TY - JOUR
T1 - Mixed-integer programming models for simultaneous batching and scheduling in multipurpose batch plants
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 . H. Lee would like to acknowledge support from University of Wisconsin – Wisconsin Distinguished Graduate Fellowship, as well as the Kwanjeong Educational Foundation, South Korea. Appendix A
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/11/2
Y1 - 2017/11/2
N2 - We propose two novel discrete-time mixed-integer programming models for simultaneous batching and scheduling in multipurpose batch plants with storage constraints. The proposed models adopt two different modeling approaches. The first is based on explicit labeling of batches, while the second is based on identifying possible batch size intervals for each order and the corresponding unit routings. We also present extensions that allow us to consider limited shared utilities (with both fixed and time-varying availability and cost), storage with capacity limits and stage-dependent batch sizes. Finally, we study how instance characteristics (e.g. expected number of batches per order, uniformity in unit capacities) impact the effectiveness of the proposed models. We show that by carefully selecting the model allows us to effectively solve large-scale instances.
AB - We propose two novel discrete-time mixed-integer programming models for simultaneous batching and scheduling in multipurpose batch plants with storage constraints. The proposed models adopt two different modeling approaches. The first is based on explicit labeling of batches, while the second is based on identifying possible batch size intervals for each order and the corresponding unit routings. We also present extensions that allow us to consider limited shared utilities (with both fixed and time-varying availability and cost), storage with capacity limits and stage-dependent batch sizes. Finally, we study how instance characteristics (e.g. expected number of batches per order, uniformity in unit capacities) impact the effectiveness of the proposed models. We show that by carefully selecting the model allows us to effectively solve large-scale instances.
KW - Sequential production environment
KW - Storage policies
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U2 - 10.1016/j.compchemeng.2017.07.007
DO - 10.1016/j.compchemeng.2017.07.007
M3 - Article
AN - SCOPUS:85032919334
SN - 0098-1354
VL - 106
SP - 621
EP - 644
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
ER -