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Order scheduling
Scheduling is used for daily order termination in the production environment, ensuring near-optimal resource allocation and adherence to deadlines for most customer orders.
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Conclusion

The simulation-based scheduling can be more effective in a dynamic production environment than the optimization-based scheduling. It predicts delivery dates more accurately, because many potential process variations and possible production events are considered very early in the planning. On the other hand the production orders are processed sequentially in the simulation-based planning according to their start date and perhaps some local selection rules. Contrary to that the optimization-based planning utilizes a global search strategy for the best sequence of the individual orders and operations. That is why the optimization-based planning is usually preferred for well predictable production systems, i.e. with little variability.

An experiment, which scheduling type can predict the delivery dates more precisely?

We have compared the two types of scheduling with each other in a simulation experiment. For that we have built a simulation model with a dynamic production process. The process steps times are stochastic times in a certain range (min, max) rather than fixed times. The resources in the model may also fail randomly. The simulation-based scheduling utilizes directly the process time, resource variations and failures in the model. The quasi-optimization-based scheduling, however, requires fixed process times so we provide an average process times instead and remove resource failures from the model. In this experiment we let both scheduling tools create their schedules for all the production orders first. Then we run the simulation model again to find out a possible quasi production period outcome. At the end of the simulation run we have got all the results painted in one chart. The chart shows the sum of delays in hours for the orders completed by the time of every measurement.
We offer simulation or optimization based scheduling technology. Both of them can:
In other points differ the two scheduling techniques considerably. It is important to realize their advantages and disadvantages in order to use their potential correctly.
For employees and resources: manage qualifications of the personnel use changeover matrices for machines take into account alternative resources and operators apply shift models calculate secondary resources (such as tools with limited qty)
Results and data sources: visualize results in Gantt charts report with detailed log lists per resource bind directly to databases or other data sources
Daten-Kreislauf What scheduling systems are for?
What scheduling systems are for?
Differences in           scheduling systems
Differences in scheduling systems
Impact of variability, an experiment
Impact of variability, an experiment
ERP Scheduling MES
For orders: schedule production orders forwards experiment with additional customer demand take into account limited capacities of resources plan material usage with bill of materials consider and calculate priorities, qualities or other factor depending on resource availability
Explanations of the lines in the diagram •	Orange line - due dates - shows the target values, the delivery dates promised to customers (same as due dates, so no delays) •	Brown line - deterministic - the optimization based scheduling shows a value of about -1400 hours (minus 1400) at the end. The sum of all orders delays is negative. On average are the orders produced much earlier than their due dates. •	Blue line - stochastic - the simulation-based scheduling ends at about + 1700 hours. The orders were very delayed. On average they are much later than their due dates. •	Green line - the actual sequence in production-ends at about + 1000 hours. Orders were on average late. How you can use scheduling to gain

Simulation based scheduling

Optimization based scheduling

unique advantages evaluates the production environment dynamically with detailed transport activities, delayed parts supply and congestion in production bottlenecks can be specifically managed and planned, so that all other processes follow them accordingly calculates and shows the likelihood of order completion, takes into account random delays that can happen in production such as equipment failures or unavailable staff precise availabilities and movements for staff, batches and resources builds up all unique features in a production system faithfully 3D animation allows for easy validation of the daily plan Each project is worked out in detail using simulation models, with many aha effects for all involved
unique advantages reverse planning strategies possible orders can be manually fixed at resources seeks global best combination of processes in production according to a user defined target function. Thus, the orders are resorted and individually scheduled, until the target function achieves the best result there are predefined data masks and solid model framework for many standard applications in the production environment includes a user console with active feedback loop from production. in case of discrepancies it can be rescheduled on the fly. visualizes order networks and order dependencies large costumer pool
disadvantages or limitations no global optimization of operations as standard. The decision and selection rules are applied locally in decision points instead a rough cut production plan from ERP is helpful to find the first sequence for the schedule. This plan is then turn by turn improved. The resulting schedule offers a detailed order plan and accurate use of resources and materials reverse order scheduling only conditionally possible pure planning and scheduling software - no MES functions available. Production data from MES and ERP is loaded on demand. After the simulation based scheduling ends, the new results/evaluations will be exported to the external systems (MES, ERP, ...).
disadvantages or limitations all operations are considered equivalent, whether it is a bottleneck or a derivative operation only fixed process data can be entered, no "from ... to ..." times, costs and other stochastic data are possible a failure or delay in process frequently brings the work schedule messed up. Because it needs to be rescheduled it leads often to even later delivery dates paths and movements of staff / resources can be only roughly estimated congestions and traffic delays are not considered and not visible in a schedule
Software example Read more about GanttPlan
Compare yourself
Compare yourself
Compare them yourself
We can show you on your system what each of these scheduling types means for your production system. Please contact us.
Which scheduling fits to your production? Comparison of unique scheduling features Impact of variability on production PL EN
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© 2018 Simulate First. All rights reserved.
Order scheduling
Scheduling is used for daily order termination in the production environment, ensuring near-optimal resource allocation and adherence to deadlines for most customer orders.
How you can use fine planing to gain
The two fine-planning technologies we offer, namely simulation and optimized fine planing, can:
employees and resources manage qualifications of the personnel use changeover matrices for machines choose alternative resources and operators apply shift models calculate secondary resources
Results and data sources visualize results in Gantt charts report with detailed log lists per resource bind directly to databases or other data sources
Daten-Kreislauf ERP Feinplanung MES
Orders: schedule production orders forwards experiment with additional customer demand take into account limited capacities of resources plan material usage with bill of materials consider and calculate priorities, qualities depending on resource availability
Comparison of fine-planning options
In other points differ the two scheduling techniques considerably. It is important to realize their advantages and disadvantages in order to use their potential correctly.
Vergleich zwischen Feinplanungen, Summe aller Verspätungen
Tell us what requirements has your project and we help you find a good solution to realize it.
Contact              us
Contact us
Impact of variability on production Do you have further questions to your projects? What scheduling systems are for?
What scheduling systems are for?
Differences in           scheduling systems
Differences in scheduling systems
Impact of variability, an experiment
Impact of variability, an experiment
Compare yourself
Compare yourself
Compare them yourself

Conclusion

The simulation-based scheduling can be more effective in a dynamic production environment than the optimization-based scheduling. It predicts delivery dates more accurately, because many potential process variations and possible production events are considered very early in the planning. On the other hand the production orders are processed sequentially in the simulation-based planning according to their start date and perhaps some local selection rules. Contrary to that the optimization-based planning utilizes a global search strategy for the best sequence of the individual orders and operations. That is why the optimization- based planning is usually preferred for well predictable production systems, i.e. with little variability.
We can show you on your system what each of these scheduling types means for your production system. Please contact us.

Optimization based scheduling

unique advantages reverse planning strategies possible orders can be manually fixed at resources seeks global best combination of processes in production according to a user defined target function. Thus, the orders are resorted and individually scheduled, until the target function achieves the best result there are predefined data masks and solid model framework for many standard applications in the production environment includes a user console with active feedback loop from production. In case of discrepancies it can be rescheduled on the fly. visualizes order networks and order dependencies large costumer pool
disadvantages or limitations all operations are considered equivalent, whether it is a bottleneck or a derivative operation only fixed process data can be entered, no "from ... to ..." times, costs and other stochastic data possible a failure or delay in process frequently brings the work schedule messed up. Because it needs to be rescheduled it leads often to even later delivery dates paths and movements of staff / resources can be only roughly estimated congestions and traffic delays are not considered and not visible in a schedule
Software example Read more about GanttPlan

Simulation based scheduling

unique advantages evaluates the production environment dynamically with detailed transport activities, delayed parts supply and congestion in production bottlenecks can be specifically managed and planned, so that all other processes follow them accordingly calculates and shows the likelihood of order completion, takes into account random delays that can happen in production such as equipment failures or unavailable staff precise availabilities and movements for staff, batches and resources builds up all unique features in a production system faithfully 3D animation allows for easy validation of the daily plan Each project is worked out in detail using simulation models, with many aha effects for all involved
disadvantages or limitations no global optimization of operations as standard. The decision and selection rules are applied locally in decision points instead A rough cut production plan from ERP is helpful to find the first sequence for the schedule. This plan is then turn by turn improved. The resulting schedule offers a detailed order plan and accurate use of resources and materials reverse order scheduling only conditionally possible pure planning and scheduling software - no MES functions available. Production data from MES and ERP is loaded on demand. After the simulation based scheduling ends, the new results/evaluations will be exported to the external systems (MES, ERP, ...).

An experiment, which scheduling type can

predict the delivery dates more precisely?

We have compared the two types of scheduling with each other in a simulation experiment. For that we have built a simulation model with a dynamic production process. The process steps times are stochastic times in a certain range (min, max) rather than fixed times. The resources in the model may also fail randomly. The simulation-based scheduling utilizes directly the process time, resource variations and failures in the model. The quasi-optimization-based scheduling, however, requires fixed process times so we provide an average process times instead and remove resource failures from the model. In this experiment we let both scheduling tools create their schedules for all the production orders first. Then we run the simulation model again to find out a possible quasi production period outcome. At the end of the simulation run we have got all the results painted in one chart. The chart shows the sum of delays in hours for the orders completed by the time of every measurement.
Our flexible optimization methods for production and processes