Abstract: This paper puts forward a multi-objective constrained optimization model on unloading scheduling problem of coal terminal and the design of the genetic algorithm in which a simulation strategy is used to decode. First, the optimization model on unloading job scheduling problem is established with the multi-objective concerning maximizing the rate of unloading and minimizing the total time of train in port and some constraints including trains, coals, stocks, equipment, dumping lines and unloading processes. On the basis of summing up operations research, genetic algorithm and simulation, the genetic algorithm is designed including the improved coding and decoding with the simulation and deduction method, the way of chromosome generation, the design of fitness, genetic operation and correction using multiple strategies. Besides, the paper lists the steps of the genetic algorithm. Finally, the actual numerical experiment and the successful application have shown that the solution has a high execution efficiency and satisfactory effect.

Key words: coal terminal, unloading scheduling problem, multi-objective optimization, simulation and deduction, genetic algorithm

摘要： 本文针对煤炭码头卸车调度问题,提出了相应的多约束多目标优化模型,并设计了采用仿真推演策略解码的遗传算法求解。首先,本文考虑列车、煤种、场存、设备、翻堆线和卸车作业过程等约束条件,以卸车效率最大和列车在港时间最短为目标,构建了煤炭码头卸车调度问题多目标数学模型。然后,综合运筹学、遗传算法以及仿真技术,给出了煤炭码头卸车调度问题遗传算法详细设计,包括组合式编码和仿真推演解码方法,染色体生成算法,适应度函数设计,以及采用多种策略的遗传操作及修正等,并列出了算法步骤。实例测试表明,本算法的执行效率高而且优化效果好,结果适用。

关键词: 煤炭码头, 卸车调度问题, 多目标优化, 仿真推演, 遗传算法