory-based algorithm is introduced to process solutions and calculate fitness values. Additionally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank and sort the layouts in each iteration. The graph-based algorithm calculates variables such as cage accessibility and shortest paths to access points. Furthermore, a heuristic algorithm is developed to calculate layout scores based on the number of cages facing each other. This framework provides animal shelter management with a flexible decision-support system that allows for different strategies by assigning various weights to the TOPSIS criteria. Results from cats' and dogs' kennel areas show that the proposed framework can suggest optimal layouts that respect different priorities within acceptable runtimes.

Automated Optimal Layout Generator for Animal Shelters: A framework based on Genetic Algorithm, TOPSIS and Graph Theory

TL;DR

This work tackles automated cage-layout design for animal shelters by integrating a Genetic Algorithm, a graph-based accessibility analysis, and TOPSIS for multi-criteria ranking. The framework optimizes four criteria—AC, LSP, ASP, and CF—to balance shelter capacity, ease of access, and noise reduction, while allowing management to weight criteria according to priorities. A graph-theoretic distance and a CF metric quantify welfare-relevant factors, and TOPSIS ranks layouts within GA iterations to drive Pareto-like trade-offs. Demonstrations on cat and dog kennels show feasible runtimes and practical layouts that adapt to varying priorities, underscoring the framework's value as a decision-support tool for shelter management.

Abstract

Overpopulation in animal shelters contributes to increased disease spread and higher expenses on animal healthcare, leading to fewer adoptions and more shelter deaths. Additionally, one of the greatest challenges that shelters face is the noise level in the dog kennel area, which is physically and physiologically hazardous for both animals and staff. This paper proposes a multi-criteria optimization framework to automatically design cage layouts that maximize shelter capacity, minimize tension in the dog kennel area by reducing the number of cages facing each other, and ensure accessibility for staff and visitors. The proposed framework uses a Genetic Algorithm (GA) to systematically generate and improve layouts. A novel graph theory-based algorithm is introduced to process solutions and calculate fitness values. Additionally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank and sort the layouts in each iteration. The graph-based algorithm calculates variables such as cage accessibility and shortest paths to access points. Furthermore, a heuristic algorithm is developed to calculate layout scores based on the number of cages facing each other. This framework provides animal shelter management with a flexible decision-support system that allows for different strategies by assigning various weights to the TOPSIS criteria. Results from cats' and dogs' kennel areas show that the proposed framework can suggest optimal layouts that respect different priorities within acceptable runtimes.
Paper Structure (15 sections, 6 equations, 12 figures, 5 tables)

This paper contains 15 sections, 6 equations, 12 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Computer-Aided Layout Design
  3. Problems in Animal Shelters
  4. Objectives of This Study
  5. The Proposed Framework and Materials
  6. TOPSIS Algorithm
  7. Genetic Algorithm (GA)
  8. Creating the Initial Population
  9. Placement Strategies
  10. Crossover and Mutation Operators
  11. Fitness Operator
  12. Results and Discussion
  13. Specifications of the Case
  14. Optimization Results
  15. Conclusion and Future Opportunities

Figures (12)

  • Figure 1: The steps of the proposed framework.
  • Figure 2: Two initial chromosomes for a shelter with 20 cages.
  • Figure 3: The corresponding layouts of the two initial chromosomes.
  • Figure 4: The crossover operation to create chromosomes AB and BA from parents A and B.
  • Figure 5: The mutation operation to create chromosomes AA from parent A.
  • ...and 7 more figures