@inbook{914d823fb9784cfeb99b9666f158256f,
title = "Adjustable Robust Optimization for Scheduling of Batch Processes under Uncertainty",
abstract = "In this work, we hedge against the uncertainty in the of batch process scheduling by using a novel two-stage adjustable robust optimization (ARO) approach. We introduce symmetric uncertainty sets into the deterministic mixed-integer linear programming (MILP) model for batch scheduling problem and then reformulate it into a two-stage problem. The budgets of uncertainty is used to adjust the degree of conservatism. Since the resulting two-stage ARO problem cannot be solved directly by any existing optimizer, the column-and-constraint generation (C&CG) algorithm is then applied to solve it efficiently. One case study for batch manufacturing processes is considered to demonstrate the validation of the two-stage ARO model formulation and the efficiency of the C&CG algorithm.",
keywords = "batch processes, column-and-constraint generation algorithm, scheduling, two-stage adaptive robust optimization",
author = "Hanyu Shi and Fengqi You",
year = "2016",
month = jan,
day = "1",
doi = "10.1016/B978-0-444-63428-3.50096-5",
language = "English (US)",
isbn = "9780444634283",
volume = "38",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier B.V.",
pages = "547--552",
booktitle = "26 European Symposium on Computer Aided Process Engineering, 2016",
}