Improved tabu search algorithms for storage space allocation in integrated iron and steel plant

The fact that diversified and enormous materials are usually stored in open yards adds extra difficulty to model and solve the storage space allocation problem in material yards of iron and steel plants. This paper presents a nonlinear mathematical model for such a problem with the objective function of minimizing transportation costs and penalty trigged by the difference between materials and develops improved tabu search algorithms to solve it. These algorithms have two diversification strategies: (1) using an iterated local search strategy based on random kick moves as a method to escape from local optima and the neighborhood of descent heuristic in the iterated local search is generated by cyclic exchange moves; (2) directly using a cyclic exchange move to guide the search to a solution outside the neighborhood of a local optimum. The test with 150 random data sets proves that the tabu search with new diversification strategies is a fast and effective near optimal algorithm to solve such a practical industry problem