An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint

Since there are some special constraints in a real foundry enterprise, including the limitation of starting time in some casting operations and the transportation time between two adjacent operations, processing interval constraint (PIC) and job transportation time (TIT) are introduced in this paper. With the consideration of PIC and JTT, a multi-objective casting production scheduling model is constructed to minimize makespan, the total production cost and the total delivery delay time. A hybrid discrete multi-objective grey wolf optimizer (HDMGWO) is developed to solve this model. An initialization strategy based on reducing job transportation time and processing time (RTP) are designed to improve the quality of initial population. A improved tabu search (ITS) algorithm is embedded into grey wolf optimizer (GWO) to overcome the premature convergence of the GWO. A modified search operator of GWO is designed to tackle discrete combinatorial optimization. A case example of the real foundry enterprise is illustrated to evaluate the effectiveness of proposed HDMGWO. Experimental results demonstrate that the proposed HDMGWO is superior in terms of the quality of solutions compared to five multi-objective algorithms. Real running in a casting ERP system verifies the applicability of the proposed scheduling model and the HDMGWO.