Comparative Analysis of Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem

This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric

Swarm intelligence for scheduling: a review

Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduces some of the theoretical foundations, the biological motivation and fundamental aspects of swarm intelligence based optimization techniques such Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bees Colony (ABC) algorithms for scheduling optimization