Scheduling flexible flow lines with sequence dependent setup times
AuthorKurz, Mary Elizabeth
AdvisorAskin, Ronald G.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThis dissertation examines scheduling in flexible flow lines with sequence-dependent setup times to minimize makespan. This type of manufacturing environment is found in industries such as printed circuit board and automobile manufacture. Lower makespans can be associated with more efficient use of resources. Poor scheduling when sequence-dependent setup times exist can negatively impact productivity. As a building block, minimizing makespan in parallel identical machines with sequence-dependent setup times is examined. Several heuristics are compared empirically using statistical analysis. Experimental results indicate that a heuristic based on the Insertion Heuristic for the Travelling Salesman Problem is effective. Subsequently, minimizing makespan in flexible flow lines with sequence-dependent setup times is considered. An integer program that incorporates all aspects of the problem is formulated. Due to the NP-hard nature of the problem, heuristic methods are considered. The heuristics, based on greedy methods, flow line methods, the Insertion Heuristic for the Travelling Salesman Problem and genetic algorithms are compared empirically using statistical analysis. The heuristics are designed to take advantage of the flow line nature of the problem, the parallel machine nature and the combinatorial features of the problem. Problem data is generated in order to evaluate the heuristics. The characteristics are chosen to reflect those used by previous researchers. An effective lower bound is created in order to evaluate the heuristics. A random keys genetic algorithm is found to be very effective for the problems eyed. In addition, several extensions based on backwards pass of the schedule and focusing on the bottleneck stage are proposed and examined. These proved to be ineffective approaches but yielded insight regarding what features of schedule are important. Most significantly, the first stage is very important in determining the quality of the subsequent schedule. The heuristics considered here focus on setting a schedule for one stage and then considering the next. Areas for future research include developing methods of scheduling job-by-job and examination of branch and bound methods to find optimal solutions, aided by effective lower bounds and theorems regarding schedule domination.
Degree ProgramGraduate College
Systems and Industrial Engineering