industrial applications of constraint based scheduling helmut simonis parc technologies ltd ic-parc,...

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  • Slide 1
  • Industrial Applications of Constraint Based Scheduling Helmut Simonis Parc Technologies Ltd IC-Parc, Imperial College London Helmut Simonis Parc Technologies Ltd IC-Parc, Imperial College London Based on joint work with Y. Cloner, A. Aggoun (COSYTEC)
  • Slide 2
  • 2003 Parc Technologies Limited 21-Oct-2003, #2 Overview Global constraints Scheduling with global constraints Brief history Operational examples
  • Slide 3
  • 2003 Parc Technologies Limited 21-Oct-2003, #3 Constraint Programming - in a nutshell Declarative description of problems with Variables which range over (finite) sets of values Constraints over subsets of variables which restrict possible value combinations A solution is a value assignment which satisfies all constraints Constraint propagation/reasoning Removing inconsistent values for variables Detect failure if constraint can not be satisfied Interaction of constraints via shared variables Incomplete Search User controlled assignment of values to variables Each step triggers constraint propagation
  • Slide 4
  • 2003 Parc Technologies Limited 21-Oct-2003, #4 Need for global constraints Y in {2,3} Z in {1,3} U in {1,2,3,4} X in {2,3} Y X Z U 1 2 3 4 local reasoning, no actionglobal reasoning, detect implications by bi-partite matching
  • Slide 5
  • 2003 Parc Technologies Limited 21-Oct-2003, #5 Global constraints Work on sets of variables Global conditions, not local constraints Semantic methods Operations Research Spatial algorithms Graph theory Network flows Building blocks (high-level constraint primitives) Multi-purpose As general as possible Usable with other constraints Very strong propagation Acceptable algorithmic complexity
  • Slide 6
  • 2003 Parc Technologies Limited 21-Oct-2003, #6 Temporal Relations Some task must start after others have finished Easy to model with inequality constraints Much better reasoning possible when considered together with resource constraints precedence constraint
  • Slide 7
  • 2003 Parc Technologies Limited 21-Oct-2003, #7 Cumulative (Disjunctive) Resources End Limit start duration resource time resource Cumulative constraint
  • Slide 8
  • 2003 Parc Technologies Limited 21-Oct-2003, #8 Machine Choice (Speed) M1 M2 M3 M4 M5 M6 time machine start machine duration 1 Diffn (2D)
  • Slide 9
  • 2003 Parc Technologies Limited 21-Oct-2003, #9 Machine Calendars M1 M2 M3 M4 M5 M6 time machine start machine duration 1 Diffn (2D) with calendar rules Interruptions non-interruptible task
  • Slide 10
  • 2003 Parc Technologies Limited 21-Oct-2003, #10 Consumable Resources Storage Max capacity Min capacity
  • Slide 11
  • 2003 Parc Technologies Limited 21-Oct-2003, #11 Storage Assignment produce store consume Diffn (2D)
  • Slide 12
  • 2003 Parc Technologies Limited 21-Oct-2003, #12 Storage Assignment with Capacity produce store consume Diffn (3D)
  • Slide 13
  • 2003 Parc Technologies Limited 21-Oct-2003, #13 Sequence Dependent Setup cycle with distance matrix forbidden sequence variable time
  • Slide 14
  • 2003 Parc Technologies Limited 21-Oct-2003, #14 Brief history of CP-based scheduling Alice (Lauriere), 1978 CHIP (Dincbas, Van Hentenryck, Simonis), 1987 First commercial CP scheduling application (HIT, ICL), 1989 Cumulative resources (Aggoun, Beldiceanu), 1993 Disjunctive resources (Nuijten, Caseau, LePape), 1994 Machine choices (Beldiceanu, Contejean), 1994 Sequence dependent setup (Beldiceanu, Contejean), 1994 Alldifferent (Regin), 1994 Pre-emptive scheduling constraint (Baptiste, LePape), 1998 LP/CP hybrids (Wallace, Rodosek, El Sakkout), 1998
  • Slide 15
  • 2003 Parc Technologies Limited 21-Oct-2003, #15 PLANE (Dassault) Assembly line scheduling developed by Dassault Aviation for Mirage 2000 Jet/ Falcon business jet Two user system production planning 3-5 years commercial what-if sales aid Optimization requirement to balance schedule minimize changes in production rate minimize storage costs Benefits and status replaces 2 week manual planning operational since Apr 94 now used in US for business jets
  • Slide 16
  • 2003 Parc Technologies Limited 21-Oct-2003, #16 FORWARD (TECHNIP, COSYTEC) Oil refinery production scheduling Incorporates ELF FORWARD LP tool Schedules daily production Crude arrival -> processing -> delivery Design, optimize and simulate Crude mix optimization Ship unloading, storage Pipeline transport Product blending Explanation facilities Handling of over-constrained problems Status Operational at FINA, ISAB, BP,
  • Slide 17
  • 2003 Parc Technologies Limited 21-Oct-2003, #17 ORDO-VAP (VCA, COSYTEC) Production scheduling for glass factory integrated with Ingres Information system manual and automatic scheduling Constraints multi-stage manufacturing consumer/producer varying production rates, setup balance manpower utilization minimize downtime Status 2 phases operational since March 96 replaced manual operation
  • Slide 18
  • 2003 Parc Technologies Limited 21-Oct-2003, #18 MOSES (Dalgety, COSYTEC) Production scheduling for animal feed production Feed in different sizes/ for different species Contamination human health risk Strict regulations imposed by customers Constraints Avoid contamination risks Machine setup times Machine choice (quality/speed) Limited storage of finished products Very short lead times (8-48 hours) Factory structure given as data Status operational since Nov 96 installed in 5 mills
  • Slide 19
  • 2003 Parc Technologies Limited 21-Oct-2003, #19 Bandwidth on demand (Schlumberger, IC-Parc, PTL) Provide on-demand, high QoS bandwidth for limited time period Use cases Well logging Video conference Runs on MPLS-TE, diffserv Temporal extension of general routing problem Hard QoS limits Overall bandwidth limits Uses hybrid (CP/MIP/local search) algorithm Delivered on Schlumbergers Dexa.net Self-provisioned by customer
  • Slide 20
  • 2003 Parc Technologies Limited 21-Oct-2003, #20 Conclusion Constraints are a mature technology for scheduling Easy to combine different constraints in one system, flexible for modeling complex systems Most useful for hard problems, medium size (hundreds of tasks, dozens of resources) Large variety of solutions in different application fields using commercial, off-the-shelf tools