Abstract:
This research deals with the application of Ant Colony Optimization (ACO) approach to solve no-wait flowshop scheduling problem (NW-FSSP). ACO algorithm so developed has been coded on Matlab and Visual basic computer applications. The paper covers detailed steps to apply ACO and focuses on judging the strength of ACO in relation to other solution techniques previously applied to solve a standard NW-FSSP. The general purpose approach yielded reasonably accurate results for almost all the problems under consideration and was able to handle a fairly large spectrum of problems with far reduced CPU effort. Careful scrutiny of the results reveals that the algorithm presented results better than other approaches like Genetic algorithm and Tabu Search heuristics etc; earlier applied to solve NW-FSSP data sets.
"The purpose of this research is to introduce a set of metaheuristics which have proven to be effective in managing resources by intelligently scheduling the jobs. Main emphasis during the research has been kept on a state of the art optimization technique i.e. Ant Colony Optimization (ACO) as this approach has drawn significant attention of the researchers all around the globe during last ten years due its much better performance as compared to other conventional metaheuristics. The proposed approach conveniently achieved solutions better than those presented by the previous metaheuristics for almost all the problems under consideration. Its performance appreciably improved with the problem size. Hence it is particularly suited for solving fairly large sized data sets i.e. around 75x20 or more; with a reasonable accuracy level while consuming very less amounts of time for completing calculation cycles.
