We apply metaheuristics to NP-hard optimization problems when we have large data sets. Often, these occur in multi-objective settings. Metaheuristics from my research include genetic algorithms, tabu search, and particle swarm optimization. These metaheuristics have been applied to a wide variety of research topics.
We apply techniques to solve assembly and transfer line balancing and sequencing. Problem specifications include parallel workstations, mixed model assembly lines, two-sided assembly lines, zoning constraints, and ergonomic constraints.
We apply heuristics and metaheuristics to flexible flowline scheduling problems. The data used to solve flexible flowline scheduling problems came from a given repository.
We solve machine scheduling problems using different methods. Problem objectives include minimizing makespan, the number of tardy jobs, and total weighted tardiness as well as multi-objective scheduling. Problem specifications include parallel machines, batch-processing and sequence dependent setups.