This study contributes to the hybrid flow shop due to a lack of consideration of characteristics existing in real-world problems. Prior studies are neglecting identical machines, explicit and sequence-dependent setup times, and machine blocking. We propose a hybrid genetic algorithm to solve the problem. Furthermore, we also propose a mixed-integer linear programming formulation. We note a predominance of the mathematical model for small instances, with five jobs and three machines because of how fast there is convergence. The objective function adopted is to minimize the makespan, and relative deviation is used as a performance criterion. Our proposal incorporates two metaheuristics in this process: a genetic algorithm to generate sequences (the flow shop subproblem) and a GRASP to allocate the jobs in the machines (the parallel machines subproblem). The extensive computational experience carried out shows that the proposed hybrid genetic algorithm is a promising procedure to solve large-sized instances.

In future electricity industry transferring high quality of power is essential. In this case, using Flexible AC Transmission System (FACTS) devices is inevitable. FACTS devices are used for controlling the voltage, stability, power flow and security of transmission lines. Therefore, finding the optimal locations for these devices in power networks is necessary. There are several varieties of FACTS devices with different characteristics, deployed for different purposes. Imperialist Competitive (IC) algorithm is a recently developed optimization technique, applied in power systems. IC algorithm is a new heuristic approach for global optimization searches based on the concept of imperialistic competition. In this paper, an IEEE 4-bus system is deployed as a case study in order to demonstrate the results of this novel approach using MATLAB.

Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various meta-heuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF) problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area.