A Dynamic Approach to Selecting Time Points for Short-Term Scheduling with Application to Multipurpose Facilities

Abstract

Choosing an efficient time representation is an important consideration when solving short-term scheduling problems. Improving the efficiency of scheduling operations may lead to increased yield, or reduced makespan, resulting in greater profits or customer satisfaction. When formulating these problems, one must choose a time representation over which to execute to scheduling operations. We propose in this study an iterative framework to refine an initial coarse discretization, by adding key time points that may be beneficial. This framework is compared against existing static discretizations using computational experiments on a scientific services facility. Using case studies from other applications in chemical engineering, we compare the performance of our framework against a previously reported time-discretization approach in the literature. The results of these experiments demonstrate that when problems are sufficiently large, our proposed dynamic method is able to achieve a better tradeoff between objective value and CPU time than the currently used discretizations in the literature.

Integration of Planning and Scheduling for Large-Scale Multijob Multitasking Batch Plants

“…The goal is to plan efficiently and generate schedules for the plant, which dictates what samples to assign to which processes over the length of time that is to be scheduled such that an objective is optimized while abiding by the operational constraints of the facility. The studies available in the literature for such multijob plants are limited to developing scheduling models. − Exploring the scope of integrating planning and scheduling decisions for such plants remains an open and challenging problem.…”