Meta-heuristic approaches for flowshop scheduling problems: a review. (2016)

• Record Type: Journal Article
• Authors: Arora, Deepanshu; Agarwal, Gopal
• Is Part Of: International journal of advanced operations management. Volume 8:Number 1(2016)
• Page Start: 1
• Page End: 16
• Publication Date: 2016
• Subjects: metaheuristics -- genetic algorithms -- particle swarm optimisation -- PSO -- simulated annealing -- ant colony optimisation -- ACO -- artificial neural networks -- ANNs -- tabu search -- flowshop scheduling; Business logistics -- Periodicals; Industrial management -- Periodicals; 658.5005
• Languages: English
• ISSNs: 1758-938X

Abstract

The right approach towards the optimisation of multi-objective nonlinear problem is the incremental adoption of available techniques and strategies that help to minimise the solution time and to improve the optimised results. Meta-heuristics are heuristic methods that help the search through the solution space using algorithms. Meta-heuristics further improve the solution that comes initially from heuristics. This paper presents various types of meta-heuristics approaches for optimising flowshop scheduling (FSS) with focus on minimising parameters like total tardiness, total earliness, maximum makespan, total no. of tardy jobs, etc. Since the problem is NP-hard, the meta-heuristics and hybrid meta-heuristics are most appropriate methods to solve this kind of problem. Meta-heuristics are projected as strategic means to achieve the optimum and efficient schedule, with positive impact on cost minimisation (economies of scale) and simultaneously reducing solution time.