Abstract
We study a steel hot rolling scheduling (HRS) problem considering slab stack shuffling (SSS), which is a kind of key issues in the steel strip production. The HRS problem is to select suitable slabs for a predefined sequence of hot rolling slots, from their respective candidate slab sets so that the number of shuffling operations of slabs is minimized. Different from the most researches, we consider a situation where there are intersections among candidate slab sets and shuffled slabs will not return original stacks. Basing on a special index method of slabs, we present an integer linear programming (ILP) to compute approximation solutions of the problem. In order to improve the solutions obtained by the ILP model, we propose a very large-scale neighborhood (VLSN) search algorithm. In the VLSN search process, the solutions of HRS problems are improved by an iteration procedure that partial solutions are interactively destroyed and repaired. In each iteration, partitioned hot rolling slots correspond to a sub-problem. In the repair operations, we propose an efficient branch-and-bound algorithm for solving sub-problem. The computational results on a number of different scale simulated instances show that the VLSN search algorithm is efficient for solving this kind of HRS problems.
Highlights
The steel hot rolling scheduling (HRS) is one of key tasks in operation management of steel production processes, which has an important impact on product quality and production efficiency of hot rolling processes
3) Taking the solution obtained by integer linear programming (ILP) model as an initial one, we propose a very large-scale neighborhood (VLSN) search algorithm to improve it in which a branch and bound (BAB) procedure is applied as a repairing process
COMPUTATIONAL RESULTS we report our computational results of ILP model, the local search (LS) algorithm and the VLSN search algorithm
Summary
The steel hot rolling scheduling (HRS) is one of key tasks in operation management of steel production processes, which has an important impact on product quality and production efficiency of hot rolling processes. Tang et al [13] established a nonlinear integer programming model for an HRS problem considering SSS and proposed a genetic algorithm. They assumed an ideal case that there is no intersection among candidate slab sets. We establish an optimization model and propose a very large-scale neighborhood (VLSN) search algorithm. An HRS problem is to select a slab for each slot i from Ci so as to minimize the number of shuffled slabs during the execution of hot rolling schedule. Minimizing the number of the shuffled slab is taken as the optimization objective when we solve the HRS problems.
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