Development of a hybrid metaheuristic to minimise earliness and tardiness in a hybrid flowshop with sequence-dependent setup times

This paper presents an efficient hybrid metaheuristics for scheduling jobs in a hybrid flowshop with sequence-dependent setup times. The problem is to determine a schedule that minimises the sum of earliness and tardiness of jobs. Since this problem class is NP-hard in the strong sense, there seems to be no escape from appealing to metaheuristic procedures to achieve near-optimal solutions for real life problems. This paper proposes the hybrid metaheuristic algorithm which comprises three components: an initial population generation method based on an ant colony optimisation, a simulated annealing algorithm as an evolutionary algorithm that employs certain probability to avoid becoming trapped in a local optimum, and a variable neighbourhood search which involves three local search procedures to improve the population. A design of experiments approach is employed to calibrate the parameters of the algorithm. Results of computational tests in solving 252 problems up to 100 jobs have shown that the proposed algorithm is computationally more effective in yielding solutions of better quality than the adapted random key genetic algorithm and immune algorithm presented previously.