a knowledge-based nsga-ii approach for scheduling in virtual manufacturing cells

this paper considers the job scheduling problem in virtual manufacturing cells (vmcs) with the goal of minimizing two objectives namely, makespan and total travelling distance. to solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (nsga-ii) and knowledge-based non-dominated sorting genetic algorithm (kbnsga-ii). the difference between these algorithms is that, kbnsga-ii has an additional learning module. finally, we draw an analogy between the results obtained from algorithms applied to various test problems. the superiority of our kbnsga-ii, based on set coverage and mean ideal distance metrics, is inferred from results.