SCHEDULING IN THE PHARMACEUTICAL INDUSTRY IEOR 4405 – Production Scheduling Kristinn Magnusson...
-
Upload
junior-blush -
Category
Documents
-
view
214 -
download
1
Transcript of SCHEDULING IN THE PHARMACEUTICAL INDUSTRY IEOR 4405 – Production Scheduling Kristinn Magnusson...
SCHEDULING IN THE PHARMACEUTICAL INDUSTRY
IEOR 4405 – Production Scheduling
Kristinn Magnusson
Sigrun Gunnhildardottir
Pharmaceutical Industry
Most important driver: time-to-market Highly Competitive Very regulated industry
High amount of cleaning and set up time needed between jobs
Life and death: no room for mistakes
Real Life Case
High but uncertain demand Supplier’s have long lead times 40 different product families 1000 different product variations (SKU’s)
Production Process
Goals and Objectives
Determine a campaign plan and schedule customer orders within the campaigns
Provide realistic and accurate models that are solvable within acceptable computational time
General objective of the plans and schedules: meet the quantity and delivery date of customer
orders minimize the unproductive production time maximize economic performance of the company
Three Level Hierarchical Framework
Level 1: Campaign Planning
Optimize campaign plan
Fulfill predicted demand
Minimize production time
Helpful for purchasing raw material
The model is updated every 3 months
Level 1: Model
Objective: Minimize
Subject to: Allocation Sequencing Delivery Capacity Campaign Mutually Exclusivity
Level 2: Campaign Planning and Order Allocation Actual orders are
known Revise campaign plan Allocate orders to
campaigns Specify in which
campaign each order are produced on every production stage
It gives the latest allowed completion time for the order
Level 3: Detailed Schedule
Actual timing of activities
Objective to minimize late deliveries
The model gives: Machine/Campaign for each
order for every production stage
Production sequence of orders
Start and processing time of tasks
Setup time required between orders
Heuristic: Decomposition of Production Stages
Improving Lower Bounds...
... by adding valid inequalities A constraint for the minimum number of
campaignes needed for a feasible solution A constraint for the minimum number of
delayed jobs
Solution Times
These models have been tested with real data and have been shown to be solvable within acceptable computational time 1. level: 14 hours 2. level: 6 hours 3. level: 6 minutes
References
P. Jensson, N. Shah and H. Stefansson, “Multiscale Planning and Scheduling in the Secondary Pharmaceutical Industry”, Published online October 26, 2006 in Wiley InterScience (www.interscience.wiley.com)
N. Shah, “Pharmaceutical supply chains: key issues and strategies for optimisation”, Computers and Chemical Engineering 28 (2004) 929–941
Any Questions ?