Evaluating Decomposition Strategies to Enable Scalable Scheduling for a Real-World Multi-line Steel Scheduling Problem

Steel scheduling is recognised as one of the most difficult real-world scheduling problems. It is characterised by a wide range of operational constraints, variable dependencies and multiple objectives. This paper uses a divide and conquer method to reduce the combinatorial complexity of a real-world multi-line steel scheduling problem. The problem is first decomposed into sub-problems which are solved individually in parallel using parallel branch and bound, then sub-problems are combined to form a solution to the original problem. Three decomposition strategies are compared, specifically: a manual heuristic domain knowledge (DOM) intensive strategy, K-means++ (KM) clustering and Self-organising maps (SOM). Experimental results show that using SOM for decomposition is a promising approach. This paper demonstrates that despite being a highly complex and constrained problem, it is possible to use divide and conquer to achieve potentially good scalability characteristics without significant detriment to the solution quality

Evaluating decomposition strategies to enable scalable scheduling for a real-world multi-line steel scheduling problem

Steel scheduling is recognised as one of the most difficult real-world scheduling problems. It is characterised by a wide range of operational constraints, variable dependencies and multiple objectives. This paper uses a divide and conquer method to reduce the combinatorial complexity of a real-world multi-line steel scheduling problem. The problem is first decomposed into sub-problems which are solved individually in parallel using parallel branch and bound, then sub-problems are combined to form a solution to the original problem. Three decomposition strategies are compared, specifically: a manual heuristic domain knowledge (DOM) intensive strategy, K-means++ (KM) clustering and Self-organising maps (SOM). Experimental results show that using SOM for decomposition is a promising approach. This paper demonstrates that despite being a highly complex and constrained problem, it is possible to use divide and conquer to achieve potentially good scalability characteristics without significant detriment to the solution quality