Bi-objective short-term scheduling in a rolling horizon framework: a priori approaches with alternative operational objectives

Lee, Doyeon; Fukasawa, Ricardo; Ricardez‐Sandoval, Luis A.

doi:10.1016/j.cor.2019.06.006

Cited by 4 publications

(1 citation statement)

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“…The model ( P ) has been constructed to easily accommodate some of the features of the particular application that we study. However, it is similar to other general process network scheduling models proposed such as in refs and , and was used to model a semiconductor processing plant in ref . In particular, the method for generating time grids for our model presented in section may be tailored slightly to fit the general batch scheduling model presented in ref .…”

Section: Introductionmentioning

confidence: 99%

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

Stevenson

Fukasawa

Ricardez‐Sandoval

2020

Ind. Eng. Chem. Res.

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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.

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Section: Introductionmentioning

confidence: 99%