Comparison of Scheduling Algorithms for a Multi-Product Batch-Chemical Plant with a Generalized Serial Network

Despite recent advances in computer power and the development of better algorithms, theoretical scheduling methodologies developed for batch-chemical production are seldom applied in industry (Musier & Evans 1989 and Grossmann et al. 1992). Scheduling decisions may have significant impact on overall company profitability by defining how capital is utilized, the operating costs required, and the ability to meet due dates. The purpose of this research is to compare different production scheduling methods by applying them to a real-world multi-stage, multi-product, batch-chemical production line. This research addresses the problem that the theoretical algorithms are seldom applied in industry and allows for performance analysis of several theoretical algorithms.

The research presented in this thesis focuses on the development and comparison of several scheduling algorithms. The two objectives of this research are to:

1. modify different heuristic production scheduling algorithms to minimize tardiness for a multi-product batch plant involving multiple processing stages with several out-of-phase parallel machines in each stage; and
2. compare the robustness and performance of these production schedules using a stochastic discrete event simulation of a real-world production line.

The following three scheduling algorithms are compared:

1. a modified Musier and Evans scheduling algorithm (1989);
2. a modified Ku and Karimi Sequence Building Algorithm (1991); and
3. a greedy heuristic based on an earliest-due-date (EDD) policy.

Musier and Evans' heuristic improvement method (1989) is applied to the three algorithms. The computation times to determine the total tardiness of each schedule are compared. Finally, all the schedules are tested for robustness and performance in a stochastic setting with the use of a discrete event simulation (DES) model. Mignon, Honkomp, and Reklaitis' evaluation techniques (1995) and Multiple Comparison of the Best are used to help determine the best algorithm.