随机环境下铁路枢纽内集装箱中心站选址分配优化模型及其遗传模拟退火算法
Optimization Model and Genetic Simulated Annealing Algorithm for Location and Allocation Problem of Container Central Station in Railway Hub under Stochastic Environment
研究了随机环境下铁路枢纽内集装箱中心站的选址问题。首先分析了集装箱中心站的运输组织模式并给出了其选址的若干原则,然后在运输需求随机变动和有车站作业能力限制的条件下建立了0-1整数随机规划模型,目标为极小化期望总费用。该模型中含有不可转化为确定性等价类的机会约束,因此文章利用随机模拟方法来计算目标函数值并检验机会约束,并在此基础上给出了一种嵌入了模拟退火操作的混合遗传算法。测试结果表明,与经典遗传算法相比,遗传模拟退火算法运行的时间虽然有所增加,但结果明显更优,且有较强的稳定性。
his paper addresses the location problem of container central station in railway hub under stochastic environment. Firstly, the paper presents the operating policy of the container central station and some principles while locating it. Then the paper proposes a stochastic integer programming model, which aims to minimize the expected total cost, under the condition that the transportation demand is random variable and the capacity of station is limited. Because there are some chance constraints which can not be converted to deterministic equivalent in the model, stochastic simulation is introduced to obtain the objective value and check whether the solution can satisfy chance constraints. Based on this method, the paper presents a hybrid genetic algorithm which imbedded with simulated annealing operation. The numerical experiments show that compared with classical genetic algorithm, the genetic simulated annealing algorithm costs a little more time but can obtain better result, and shows strong stability.
王保华、宋瑞、何世伟
铁路运输工程综合运输
集装箱中心站选址分配问题随机整数规划模型遗传模拟退火算法
ontainer central stationlocation and allocation problemstochastic integer programming modelgenetic simulated annealing algorithm
王保华,宋瑞,何世伟.随机环境下铁路枢纽内集装箱中心站选址分配优化模型及其遗传模拟退火算法[EB/OL].(2008-05-29)[2025-08-18].http://www.paper.edu.cn/releasepaper/content/200805-841.点此复制
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