S. Deva Prasad

Multi-objective Flowshop Scheduling: a Genetic Algorithmic Approach S. Deva Prasad

This work attempts to develop an algorithm in the area of multiobjective genetic algorithms by analyzing flowshop and kanban-controlled flowshop scheduling problems. The objectives of this research effort are two-fold: first, the development of the proposed Non-Dominated and Normalized-Distance-ranked-Sorting based Multi-objective Genetic Algorithm (NDSMGA) to generate non-dominated solutions; second, a detailed investigation of the proposed algorithm by considering the conventional flowshop scheduling problem and the kanban-controlled flowshop scheduling problem. The generation of a non-dominated solution set is quite useful to any decision maker. Given the set of non-dominated sequences, the decision maker can choose the sequence that satisfies his/her preference vector with respect to the objectives. It is noteworthy that the NDSMGA can handle any number of objectives due to the non-dominated sorting, and the computation of distance metric can be extended to address any number of objectives.