Quantification of the Performance of Advanced Planning and Scheduling (APS) Systems
Budget: €30 – €250 EUR
Overview
My thesis focuses on evaluating how different production scheduling algorithms impact the performance of a manufacturing system.
In modern factories, multiple products compete for shared resources such as machines and workstations. The way production orders are sequenced and scheduled has a significant effect on:
Throughput
Machine utilization
Lead times
Overall efficiency
Objective
The main goal is to:
Measure and compare how different scheduling algorithms affect production performance.
Rather than just applying scheduling methods, the thesis aims to quantify their impact using measurable performance indicators.
Approach
The work is based on simulation using Siemens Plant Simulation, where a virtual factory model is created.
The process:
Model a production system
Multiple machines
Different product routes
Realistic constraints (buffers, queues, etc.)
Implement different scheduling algorithms
Example: FIFO, Shortest Processing Time, etc.
Potentially advanced optimization methods (APS-type logic)
Run simulation experiments
Same production scenario
Different scheduling strategies
Measure performance using KPIs
Throughput (output rate)
Machine utilization
Waiting times
Work-in-progress (inventory levels)
Makespan (total completion time)
Key Idea
The thesis investigates:
How much efficiency improvement can be achieved by using better scheduling algorithms.
This is important because even without changing physical resources, better planning alone can significantly improve productivity.
Expected Outcome
A quantitative comparison of scheduling strategies
Identification of which methods perform best under specific conditions
Insights into how APS systems contribute to operational efficiency
My thesis focuses on evaluating how different production scheduling algorithms impact the performance of a manufacturing system.
In modern factories, multiple products compete for shared resources such as machines and workstations. The way production orders are sequenced and scheduled has a significant effect on:
Throughput
Machine utilization
Lead times
Overall efficiency
Objective
The main goal is to:
Measure and compare how different scheduling algorithms affect production performance.
Rather than just applying scheduling methods, the thesis aims to quantify their impact using measurable performance indicators.
Approach
The work is based on simulation using Siemens Plant Simulation, where a virtual factory model is created.
The process:
Model a production system
Multiple machines
Different product routes
Realistic constraints (buffers, queues, etc.)
Implement different scheduling algorithms
Example: FIFO, Shortest Processing Time, etc.
Potentially advanced optimization methods (APS-type logic)
Run simulation experiments
Same production scenario
Different scheduling strategies
Measure performance using KPIs
Throughput (output rate)
Machine utilization
Waiting times
Work-in-progress (inventory levels)
Makespan (total completion time)
Key Idea
The thesis investigates:
How much efficiency improvement can be achieved by using better scheduling algorithms.
This is important because even without changing physical resources, better planning alone can significantly improve productivity.
Expected Outcome
A quantitative comparison of scheduling strategies
Identification of which methods perform best under specific conditions
Insights into how APS systems contribute to operational efficiency