TY - JOUR
T1 - Short-term production scheduling with non-triangular sequence-dependent setup times and shifting production bottlenecks
AU - Berkhout, J.
AU - Pauwels, E.
AU - van der Mei, R.
AU - Stolze, J.
AU - Broersen, S.
PY - 2021
Y1 - 2021
N2 - A novel mathematical model is introduced that allows solving real-life scheduling problems in complex multi-stage machine environment with (i) non-triangular sequence-dependent setup times and (ii) shifting production bottlenecks, both of which are important aspects appearing in varying manufacturing industries. The primary goal is to minimise the tardiness of customer orders, which may consist of multiple production orders each in turn composed of several batches. A secondary objective is to maximise the production capacity utilization as measured by the makespan. The model is elaborated for general animal-feed plants which have to deal with the particular production scheduling problem on a daily basis. Dispatching rules are introduced to enhance the optimization progress. Numerical experiments show that optimising the model leads to schedules that meet the due dates. Moreover, by reducing the mean idle time of production lines with 35.6%, the optimization leads to a makespan reduction of 6.5% on average compared to real-life applied schedules.
AB - A novel mathematical model is introduced that allows solving real-life scheduling problems in complex multi-stage machine environment with (i) non-triangular sequence-dependent setup times and (ii) shifting production bottlenecks, both of which are important aspects appearing in varying manufacturing industries. The primary goal is to minimise the tardiness of customer orders, which may consist of multiple production orders each in turn composed of several batches. A secondary objective is to maximise the production capacity utilization as measured by the makespan. The model is elaborated for general animal-feed plants which have to deal with the particular production scheduling problem on a daily basis. Dispatching rules are introduced to enhance the optimization progress. Numerical experiments show that optimising the model leads to schedules that meet the due dates. Moreover, by reducing the mean idle time of production lines with 35.6%, the optimization leads to a makespan reduction of 6.5% on average compared to real-life applied schedules.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85078592817&partnerID=MN8TOARS
U2 - 10.1080/00207543.2019.1705420
DO - 10.1080/00207543.2019.1705420
M3 - Article
SN - 0020-7543
VL - 59
SP - 727
EP - 751
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 3
ER -