In this paper, a hybrid Random Forest-Genetic Algorithm approach to detect and solve bottleneck machine problems in parallel machine Job-shop scheduling is developed with the aim of minimizing the makespan and the additional cost. The drawbacks of the existing methods for diagnosing bottlenecks is that they either do not consider the severity of the bottleneck or they do not consider the existence of multiple bottlenecks. In the existing models for solving bottlenecks, the cost is not considered as an objective function and only shifting of bottlenecks is utilized to solve the bottleneck machine problem. This approach is not feasible if the maximum capacity of the workshop has been reached. In this paper, a Random Forest classification model is utilized to diagnose bottleneck machine with different severity where the severity of the machines on the shop floor can either be none, low, medium, or high. Due to the lack of historical data, the Random Forest algorithm is trained using bottleneck data generated by simulating several identical parallel machine Job-shop scheduling problems. The trained Random Forest algorithm is then used in conjunction with Genetic Algorithm for finding the optimal actions to be taken for the most severe bottlenecks machines in order to reduce the makespan and the additional cost by optimizing the number of additional parallel machines to be utilized and overtime hours for the most severe bottleneck machines. The two objectives, makespan and additional cost, are combined into a single objective value by the use of weight values. These weight values depend on severity of the most severe bottleneck machine. If the bottleneck severity is “high” then makespan has a higher weight value than cost, if the severity is “medium” then both cost and makespan are weighed equally, and if the severity is “low” then cost has a higher weight value than makespan. In order to show the validity of the proposed approach it is used for diagnosing and solving the bottleneck problems in three different identical parallel machine Job-shop scheduling case studies 1. 3 jobs with 6 machines 2. 5 jobs with 9 machines and 3. 5 jobs with 12 machines. By utilizing the proposed approach the makespan and cost were reduced by 19.0%, 24.5% and 25.4% in case studies 1, 2, and 3 respectively. The results show that the trained Random Forest algorithm was able to correctly diagnose the bottleneck machines and their severity and Genetic algorithm was able to find the optimal number of additional hours and additional machines for the most severe bottleneck machines on the shop floor.

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