  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.
