GHANA Ermolaos Ververis Michael Wellington Ozge Nilay Yurdakul EMFOL Summer school August 2015.
Hierarchically Structured Integrated Multi-scale Approach Hlynur Stefansson and Prof. Nilay Shah
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Transcript of Hierarchically Structured Integrated Multi-scale Approach Hlynur Stefansson and Prof. Nilay Shah
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 ShahCentre for Process Systems Engineering Imperial College London
Overview
Introduction Project objectives Case study Proposed approach Models Solution procedure Results Conclusions
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
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
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
Machines
Time
Granulation
Compression
Coating
Packing
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Machines
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Machines
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Compression
Coating
Packing
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Machines
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Granulation
Compression
Coating
Packing
Machines
Time
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Granulation
Compression
Coating
Packing
Machines
Time
Granulation
Compression
Coating
Packingxxxxxxxxxxxxxxxxxxxxxxx
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Machines
Time
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Granulation
Compression
Coating
Packing
Machines
Time
Granulation
Compression
Coating
Packing
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
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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
Scale 1
Scale 2
Scale N
Removedegrees of freedom
Adddegrees of freedom
Multi-scale modelling
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
Level3
Level2
Level1
0 1 2 3 4 5 6 7 8 9 10 11 12 time
Continuous moving time frame
Information availability
Available information UncertaintyAggregation
Level3 – Detailed scheduling
Level2 – Campaign planning and order scheduling
Level1 – Campaign planning
0 1 2 3 4 5 6 7 8 9 10 11 12 time
Aggregation
Level3 – Detailed scheduling
Level2 – Campaign planning and order scheduling
Level1 – Campaign planning
0 1 2 3 4 5 6 7 8 9 10 11 12 time
Continuous moving time frame
Information availability
Aggregation
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
12 months
campaigns with different product groups
Level3 – Detailed scheduling
Level2 – Campaign planning and order scheduling
Level1 – Campaign planning
0 1 2 3 4 5 6 7 8 9 10 11 12 time
Aggregation
Model for level 1
Model for level 1
Forcast errors analysed and a more robust plan obtained with an iterative MILP + LP procedure
Robustness criteria depends on the
required service level
Plan meets robustness
criteria
LPs solved for alternative
demand samples
Demand forecast adjusted
MILP solved for forecasted demand
No
Yes Campaign plan
Demand forecastStatistically generated
demand samples
Level3 – Detailed scheduling
Level2 – Campaign planning and order scheduling
Level1 – Campaign planning
0 1 2 3 4 5 6 7 8 9 10 11 12 time
Aggregation
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
Level3 – Detailed scheduling
Level2 – Campaign planning and order scheduling
Level1 – Campaign planning
0 1 2 3 4 5 6 7 8 9 10 11 12 time
Aggregation
3 months
campaigns with different product groups
specific orders
Model for level 2
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
Level3 – Detailed scheduling
Level2 – Campaign planning and order scheduling
Level1 – Campaign planning
0 1 2 3 4 5 6 7 8 9 10 11 12 time
Aggregation
1 month
production tasks within campaigns
campaigns with different product groups
Model for level 3
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
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
Computational Results
Level Number oforders
Integervariables
Constraints Max computationaltime [CPU seconds]
1 400 41844 52478 31716
2 180 31726 46986 21060
3 70 8817 25344 1062
Full scale test cases based on data collected in the production plant An example of computational results:
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
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 ShahCentre for Process Systems Engineering Imperial College London