Geodesy

Flow shop scheduling

Routing (electronic design automation)

Metaheuristic

Algorithm

Benchmark (surveying)

Artificial bee colony algorithm

Ant colony optimization algorithms

Embedded system

Crossover

Scheduling (production processes)

Operating system

Computer science

Mathematical optimization

Mathematics

Genetic algorithm

Geography

Ant colony

Artificial intelligence

Schedule

Machine learning

Job shop scheduling

Applied Soft Computing

This paper proposes an effective new hybrid ant colony algorithm based on crossover and mutation mechanism for no-wait flow shop scheduling with the criterion to minimize the maximum completion time. The no-wait flow shop is known as a typical NP-hard combinational optimization problem. The hybrid ant colony algorithm is applied to the 192 benchmark instances from literature in order to minimize makespan. The performance of the proposed Hybrid Ant Colony algorithm is compared to the Adaptive Learning Approach and Genetic Heuristic algorithm which are used in previous studies to solve the same set of benchmark problems. The computational experiments show that the proposed Hybrid Ant Colony algorithm provides better results relative to the other algorithms.

A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems