O processo de alocação de empresas candidatas em estandes comercializados pelas feiras agrícolas - Gutierrez, Ronier Pauletto (2025)
Agricultural fairs are important events for the agricultural sector, being an opportune space for the commercialization of products and services, as well as providing an environment for the exchange of knowledge and the display of innovations related to rural production and management. With the purpose of making the fairs more financially self-sustainable, the objective of the research was to maximize the profit with the allocation of candidate companies in booths commercialized by the agricultural fairs. First, a literature review in the Scopus indexing database on the management methodologies to be used in agricultural fairs was carried out, with the aim of understanding the fairs in terms of organizations from the perspective of the managers. In addition, an article and a thesis based on the commercialization of spaces in areas other than the rural sector were added, with the purpose of verifying the variables and methods used that can be reused in the context of agricultural fairs. Based on the information obtained, a mathematical model was developed together with an individual scoring system for the candidate companies, aimed at maximizing points with the allocation in the booths. The scenario defined was an expo fair, with a covered pavilion containing 52 stands, and an outdoor area with four stands. Next, a Python algorithm was developed to generate the values related to the stands and the parameters of the candidate companies. After generating the instance for the scenario, the optimal solution obtained is presented, as well as a descriptive analysis of the allocation of the candidate companies in the stands and a comparison with the traditional format adopted by expo fairs. Aiming to expand the testing of the mathematical model, 22 instances were generated, where the values related to the company/stand ratio, number of stands, number of candidate companies, the score of the parameters, type and relationship are presented. From the grouping of the instance data, a descriptive analysis and a Pearson linear correlation analysis were performed in order to verify differences and similarities of the determining instances to find optimal solutions. An average increase of 50.45% in the profit from selling stands through the mathematical model compared to the traditional format was indicated. In addition, the usability of the mathematical model depends on multiple parameters to find the optimal solution for an instance, and consequently the allocation of candidate companies in the stands.