Hybrid Algorithm Based on an Estimation of Distribution Algorithm and Cuckoo Search for the No Idle Permutation Flow Shop Scheduling Problem with the Total Tardiness Criterion Minimization

The no idle permutation flow shop scheduling problem (NIPFSP) is a popular NP-hard combinatorial optimization problem, which exists in several real world production processes. This study proposes a novel hybrid estimation of the distribution algorithm and cuckoo search (CS) algorithm (HEDACS) to solve the NIPFSP with the total tardiness criterion minimization. The problem model is built on the basis of the starting and ending time point of each job. A discrete solution representation method is applied in HEDACS to increase the operation efficiency. A novel probability matrix build method is also designed within the knowledge of the processing time matrix. The partially-mapped crossover operation works effectively during the CS phase. A suitable knowledge-based local search is also designed in the HEDACS to balance the exploitation and exploration. Finally, many simulations based on the new hard Ruiz benchmarks are conducted. Computational results demonstrate the effectiveness of the proposed HEDACS.