Broiler management using fuzzy multi-objective genetic algorithm: A case study

•The paper surveys Time Cost Quality Trade-off Problems in broiler production project.•We manage risk in broiler production using by fuzzy α cut.•We use multi-objective genetic algorithm (NSGA-II) for optimization.•The emphasis is on the definition of project activities quality.•We define weights for time, cost and quality in objective function. In recent years, broiler meat consumption and trade have undergone considerable growth. Due to this reason, particular attention has been paid to the management of its production process with the aim of reaching a high quality, short process time and low-cost production. This study attempts to use fuzzy logic mathematics as part of a decision support system for managing time, cost and quality in broiler production. For having an effective management system, process uncertainties have been taken into account This approach considers the process as an interval with fuzzy numbers and, for managing the risks, it uses the variable α, a parameter determined by the manager in an interval between 0 and 1. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) with fuzzy input has been used to optimize the entire broiler production. Due to a large number of activities and logical options available, this process has more possible solutions. To achieve an optimal unique solution, an objective function has been used and, weights for time, cost and quality have been assigned. Based on the results, in conditions of uncertainty (Fuzzy α Cut = 0), the amount of time, cost and quality, have been calculated respectively as 1792.836 h, 260573.04 $ and 49.6%, while, in conditions of certainty (Fuzzy α Cut = 1), they have been computed as 1789.194 h, 260392.34 $ and 52.85%.