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 ?