A network flow model for clustering segments and minimizing total maintenance and rehabilitation cost

Because of shrinking budgets, transportation agencies are facing severe challenges in the preservation of deteriorating pavements. There is an urgent need to develop a methodology that minimizes maintenance and rehabilitation (M&R) cost. To minimize total network M&R cost of clustering pavement segments, we propose an integer programming model similar to an uncapacitated facility location problem (UFLP) that clusters pavement segments contiguously. Based on the properties of contiguous clustered pavement segments, we have transformed the clustering problem into an equivalent network flow problem in which each possible clustering corresponds to a path in the proposed acyclic network model. Our proposed shortest-path algorithm gives an optimal clustering of segments that can be calculated in a time polynomial to the number of segments. Computational experiments indicate our proposed network model and algorithm can efficiently deal with real-world spatial clustering problems.     

物流配送中心选址问题的0-1规划并行算法

建立物流配送中心选址问题的0-1混合整数规划模型,并结合目标排序法和改进的PSRS设计求解0-1规为1的并行算法。改进PSRS可将各个目标的验证任务进行均衡划分,并提交给各个处理器并行进行可行性验证,算法理论上具有接近处理器个数P的加速比。     

带时间窗的整车多式联运模型研究与解析*

限时性的快速货物运输已成为货运发展的趋势之一,多式联运则是较佳的运输组织形式。针对我国汽车消费市场飞速发展的大环境,以实现汽车整车时效性物流费用最小化为目的,建立了时间和容量约束下的多式联运方式0-1整数规划模型,根据模型的特点,设计了自然数编码的遗传算法,最后结合实例检验了模型和算法的有效性。     

Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview

In this paper, a novel multi-objective mathematical model is developed to solve a capacitated single-allocation hub location problem with a supply chain overview. Three mathematical models with various objective functions are developed. The objective functions are to minimize: (a) total transportation and installation costs, (b) weighted sum of service times in the hubs to produce and transfer commodities and the tardiness and earliness times of the flows including raw materials and finished goods, and (c) total greenhouse gas emitted by transportation modes and plants located in the hubs. To come closer to reality, some of the parameters of the proposed mathematical model are regarded as uncertain parameters, and a robust approach is used to solve the given problem. Furthermore, two methods, namely fuzzy multi-objective goal programming (FMOGP) and the Torabi and Hassini's (TH) method are used to solve the multi-objective mathematical model. Finally, the concluding part presents the comparison of the obtained results.     

一种自适应调整种群子代数量 与步长的优化算法——爆米花算法

提出了一种新的群体智能优化算法——爆米花算法。借鉴了烟花算法爆炸机制的优点,利用个体在寻优过程中适应度值的优劣来动态调整子代的数量,个体的适应度值越好,产生的子代数量越多,并且在该个体附近搜索的子代数量越多,以此控制局部搜索与全局搜索之间的平衡。还借鉴了粒子群优化算法的记忆机制,引入个体最优和全局最优来构造新的爆炸半径,使算法能够在寻优过程中动态地调整步长,并对全局最优进行高斯扰动,增加种群的多样性。实验结果表明:与其他优化算法(如蝙蝠算法、标准粒子群算法、烟花算法)相比,本文提出的爆米花算法总体性能更优。