Contrasting stuctured and random permutation flow-shop scheduling problems: Search-space topology and algorithm performance

The use of random test problems to evaluate algorithm performance raises an important, and generally unanswered, question: Are the results generalizable to more realistic problems? Researchers generally assume that algorithms with superior performance on difficult, random test problems will also perform well on more realistic, structured problems. Our research explores this assumption for the permutation flow-shop scheduling problem. We introduce a method for generating structured flow-shop problems, which are modeled after features found in some real-world manufacturing environments. We perform experiments that indicate significant differences exist between the search-space topologies of random and structured flow-shop problems, and demonstrate that these differences can affect the performance of certain algorithms. Yet despite these differences, and in contrast to difficult random problems, the majority of structured flow-shop problems were easily solved to optimality by most algorithms. For the problems not optimality solved, differences in performance were minor. We conlude that more realistic, structured permutation flow-shop problems are actually relatively easy to solve. Our results also raise doubts as to whether superior performance on difficult random scheduling problems translates into superior performance on more realistic kinds of scheduling problems.