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Scheduling In Logistics. Presented by. J érôme Rogerie Chief-Architect ILOG Scheduler ILOG SA, R&D Optimization. Summary. Introduction to combinatorial methods and related tools at ILOG Transportation in VMI On Line Manufacturing Execution and Control Design.

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  1. Scheduling In Logistics Presented by Jérôme Rogerie Chief-Architect ILOG Scheduler ILOG SA, R&D Optimization

  2. Summary • Introduction to combinatorial methods and related tools at ILOG • Transportation in VMI • On Line Manufacturing • Execution and Control Design Planet Industrial Day IKERLAN, Zamudio

  3. Methods in Combinatorial Optimization at ILOG • Mathematical programming • Linear programming, MIP, B&B search, associated methods • Constraint programming • Structured Variable and Constraint modeling • Propagation per constraint • Iterative methods • Local Search and meta-heuristics • LNS Planet Industrial Day IKERLAN, Zamudio

  4. ILOG Libraries • Math Programming • ILOG Cplex + facilities for some classical methods • Constraint programming • ILOG Solver: constraint propagation and local search engine, numerical and logical constraints • ILOG Scheduler: Constraint Based Scheduling • ILOG Configurator: configuration modeling and solving • ILOG Dispatcher : routing mixing Local Search and constraint solving Planet Industrial Day IKERLAN, Zamudio

  5. Transportation in VMI • Operational Customer Delivery Planning from the Vendor Management Inventory • Core problem is a Routing model • Collaboration LS (Routing) – CP (including CBS) • Input from the VMI and Last Minute Urgent user requests • Two next days planning Planet Industrial Day IKERLAN, Zamudio

  6. Time Window Routing Core Problem ILOG Dispatcher • Routing model • Set of sourcing site for some products (unknown number of visits) • Maximum capacity of a truck • Compatibility of products: type and minimum quality • Customer requests for some product with quantity and time window • Not a pickup-and-delivery, vehicle can refill for a product • Geographical information • Daily routing • Costs • Minimize mileage, idle capacity, overtime and sourcing cost Planet Industrial Day IKERLAN, Zamudio

  7. Side ConstraintsMove evaluations using CP • ILOG Solver • Legacy rules for drivers work time • Compatibility between products • Compatibility customer-truck and sourcing site-truck • ILOG Scheduler • Tractors and drivers limitations per time period • Search strategy • Transit instantiation (how much of which product is filled) • Time placement Planet Industrial Day IKERLAN, Zamudio

  8. User Interaction with engine • Configuration of the model • Cost Adjustments • Constraint Adjustment (addition or relaxation) • Results Analysis • What-if analysis • Debugging Planet Industrial Day IKERLAN, Zamudio

  9. On Line Manufacturing • Automatic factory driven by the on-line real-time scheduler • Operational Discrete Scheduling • Collaboration MIP – CBS • Input from the day-to-day planning scheduling • User can adjust the objectives Planet Industrial Day IKERLAN, Zamudio

  10. Factory Description • Constraints • Machines capabilities, availabilities, and locations • Single processing or batching, single or multiple functions • Tools availabilities, locations, and compatibilities • Setup and transition time for tools on machine • Transportation time between machine • Initial status • Objectives • Minimize transportation • Minimize tools change • Load balancing Planet Industrial Day IKERLAN, Zamudio

  11. Orders Description • Constraints • Sequence of Operations on a set of (machine, tools) • Due date • Daily Schedule • Predefined sequence • Compatibilities with Machine • Requirement of a tools for a given processing time • Orders initial status • Objectives • Minimize earliness • Maximize throughput • Minimize gap between actual and daily schedule Planet Industrial Day IKERLAN, Zamudio

  12. Decisions • Affectation of an operation to (machine-tool) • Sequence or batch composition on the machine • Operations Execution date Planet Industrial Day IKERLAN, Zamudio

  13. On-line scheduling model • From horizon to origin, • define a time interval [t, t+t) • Decide which task on which machine with which tools • Decide the sequence of tools, then schedule task horizon t t+t Origin Scheduled Actual Postponed Schedule Planet Industrial Day IKERLAN, Zamudio

  14. First Phase, MIP model • Decide which operation on which machine with which tools • Capacity Planning like MIP model on buckets [t, t+t) [t+t, horizon) • Approximation of earliness cost • Approximation of tools setup time and cost • Approximation of transportation time, predefined sequence, and temporal constraints Planet Industrial Day IKERLAN, Zamudio

  15. Second phase, CBS Model • From the solution of the MIP model on [t, t+t) bucket • Sequence tools on machine and operations on machines, then schedule process • Full constraint model • Selection tie-break strategy driven by objective and/or jam risk Planet Industrial Day IKERLAN, Zamudio

  16. Execution and Control DesignILOG Configurator • Ease engineering • Reduce control command room size Automat Cabinet Function T° CPU Rack Function T1 CPU Function T2 BUS Planet Industrial Day IKERLAN, Zamudio

  17. Configuration • Dynamic CSP • Variable and Constraints are dynamically created when decision on components are done • Hierarchical variables • The actual definition of the component is deduced from decisions • Composition Variables • Set of components are filled • Component properties • Variable that defines a property such a cost, energy consumption, … Planet Industrial Day IKERLAN, Zamudio

  18. ILOG Configurator User Specifications Configured Product Catalog Components Enumeration Components typing Compatibility Components connections (port) Sum/Max Ct Planet Industrial Day IKERLAN, Zamudio

  19. Execution and ControlTypical constraints • Compatibilities • Functions on same automat • Function and CPU • Automat and cabinet • Capacities • Cabinet size • Energy power • CPU and Bus capacities Planet Industrial Day IKERLAN, Zamudio

  20. Execution and Control3 Phases search • User request • a set of functions • A set of racks • Generation of a set of automats (and their constituents) and their placement in cabinets • Minimization of the number of automats • Minimization of some preference criteria. • Fixed Minimum of number of cabinets • Load balancing of the CPU charge Planet Industrial Day IKERLAN, Zamudio

  21. User Interaction • Very important in design application • Scenario analysis • What-if analysis • Constraint edition • (partially) Freezing a cabinet • Relaxing CPU security • Adding exclusion rule between functions • Reservation of free racks Planet Industrial Day IKERLAN, Zamudio

  22. conclusion • The complexity and the required performances of real life problems needs to combine several combinatorial optimisation techniques. • A solver must answer to several issues that a user may want to handle • Building good solutions from scratch • Maintaining a plan at realisations update • What-if analysis, hypothesis management • Constraint edition for addition, extension and relaxation • Cost edition to deal with user preferences • These point must be taken into account at the design phase of the solver Planet Industrial Day IKERLAN, Zamudio

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