Theory vs. Reality: Job Shop Scheduling Projects & Challenges in Practice

1. When Theory Crashed into Reality Yossi Rissin Chief Executive Officer, Visopt B.V Roman Barták Chief Scientist, Visopt B.V.

2. What is the talk about? Theory Practice Planning vs. scheduling

3. Planners from VenusResearchers from Mars

4. A theoretical factory • M machines • N jobs • each job consists of Oi operations with the precedence relation (dedicated machines for operations) • Job Shop Scheduling (JCC) • Flow Shop Scheduling • Open Shop Scheduling

5. JSS in Practice? „I have never seen a Job Shop Scheduling Problem in practice“ Wim Nuiten, ILOG

6. The Human Factor (Planners & plant personnel are motivated by:) • Pride. No disclosure of mistakes, problems and weaknesses. • Position in the organisation. Position is protected by being nice to superiors, serving many masters at once, gaining professional respect. • Future security. No disclosure of knowledge, development of organisation dependency.

7. The Human Factor (Planners & plant personnel are characterised by:) • Politics. Internal politics and power plays are key factor in decision taking. • Inconsistency. A human being is tend to inconsistency and easily affected by mood, environment and psychology pressure. • Unexpected. Human behaviour can be determined and can be foreseen just by statistical methods (big numbers, long periods, distributions, etc.)

8. The Ideal Scheduling Projects • Fully automatic factory based on robots and AGV’s • Engineering oriented • No one to argue with • No one knows better • More visibility, less surprises and fluctuations • New factory, not operating yet • Very stable, no fluctuations • No previous “know-how” • No old rules and procedures • No bad habits • No day-to-day-reality to confront the theory

9. Points Of View • Planners • The planner’s world consists of products and their flow • “how can I produce this product now, and this one and that one…” • “How can I satisfy Mr. X from sales and Mr. Y from the plant and the customer at the same time, without getting into new troubles…” • Academy • The engineer/researcher world consists of resources and their usage • “How can I use the resources to get max X and min Y…” • “How can I get, using objective metrics, a plan that for the long term, will improve the plant efficiency…”

10. Not Invented Here • “We are different…” • Means, what you know is useless here • “Outsiders cannot understand it, it takes a lot of time…” • Means, you have to listen to us or to spend part of your life here • “Methods that suite others cannot implemented here…” • Means, your experience and knowledge are impressive, but you have to start from scratch

11. Visopt View • Visual Modelling Language

12. Inside Visopt load clean cool Alternative recipes Recycling N-to-N relations heat unload • Complex resources clean load heat unload load heat unload cool clean • General item flow

13. Quality Issues

14. Theoretical Objectives • Minimise makespan • Minimise lateness (tardiness) • Minimise earliness • Minimise the number of set-ups • Maximise resource utilisation • ...

15. Quality Definition • Quality metrics by the user/planner • “It should looks like the schedules I am doing…” • “Good plan should resemble those I use to make manually…” • “In order to produce good plan you have to follow my rules, know-how, procedures…” • Good plan is a one that can be ‘sold’ to production people easily • Most of times there are no history records of the manual plans to analyse their efficiency!

16. Visopt View • Understand the reason by asking Why! minimise makespan minimise lateness minimise earliness minimise number of set-ups maximise resource utilisation ... more satisfied demands penalty for delays storing cost expensive set-ups fix expenses So what is the common objective? M O N E Y In Visopt we minimise cost (= maximise profit).

17. Bridging the Gap Lessons learned

18. The Common Language • The planner tells a “story” – how to produce a given product or product family, but cannot follow what was understood • Tables and fields say nothing to the planner and not resemble his world • Visual modelling is the key – same, simple language for the user and the computer – the ability to draw the user story

19. Best Is Worse • “The Worst Enemy Of The Good Is The Best” • A very good plan (based on objective metrics) delivered after three hours is not relevant anymore – the factory is not the one it was few hours ago • The art of real-life scheduling is to deliver a plan which is good enough and fast enough: • Good enough – the user cannot improve it in reasonable time • Fast enough – depends on the plant dynamics. One hour can be too late for one plant and very fast to another

20. The Cure Is The Pain • Most manual planning methods that are considered as “know-how” are not relevant to automated scheduling… • What is considered as the “solid true” (Cure), is many times simplifications of reality to enable the manual scheduling (The pain) • Extract the real knowledge from the overall know-how with the help of plant experts • Always ask Why, for everything, and never accept an answer such as “this is the way to do it” • If there is no solid reason behind the “fact” – ignore it

21. Scheduling Is Knowledge Handling • Scheduling is not mathematics, but first of all a knowledge handling process • Capturing the real knowledge • Mapping the knowledge so the user can verify and update it • Process it concerning its elusive nature • Understand and overcome the accurate mathematical metrics when dealing with knowledge

22. 2 slides per hour talk only three words are different on these slides 78 slides per hour talk What is the real difference? Practitioner Researcher Based on „real-life“ data (PACT 96)!

23. Thank you! Yossi Rissin yossi.rissin@visopt.com Roman Barták bartak@visopt.com @