Date of Graduation
Statler College of Engineering and Mineral Resources
Industrial and Managements Systems Engineering
In the pharmaceutical manufacturing world, a deadline could be the difference between losing a multimillion-dollar contract or extending it. This, among many other reasons, is why good scheduling methods are vital. This problem report addresses Flexible Flowshop (FF) scheduling using Simulated Annealing (SA) in conjunction with the Steepest Descent heuristic (SD).
FF is a generalized version of the flowshop problem, where each product goes through S number of stages, where each stage has M number of machines. As opposed to a normal flowshop problem, all ‘jobs’ do not have to flow in the same sequence from stage to stage. The SA metaheuristic is a global optimization method for solving hard combinatorial optimization problems. SD is a local search method that keeps track only of the current solution and moves only to neighboring permutations based on the largest decrease in the objective function value. The goal of this problem report is to use FF in conjunction with SA to minimize the makespan (length of schedule) in a pharmaceutical manufacturing environment. There are 4 total stages in the tentative production route: granulation, compression, coating, and packaging. This process will be uniform; as in, each stage will have the same number of identical machines.
In this study, SA solved the illustrative small-scale example problems precisely and efficiently using a very small amount of computation time. Afterward, the SD heuristic is used to ensure that the best solution found by SA is a local optimum. SD did not improve upon the solutions found by SA.
Spencer, Bryant Jamison, "PHARMACEUTICAL SCHEDULING USING SIMULATED ANNEALING AND STEEPEST DESCENT METHOD" (2019). Graduate Theses, Dissertations, and Problem Reports. 3796.