Multi-Objective Green Shop Scheduling Optimization Based on Improved Grey Wolf Algorithm

In this paper, a multi-objective optimization model with the objectives of shop floor completion time, machine load and total energy consumption is developed to address the inefficiency of solving the multi-objective flexible job shop scheduling problem. A flexible job shop scheduling method is designed by combining the gray wolf algorithm and the non-dominated ranking genetic algorithm. Complex numerical coding increases population diversity, and the gray wolf method uses non-dominated sorting and crowding degree notions in population updates to make it more suitable to multi-objective issues. The algorithm’s ability to perform global searches is improved by using random search coefficients. Testing using benchmark arithmetic instances reveals the modified gray wolf algorithm's effectiveness. The algorithm can optimize the multi-objective workshop problem and discover the best solution with fewer iterations.