In recent decades, the production speed and efficiency of open shop scheduling problems (OSSP) have increased by considering identical machines for each stage. In addition to the time of the operation, the time of transporting the jobs between the stages is considered significantly. In contrast, the transportation time could be more attention. The nature of machine operations determines the transportation durations between tasks and is independent of distance, therefore eliminating any waiting time for shop activities during transfers. A mixed-integer linear programming approach is proposed to decrease the maximum completion of work time. A novel whale optimization algorithm (WOA) for OSSP is introduced to re-solve extensive OSSP issues within a reasonable computational timeframe. The novelties are based on several factors in the optimal solutions in all small and medium-sized problem in-stances. For solving problem sizes, we consider several metaheuristic algorithms, including particle swarm optimisation (PSO) and differential evolution (DE) methods, for all random large-sized problem instances. The algorithm for OSSP produced better results than PSO, DE, and bucket elimination regarding the spacing, quality, and diversity parameters. Integrating transportation time considerations into OSSP through the WOA algorithm aimed to achieve more effective and practical scheduling solutions in dynamic and complex conditions. The manufacturing industries can benefit from the proposed WOA for OSSP to enhance schedul-ing practices and improve operational efficiency and cost-effectiveness.