TY - JOUR
T1 - Combining the advantages of discrete- and continuous-time scheduling models
T2 - Part 1. Framework and mathematical formulations
AU - Lee, Hojae
AU - Maravelias, Christos T.
N1 - Funding Information:
The authors acknowledge financial support from the National Science Foundation under grants CMMI-1334933 and CBET-1264096 , and H. Lee would like to acknowledge support from University of Wisconsin – Wisconsin Distinguished Graduate Fellowship , as well as the Kwanjeong Educational Foundation, South Korea .
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/8/4
Y1 - 2018/8/4
N2 - We propose a general method for the solution of chemical production scheduling problems in network environments. The method consists of three stages. In the first stage, a discrete-time mixed-integer programming (MIP) model is solved to quickly obtain an approximate solution. In the second stage, the solution is mapped onto newly introduced unit- and material-specific continuous-time grids, using a mapping algorithm. In the third stage, a continuous-time linear programming (LP) model is solved to improve the accuracy of the mapped discrete-time solution by refining the timing of events and batch sizes. The proposed method takes advantage of the complementary strengths of discrete- and continuous-time formulations, which enables us to not only handle various processing features (e.g., intermediate deliveries and orders, time-varying resource availability and cost, variable processing times), but also obtain order of magnitude speedups in the solution of large-scale instances.
AB - We propose a general method for the solution of chemical production scheduling problems in network environments. The method consists of three stages. In the first stage, a discrete-time mixed-integer programming (MIP) model is solved to quickly obtain an approximate solution. In the second stage, the solution is mapped onto newly introduced unit- and material-specific continuous-time grids, using a mapping algorithm. In the third stage, a continuous-time linear programming (LP) model is solved to improve the accuracy of the mapped discrete-time solution by refining the timing of events and batch sizes. The proposed method takes advantage of the complementary strengths of discrete- and continuous-time formulations, which enables us to not only handle various processing features (e.g., intermediate deliveries and orders, time-varying resource availability and cost, variable processing times), but also obtain order of magnitude speedups in the solution of large-scale instances.
KW - Discrete- and continuous-time representation
KW - Network environment
KW - Solution refinement method
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U2 - 10.1016/j.compchemeng.2017.12.003
DO - 10.1016/j.compchemeng.2017.12.003
M3 - Article
AN - SCOPUS:85040627255
SN - 0098-1354
VL - 116
SP - 176
EP - 190
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
ER -