392
The next step is to implement this optimization algorithm for short-term and operational schedules (monthly to daily plans), allowing plan revisions that dynamically optimize
resource allocation according to production results while considering the big picture set by
tactical plans.
Although a significant improvement is achieved compared to manual scheduling, this
algorithm still has some limitations and does not consider:
• different processing rates for each parallel machine;
• activities being partially done before machine change (preemptions);
• machines performing different activities;
• multiple activities of same task happening in parallel (at same time);
• intangible information of tasks and activities.
It is worth noting that most of these limitations are currently not considered in manual scheduling assumptions and do not represent the general operational procedures (are
exceptions).
Future work includes addition of some of this functionality; mainly the use of different
machine rates and preemptions.
REFERENCES
AIMMS B.V. 2018. AIMMS Modeling Guide—Integer Programming Tricks. Haarlem, The Netherlands: AIMMS B.V. https://download.aimms.com/aimms/download/manuals/AIMMS3OM_
IntegerProgrammingTricks.pdf.
Pinedo, Michael. Scheduling: theory, algorithms, and systems. Springer, 2016.
Schmidt, Günter & Wilhelm, Wilbert. (2000). Strategic, Tactical and Operational Decisions in Multinational Logistics Networks: A Review and Discussion of Modeling Issues. International Journal of
Production Research—INT J PROD RES. 38. 1501–1523. 10.1080/002075400188690.
Taha, H.A. Operations Research: An Introduction, 10th ed., Pearson, 2017.
Winston, W.L. Operations Research: Applications and Algorithms, 4th ed., Thomson Brooks/Cole, 2004.
Wolsey, L.A. Integer Programming, John Wiley & Sons, New York, 1998.
APPENDIX I: SOFTWARE REFERENCES
Anaconda: https://www.anaconda.com/.
Google OR Tools: https://developers.google.com/optimization/.
Jupyter Notebook: https://jupyter.org/.
Pyinstaller: http://www.pyinstaller.org/.
Python: https://www.python.org/.
wxFormBuilder: https://github.com/wxFormBuilder.
The next step is to implement this optimization algorithm for short-term and operational schedules (monthly to daily plans), allowing plan revisions that dynamically optimize
resource allocation according to production results while considering the big picture set by
tactical plans.
Although a significant improvement is achieved compared to manual scheduling, this
algorithm still has some limitations and does not consider:
• different processing rates for each parallel machine;
• activities being partially done before machine change (preemptions);
• machines performing different activities;
• multiple activities of same task happening in parallel (at same time);
• intangible information of tasks and activities.
It is worth noting that most of these limitations are currently not considered in manual scheduling assumptions and do not represent the general operational procedures (are
exceptions).
Future work includes addition of some of this functionality; mainly the use of different
machine rates and preemptions.
REFERENCES
AIMMS B.V. 2018. AIMMS Modeling Guide—Integer Programming Tricks. Haarlem, The Netherlands: AIMMS B.V. https://download.aimms.com/aimms/download/manuals/AIMMS3OM_
IntegerProgrammingTricks.pdf.
Pinedo, Michael. Scheduling: theory, algorithms, and systems. Springer, 2016.
Schmidt, Günter & Wilhelm, Wilbert. (2000). Strategic, Tactical and Operational Decisions in Multinational Logistics Networks: A Review and Discussion of Modeling Issues. International Journal of
Production Research—INT J PROD RES. 38. 1501–1523. 10.1080/002075400188690.
Taha, H.A. Operations Research: An Introduction, 10th ed., Pearson, 2017.
Winston, W.L. Operations Research: Applications and Algorithms, 4th ed., Thomson Brooks/Cole, 2004.
Wolsey, L.A. Integer Programming, John Wiley & Sons, New York, 1998.
APPENDIX I: SOFTWARE REFERENCES
Anaconda: https://www.anaconda.com/.
Google OR Tools: https://developers.google.com/optimization/.
Jupyter Notebook: https://jupyter.org/.
Pyinstaller: http://www.pyinstaller.org/.
Python: https://www.python.org/.
wxFormBuilder: https://github.com/wxFormBuilder.