Heuristic solution methods for the stochastic flow shop problem
We investigate the stochastic flow shop problem with m machines and general distributions for processing times. No analytic method exists for solving this problem, so we looked instead at heuristic methods. We devised three constructive procedures with modest computational requirements, each based on approaches that have been successful at solving the deterministic counterpart. We compared the performance of these procedures experimentally on a set of test problems and found that all of them achieve near-optimal performance.
flow shop stochastic scheduling makespan lognormal distribution