In this seminar, we will study variants of decomposition algorithms applied to scheduling and inventory location problems.

Scheduling problems are considered particularly hard for MIP solvers.

For this reasons, dynamic programming or constraints programming techniques are often applied instead.

However, applications in industry frequently complicate these problems further as they introduce additional constraints and objectives that render these non-MIP based approaches infeasible.

This issue can be addressed by exact or heuristic decomposition algorithms.

Here, we aim to decompose the problem into master and subproblems each exhibiting well-studied structure.

These problems are then solved repeatedly with appropriate algorithms.

In each iteration, the solution of the subproblems is used to guide search in the master problem and vice versa.

In this seminar, we will study variants of decomposition algorithms applied to scheduling and inventory location problems.

Scheduling problems are considered particularly hard for MIP solvers.

For this reasons, dynamic programming or constraints programming techniques are often applied instead.

However, applications in industry frequently complicate these problems further as they introduce additional constraints and objectives that render these non-MIP based approaches infeasible.

This issue can be addressed by exact or heuristic decomposition algorithms.

Here, we aim to decompose the problem into master and subproblems each exhibiting well-studied structure.

These problems are then solved repeatedly with appropriate algorithms.

In each iteration, the solution of the subproblems is used to guide search in the master problem and vice versa.

In this seminar, we will study variants of decomposition algorithms applied to scheduling and inventory location problems.

Scheduling problems are considered particularly hard for MIP solvers.

For this reasons, dynamic programming or constraints programming techniques are often applied instead.

However, applications in industry frequently complicate these problems further as they introduce additional constraints and objectives that render these non-MIP based approaches infeasible.

This issue can be addressed by exact or heuristic decomposition algorithms.

Here, we aim to decompose the problem into master and subproblems each exhibiting well-studied structure.

These problems are then solved repeatedly with appropriate algorithms.

In each iteration, the solution of the subproblems is used to guide search in the master problem and vice versa.

In this seminar, we will study variants of decomposition algorithms applied to scheduling and inventory location problems.

Scheduling problems are considered particularly hard for MIP solvers.

For this reasons, dynamic programming or constraints programming techniques are often applied instead.

However, applications in industry frequently complicate these problems further as they introduce additional constraints and objectives that render these non-MIP based approaches infeasible.

This issue can be addressed by exact or heuristic decomposition algorithms.

Here, we aim to decompose the problem into master and subproblems each exhibiting well-studied structure.

These problems are then solved repeatedly with appropriate algorithms.

In each iteration, the solution of the subproblems is used to guide search in the master problem and vice versa.

In this seminar, we will study variants of decomposition algorithms applied to scheduling and inventory location problems.

Scheduling problems are considered particularly hard for MIP solvers.

For this reasons, dynamic programming or constraints programming techniques are often applied instead.

However, applications in industry frequently complicate these problems further as they introduce additional constraints and objectives that render these non-MIP based approaches infeasible.

This issue can be addressed by exact or heuristic decomposition algorithms.

Here, we aim to decompose the problem into master and subproblems each exhibiting well-studied structure.

These problems are then solved repeatedly with appropriate algorithms.

In each iteration, the solution of the subproblems is used to guide search in the master problem and vice versa.