GM(1,1) based grey programming model for emergency goods scheduling

To schedule emergency goods scientifically and effectively so as to meet the demand of emergency goods within limited time under uncertain circumstance, grey theory is introduced to establish a multi-objective grey programming model in this paper. GM(1,1) is adapted and employed to predict emergency goods demand quantities. Both reducing emergency goods consumption and transportation cost to the greatest extent are considered during the course of emergency goods scheduling. In the objective function, the earliest emergency activity start-time, the fewest participated emergency goods supply nodes and the highest preference degree of emergency constraints is presented. The genetic arithmetic (GA) for above model is designed, and an instance abstracted from practical emergency scheduling work is solved. By adjusting three key control parameters of GA, test and improve the performance of the algorithm. The experimentation results by GA is compared with that by the time limit maximum(TLM) method. It shows that under the condition that the total emergency goods quantities provided by all supplied nodes is const, the optimizing capability of genetic algorithm is stronger than the TLM method when the emergency goods demand quantities are not too high. GA is suitable for solving the multi-objective gray programming problem.