1. Roman Barták Visopt B.V. (NL) / Charles University (CZ) IP&S in complex and dynamic areas Visopt Experience

2. Talk outline Preliminaries planning vs. scheduling constraint technology in a nutshell Complex Worlds transition schemes item flows Dynamic Worlds handling problem changes Conclusions complex demo

3. Preliminaries Planning, scheduling, and constraints

4. Terminology “The planning task is to find out a sequence of actions that will transfer the initial state of the world into a state where the desired goal is satisfied“ “The scheduling task is to allocate known activities to available resources and time respecting capacity, precedence (and other) constraints“

5. IP&S Intelligent planning & scheduling Integrated planning & scheduling Planning Scheduling actions failure Planning Scheduling

6. Integration When do we need to integrate more?  If there are too frequent backtracks from scheduling to planning. Improving the planner may help.  If existence of the activity depends on allocation of other activities. We call it a process-dependent activity. Foregoing planning of activities cannot be done there!

7. Process-dep. activity re-heating re-cycling AB setup by-product transition final product heat process heat process process long duration re-heat

8. Constraint technology based on declarative problem description via:  variables with domains (sets of possible values) e.g. start of activity with time windows  constraints restricting combinations of variables e.g. endA < startB constraint optimisation via objective function e.g. minimise makespan Why to use constraint technology?  understandable  open and extendible  proof of concept

9. Constraints: unary resource constraint Search strategies: ordering of activities  Decide first the activities with a minimal slack  Choose ordering leading to a bigger slack 4 16 7 15 6 16Scheduling A (2) B (4) C (5) A (2) 4 7 A B slack for A<<B

10. Complex Worlds handling complex resources Visopt experience

11. Motivation Planning & scheduling in complex areas  resources with complex behaviour setup and cleaning activities  complex relations between resources alternative recipes re-cycling Some examples:  mould change in plastic industries  acid cleaning in food industries  re-cycling in petrochemical industries ...

12. Complex resources Resource behaviour is described via  a state transition diagram  activity counters per state  global activity counters e.g. force a given state (cleaning) after a given number of activities load heat unload clean cool cleanloadheatunloadloadheatunloadcoolclean produce A (3-4) produce B (1-2) produce C (2-4) AAABCCCCAAACCBAAA 1 2 22

13. Handling transitions A slot model of resources  slot is a space for activity in the resource  variables describe activity parameters in the slot state counters times constraints time shift slots can slide in time slots cannot swap their positionendstart K-1 K K+1state startend duration +counters

14. Item flows Relations between resources are described via supplier-consumer dependencies Alternative recipes Recycling supplier consumer N-to-N relations

15. Looking for suppliers Handling dependencies Basic ideas: when the activity is known (located to a slot) introduce related activities (suppliers/consumers) the solver is selecting among introduced activities (planning within scheduling) 

16. Dynamic Worlds handling problem changes academic research

17. Motivation Planning, scheduling & timetabling problems  changes in the problem formulation  minimal changes to the solution  other features: over-constrained problems hard-to-solve problems Some examples:  gate allocation in airports  production scheduling  timetabling problems ...

18. Soft solutions Return some solution even if no solution exists Soft constraints User assigns preferences/weights to the constraints. Motivation: Some constraints express preferences rather than requirements. Return some solution even if one does not know in advance that no solution exists. Soft (incomplete) solutions Assign as many variables as possible (i.e., without any conflict). Motivation: In school timetabling assign as many courses as possible. Note: Can be applied to hard-to-solve problems.

19. Perturbations initial problem  + initial solution  new problem  ´ Perturbation: change in the new solution  for  ´ w.r.t.  The task: Find a solution of the changed problem that minimises the number of perturbations.  Minimal Perturbation Problem  Mapping between objects/variables

20. MPP example Random placement problem Place a random set of rectangles (no overlaps) to a rectangular placement area ABCDEFABCDEF 1 2 3 4 5 6 7 8 9 10 ABCDEFABCDEF 3 4 5 6 7 8 Initial problem Solution of the changed problem with 3 perturbations Change: object 1 must be in row B 9 1 2

21. Solving MPP Principle:  solve the changed problem  use the initial solution as a guide Basic solver:  branch-and-bound  limited assignment number search limit the number of attempts to assign a value to the variable  linear search space (lan_limit * number_of_variables) Guide:  first, assign values to variables with perturbation  prefer values which minimise additional perturbations

22. Conclusions Demo

23. Demo problem worker machine 1 machine 2 parallel (3..3) recycle (1..1) clean (1..1) serial (1..sup) beginner (4..4) experienced (1..sup) ser.par. ser. clean 1 23456 7 8 0 par. rec.ser. par. ser. cle. par. rec. count reset after 8 parallel (3..3) clean (1..1) serial (1..sup) beg.exp. 12341234 beg. Parallel (with worker) and serial production Re-cycling of by-products after 3 parallel activities Synchronised cleaning after 8 production activities Learning curve and working time for the worker

24. Expected solutions Synchronised cleaning cleaning parallel with recycling Free cleaning cleaning

25. Roman Barták Visopt B.V. (NL) /Charles University (CZ) IP&S in complex and dynamic areas Visopt Experience