Contrasting Structured 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 diÆcult, random test problems will also perform well on more realistic, structured problems. Our research explores this assumption for the permutation ow-shop scheduling problem. We introduce a method for generating structured ow-shop problems, which are modeled after features found in some real-world manufacturing environments. We perform experiments that indicate signi cant di erences exist between the search-space topologies of random and structured ow-shop problems, and demonstrate that these di erences can a ect the performance of certain algorithms. Yet despite these di erences, and in contrast to diÆcult random problems, the majority of structured ow-shop problems were easily solved to optimality by most algorithms. For the problems not optimally solved, di erences in performance were minor. We conclude that more realistic, structured permutation ow-shop problems are actually relatively easy to solve. Our results also raise doubts as to whether superior performance on diÆcult random scheduling problems translates into superior performance on more realistic kinds of scheduling problems. (Flow Shop; Sequencing; Local Search; Problem DiÆculty; Fitness Landscapes)