1. Multi-scale Planning and Scheduling Under Uncertain and Varying Demand Conditions in the Pharmaceutical Industry Hierarchically Structured Integrated Multi-scale Approach Hlynur Stefansson and Prof. Nilay Shah Centre for Process Systems Engineering Imperial College London

2. Overview Introduction Project objectives Case study Proposed approach Models Solution procedure Results Conclusions

3. Introduction Typical process planning and scheduling approaches  Fixed time horizon  All data given Make to order manufacturing  Customers require high service levels and flexibility  Unpredictable demand  Competitive prices The pharmaceutical industry is a good example of an industry where planning and scheduling of make to order production is a big challenge

4. Project Objectives We propose an approach for a continuous and dynamic planning and scheduling process  Decisions have to be made before all data are available Objectives  An effective approach  A combination of a proactive and reactive planning  Accurate and efficient optimisation models and solution procedures  Decision support for actual MTO planning and scheduling problems

5. Case Study – Problem Description Actavis is one of the five largest generic pharmaceutical companies in the world Single plant planning and scheduling for a secondary pharmaceutical production plant Production environment  Over 40 product families and 1000 stock keeping units  4 production stages with a large number of multi-purpose production equipment  Campaign production operating in batch mode

6. Case Study – Problem Description Online and dynamic characteristics  A campaign plan made for long term planning  Each week the plant receives new customer orders with requested delivery date, feedback given to customers with confirmed delivery dates  Final detailed schedule made before production starts

7. Integrated Multi-Scale Algorithm Multi-scale modelling is emerging as an interesting scientific field in process systems engineering The idea of multi-scale modelling is straightforward:  Compute information at a smaller (finer) scale and pass it to a model at a larger (coarser) scale by leaving out degrees of freedom as moving from finer to coarser scales

8. Integrated Multi-Scale Algorithm Integrated multi-scale approach based on a hierarchically structured framework Optimisation models to provide support for the relevant decisions at each level Levels are diverse regarding aggregation, time horizon and availability of information at the time applied

9. Objectives: Campaign planning to fulfil demand and minimize production cost Input: Combination of sales forecasts and long-term orders, information regarding products, production process, performance and current status, Output: Campaign plan, raw material procurement plans Horizon: 12 months Frequency: Every 3 months Formulation: MILP - Discrete time and an iterative proced. to improve robustness campaigns with different product groups Model for level 1

10. Forcast errors analysed and a more robust plan obtained with an iterative MILP + LP procedure Robustness criteria depends on the required service level

11. Objectives: Simultaneous campaign planning and order scheduling, minimize delays and production cost Input: Customer orders, information regarding products, production process, performance and current status Output: Campaign plan, order allocation and confirmed delivery dates Horizon: 3 months Frequency: Every week Formulation: MILP - Discrete time campaigns with different product groups specific orders Model for level 2

12. Objectives: Detailed production scheduling with exact timing of all setup, production and cleaning tasks, minimize delays and production cost Input: Confirmed customer orders, information regarding products, production process, performance and current status Output: Detailed production schedule with exact timing of all tasks Horizon: 1 month Frequency: Every day Formulation: MILP - Continuous time production tasks within campaigns campaigns with different product groups Model for level 3

13. Integration of levels Information is transferred between levels with:  Hard constraints  Bounds on variables  Shaping methods  Penalty functions Feasible solutions can still be obtained when the guidelines are violated although they become less optimal

14. The MIP models become very large in order to fulfil actual industrial requirements Standard solution methods are insufficient Decomposition heuristics with pre- and post-processing procedures Pre-processing Optimisation with decomposition heuristics Post-processing Formulation – Solution Procedure subtracts knowledge from data and makes optimisaiton models tractable improves the solutions

15. Computational Results LevelNumber of orders Integer variables ConstraintsMax computational time [CPU seconds] 1400418445247831716 2180317264698621060 3708817253441062 Full scale test cases based on data collected in the production plant  An example of computational results:

16. Conclusions There is a need for designing and applying integrated multi-scale procedures for specific types of planning and scheduling problems in the process industry Benefits:  Solutions of improved quality  More efficient planning and scheduling process within acceptable computational time  Improved customer service by faster response driven by optimisation models Work remains on the robustness procedure at the top level and further testing of the MIA in the factory

17. Multi-scale Planning and Scheduling Under Uncertain and Varying Demand Conditions in the Pharmaceutical Industry Hierarchically Structured Integrated Multi-scale Approach Hlynur Stefansson and Prof. Nilay Shah Centre for Process Systems Engineering Imperial College London