Pharmaceutical Production and Project Management with Intelligent Scheduling
Aurora-ProPlan is a customization of Aurora™, Stottler Henke’s intelligent planning and scheduling system, that adds capabilities necessary to perform Pharmaceutical production and project management optimally. Pfizer uses Aurora-ProPlan to create optimal schedules for pharmaceutical production. Aurora-ProPlan also is part of the Intellicentic consortium led by Pfizer to promote leading proven solutions that optimize pharmaceutical manufacturing. By using sophisticated scheduling software as the underpinnings for Aurora-ProPlan, it can be applied to and optimize the production of pharmaceutical products, by taking into consideration the unique challenges found in pharmaceutical production. Aurora-ProPlan’s solid scheduling basis also allows it to more easily handle complex situations such as new tasks being inserted during the actual production execution, as well as other radical changes to the situation.
Features and Benefits
By adding Pharmaceutical specific capabilities to an already powerful and sophisticated scheduling software, Aurora-ProPlan provides many important benefits:
- Minimizes changeover time and inventory
- Optimizes production taking in consideration changeover times, carrying costs, etc.
- Maximize overall equipment effectiveness (OEE)
- More flexibility and an improved ability to accommodate change
Aurora-ProPlan provides enterprise solutions not otherwise available:
- Aurora-ProPlan is designed to interface with other enterprise applications
- Supports demand changes and inventory carrying cost
- Supports equipment downtime (planned and unplanned), line allocation and cost
- Large multi-project environments can be modeled
Graphical output shows line usage broken down by production time, and setup times, with options to show different products and different types of changeovers depicted differently.
Input & Output
Aurora-ProPlan can work with various information regarding the configuration of a packaging plant, in combination with the desired production goals. A multitude of constraints must be considered when performing the optimization process. To maximize throughput of the plant, the throughput of the machines needs to be known, and then the products/SKUs that are being packaged need to be provided. One critical piece of information when optimizing a schedule is the changeover matrix that provides the times when transitioning from one SKU to another. What needs to be packaged is another important input to the optimization process. This estimate of what the plant should provide can be based on historical or forecast information or on some combination of the two. In addition, there may be minimum and maximum frequencies with which any SKU must be produced. Another important factor is often the carrying cost, so that products with higher carrying cost may be biased to be produced as late as possible.
The minimum inputs needed to optimize the schedule include:
Preferably a matrix showing how the changeover time for each transition from one SKU to another
Planned machine downtime, or average percentage of uptime for the machine
Required production with deadlines
Order frequency: any information regarding minimum and/or maximum allowed orders per time period.
The image below shows the type of information that Aurora-ProPlan works with, then shows the resulting output.
Richards, R. (2015) Packaging Line Scheduling Optimization. Pharmaceutical Manufacturing Vol 14 no 8 pp 13-15, Oct2015. Article
Richards, R. (2015) Pharmaceutical Production Optimization. Presentation given at the IFPAC 2015 Conference. Arlington, VA. January 25-28, 2015. Presentation View Presentation
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The type of scheduling solved by Aurora-ProPlan extends beyond the Pharmaceutical industry and is referred to via different terminology. This section tries to encapsulate some of the more common terminology that is used to describe the solution that Aurora-ProPlan specifically and Aurora in general provides.
The problem solved by Aurora-ProPlan may be referred to as:
- Production scheduling
- Production planning of batch plants
- Parallel machine scheduling problem with setups, release and due dates and additional constraints related to the scarce availability of tools and human operators.
- Multi-product multistage batch plant scheduling
- Multi-stage multi-product batch scheduling
- Campaign and lot size scheduling problem
- Economic lot scheduling problem (ELSP)
- Parallel machine scheduling problem with sequence dependent setup times
- Scheduling problems with setup times and/or costs
- Sequence-dependent job scheduling
- Job scheduling using parallel non-identical machines with sequence dependent clearance times
With the objective function normally being the minimization of makespan.
Approaches used include:
- Discrete event simulation
- Tabu search algorithm
- Mixed Integer Programming (MIP) scheduling / Mixed integer mathematical model
- Iterative two-stage decomposition solution strategy
- Genetic algorithms
- Multi-grid continuous-time formulations