Hierarchically Structured Integrated Multi-scale Approach Hlynur Stefansson and Prof. Nilay Shah

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. campaigns with different product groups Model for level 1 • 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

10. Robustness criteria depends on the required service level Model for level 1 • Forcast errors analysed and a more robust plan obtained with an iterative MILP + LP procedure

11. campaigns with different product groups specific orders Model for level 2 • 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

12. campaigns with different product groups production tasks within campaigns Model for level 3 • 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

13. 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 Integration of levels

14. Formulation – Solution Procedure • 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 Optimisation with decomposition heuristics Pre-processing Post-processing subtracts knowledge from data and makes optimisaiton models tractable improves the solutions

15. Computational Results • 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