The efficient design of public transportation networks is critical in establishing opti- mal schedules and curtailing transport costs for both passengers and transportation organizations. This study focuses on advancing existing models for selecting timeta- bles and optimal schedules within public transportation networks. Addressing these concerns as a bi-objective optimization problem, this paper aims to develop an effec- tive method for simultaneous optimization, targeting the reduction of transportation costs and minimizing passengers’ waiting times. Initially, this study formulates the problem using appropriate mixed-integer linear programming. To tackle this chal- lenging optimization problem, various algorithms, including the non-dominated sorting genetic algorithm (NSGA), bi-objective particle swarm optimization algo- rithm (bi-objective PSO), and bi-objective red deer algorithm (bi-objective RDA), are employed. The selection of these algorithms aims to explore different solution spaces and their abilities to produce Pareto-optimal solutions. To assess the effec- tiveness of these Pareto algorithms in addressing the problem, the epsilon constraint method is utilized. Additionally, a redesign method is introduced to confront optimi- zation challenges in this specific research domain. Leveraging bi-objective problem estimator parameters and the response level method, the algorithms’ parameters are optimized, elucidating the best-case scenarios for each parameter. A comprehensive comparative analysis of algorithm performance is conducted, considering various criteria, including solution time, convergence, and diversity of solutions. Further- more, sensitivity analyses are carried out on problem-sensitive parameters in a case study, culminating in significant managerial implications for addressing the pro- posed problem in real-world scenarios.

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