The first layer handles  capacity allocation.  This layer  distributes orders over time by due date at casting, levels casting demand  loads respecting major capacity constraints, allocates “swing orders” to  multiple casters, and generally satisfies global constraints.    
The second layer is the  heat building model, which combines orders into heats, loads heats into  available capacity, and concatenates similar heats, while respecting the next  level of capacity constraints.
The third and final  layer creates the detailed production sequence at steelmaking, ladle treatment  and continuous casting, manages the liquid iron inventory balance, and respects  the most detailed constraints.
The optimization layers iterate  until the total KPI balance is achieved.   The first layer executes, and control is passed to the next level  down.  If infeasibility is detected at  the second or third layer, control is returned to the next level up for  resolution.  The model cycles up and down  between layers until the total KPI balance requested by the planner is  achieved.
Thanks to the decomposition of the  scheduling problem into layers and the usage of the best fitting combinations  of solvers for each layer the computations are executed as efficiently as  possible.

Scheduling information system

The new method of scheduling was used as a base  for development of a solution for the Czech production company. Also the Czech company  specialists and LOGIS project team members defined a set of key requirements  for a new solution.
Whether  assessing the relative merits of multiple scheduling scenarios, or reacting to  shop floor problems that require immediate schedule changes, model execution  speed is simply fundamental, and must be accomplished without compromising  schedule quality.
No  model, however sophisticated, can fully represent the richness and variation of  physical reality.  Therefore the planner  must be able to easily make manual adjustments, with complete visibility to any  potential constraint violations.
The quality  of any melt shop and caster schedule can be measured along vectors that  correspond to the goals for scheduling.   The planner must have real-time visibility to the full set of Key  Performance Indicators that reflect the value of the schedule, after every  model run and after every manual adjustment.
The  goals for melt shop and caster scheduling can have different relative  importance for different steel plants, and can vary for the same plant as  business conditions change.  For example,  when alloy prices are high relative to steel prices, it’s more important to  control over-grading.  The planner must  be able to adjust the priority of caster scheduling goals, and have the model  produce schedules that comply.
The  planning environment evolves constantly: new facilities added, new products  introduced, changed commercial priorities, etc.   The melt shop and caster scheduling model must represent constraints in  a way that allows planners to make model changes without long IT wait  times.  Constraints should be represented  in the model as data and the planner should be able to add, change or delete  constraints as needed.
To implement these and other requirements LOGIS  specialists developed a new solution. During the implementation project the  model of melt shop and caster scheduling was set up in the solution. Example of  the working space of a planner with Gantt chart of melt shop and caster is  provided on the Figure 4.