基于遗传算法的多式联运应急管理研究

实现在多式联运中实现运输时间和运输费用的最小化,多式联运运输方式选择问题直接关系到货物运输的费用和时间。首先分析了多式联运运输方式选择多目标优化问题的数学模型及虚拟运输网络图;其次,用遗传算法来解决多目标化问题,给出了染色体编码,遗传算子的设计,适应度函数定义;最后通过示例来演示,通过遗传算法来解决多式联运多目标优化的问题。实验表明,将此算法用于多式联运应急管理与传统算法相比,能加速进化速度和多角度寻优能力,提高应急决策。     

区间数型多式联运路线优化问题的混合遗传算法*

多式联运路线优化问题直接关系到货物运输的费用、时间和运输质量。首先分析了多式联运路线优化问题的数学模型及虚拟运输网络图;其次,将区间数排序的思想引入适应度函数的设计中,提出了一种求解区间数型多式联运路线优化问题的混合型遗传算法,给出了染色体编码、遗传算子设计、约束判断与调整及群体多样性控制的方法;最后用示例对算法的有效性进行了验证,算法的提出可为多式联运经营者的决策提供数据参考。     

多式联运运输问题的混合遗传算法

多式联运运输方式的选择关系到货物运输所需费用、时间等。该文对需经过多式联运过程的运输问题进行了研究。首先分析了多式联运运输问题的数学模型;其次通过引入关于运输量及运输方式的混合编码,结合两种混合遗传算子,提出了一种求解多式联运运输问题的混合遗传算法;最后用数值例子对算法的有效性进行了验证。     

求解多式联运运输方案选择的混合蛙跳算法

考虑多式联运路径上运输方案选择问题,建立了降低运输总成本和缩短运输总时间的多目标数学模型,通过加权目标函数,设计新的最差青蛙更新方式,提出一种基于字符编码方式的混合蛙跳算法,最后用示例验证了算法的有效性。     

多式联运中运输方式与运输路径集成优化模型研究*

运输方式和运输路径选择问题是影响多式联运时间和费用的关键问题,直接影响承运人和客户的利益。依据运输方式选择和运输路径优化的关系特点,采用主从混合智能启发式方法,构建了运输方式选择和运输路径优化集成模型,给出了粒子群—蚁群双层优化算法求解方案,解决了运输网络多节点、多方式、多路径的集成优化问题。实验结果表明,该方案优于蚁群算法和遗传算法。     

和声搜索算法优化多时间窗多式联运运输方案

针对多式联运运输路径上运输方式选择问题,考虑运输网络中多个节点存在服务时间窗的限制,建立了多个中间节点带软时间窗的多式联运运输方案优化模型,设计了一种基于字符编码方式的和声搜索算法,该算法采用新的和声生成方式及微调方式。仿真实例表明,所提算法与贪婪算法相比能获得具有更优运输总成本及不准点时间的运输方案。     

软时间窗多式联运4PL路径问题的改进乌鸦算法

针对机械故障、天气状况等随机因素在运输过程中易对各种运输方式造成影响,研究更具有实际意义的带软时间窗的多式联运4PL路径问题。在软时间窗约束下,以总运输费用最小为目标,建立带有软时间窗的多式联运4PL路径优化模型。设计基于天牛须搜索思想和莱维飞行机制的乌鸦搜索算法对模型进行求解,采用田口方法确定算法最优参数组合,与其他算法进行对比分析,实验结果表明改进算法具有更好的求解效果和稳定性。通过数据分析,采用多式联运的运输组织形式,相比单一3PL服务商的单一运输方式,能够有效降低总运输费用;对于客户不同的软时间窗要求,4PL集成商会确定不同的最优运送方案,并证实软时间窗的研究更具有实际意义。     

集装箱多式联运运输组合优化的建模研究

为适应经济全球化对运输效率的发展要求,针对集装箱多式联运系统中各种运输线路以及运输方式的组合模式进行了研究。分析了集装箱多式联运过程中影响运输时间以及运输费用的因素,详细描述了多式联运网络,建立了以时间为约束的集装箱多式联运系统运输费用组合优化模型,对集装箱多式联运过程中各种运输线路和运输方式的组合模式进行了定量分析,通过对1个算例进行仿真证明了模型的有效性。     

长江集装箱多式联运路径优化动态规划算法

考虑不同货流运输需求及其时间窗约束,研究长江集装箱多式联运路径优化问题,以运输总费用最小为目标构建数学模型,提出基于深度优先遍历的两阶段多式联运路径优化动态规划算法.第一阶段通过网络遍历提供所有货流可行路径方案集,作为第二阶段的输入完成多式联运路径优化.算例研究结果表明,动态规划算法可实现模型有效求解,适当调整时间窗约束或班次信息可降低多式联运费用.     

规模经济对多式联运网络成本最优模型的影响

在多式联运网路中,建立了一个基于规模经济的运输方式与车辆运力集成选择优化的模型。根据研究问题,规模经济体现在多式联运运输网络的货运量、运输距离和车辆运力上。通过多式联运运输网络成本最小化问题来表达该模型。用遗传算法可以找到合理的路线、运输方式和车辆运力。并通过一个案例对两种不同货物运输需求的情景进行了检验。启发式遗传算法的优化结果显示了规模经济在两种需求能力下,如何影响各种运输方式的物流总成本。此外,得出了两种情况下车辆安排的策略。     

多目标运输应急管理问题研究

该文首先分析了多目标运输方式选择多目标优化问题的数学模型及虚拟传输网络图,其次对传统遗传算法的染色体编码方式、交叉算子、变异算子进行了重新设计,并且重新定义了适应度函数,最后通过实例分析验证了改进的遗传算法解决多目标运输问题的优越性。     

Spanning tree-based genetic algorithm for bicriteria transportation problem

In this paper, we present a new approach which is spanning tree-based genetic algorithm for bicriteria transportation problem. The transportation problem have the special data structure in solution characterized as a spanning tree. In encoding transportation problem, we introduce one of node encoding which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation was designed based on this encoding. And we designed the criterion that chromosome always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy and roulette wheel selection is used. Numerical experiments will be shown the effectiveness and efficiency of the proposed algorithm.     

Solving exclusionary side constrained transportation problem by using a hybrid spanning tree-based genetic algorithm

In real life applications we often have the following problem: How to find the reasonable assignment strategy to satisfy the source and destination requirement without shipping goods from any pairs of prohibited sources simultaneously to the same destination so that the total cost can be minimized. This kind of problem is known as the transportation problem with exclusionary side constraint (escTP). Since this problem is one of nonlinear programming models, it is impossible to solve this problem using a traditional linear programming software package (i.e., LINDO). In this paper, an evolutionary algorithm based on a genetic algorithm approach is proposed to solve it. We adopt a Prüfer number to represent the candidate solution to the problem and design the feasibility of the chromosome. Moreover, to handle the infeasible chromosome, here we also propose the repairing procedure. In order to improve the performance of the genetic algorithm, the fuzzy logic controller (FLC) is used to dynamically control the genetic operators. Comparisons with other conventional methods and the spanning tree-based genetic algorithm (st-GA) are presented and the results show the proposed approach to be better as a whole.     

Optimisation of partner selection and collaborative transportation scheduling in Virtual Enterprises using GA

Partner selection and transportation scheduling are critical to the success of a Virtual Enterprise. Collaborative transportation is a promising strategy that can help many enterprises survive and thrive in today’s highly competitive market. To help decision makers establish and operate Virtual Enterprises more effectively, an innovative decision support system is proposed in this paper. First, new model for integration of partner selection and collaborative transportation scheduling in Virtual Enterprises is developed. This integrated optimisation problem is very dynamic in nature and it is required to optimise a number of interlinked sub-problems at the same time. Then, a novel Genetic Algorithm with a unique dynamic chromosome representation and genetic operations is developed to find an optimal solution to the integrated problem. The effectiveness of the proposed approach is demonstrated in a representative case study.     

Addressing a nonlinear fixed-charge transportation problem using a spanning tree-based genetic algorithm

In this paper, we consider the fixed-charge transportation problem (FCTP) in which a fixed cost, sometimes called a setup cost, is incurred if another related variable assumes a nonzero value. To tackle such an NP-hard problem, there are several genetic algorithms based on spanning tree and Prüfer number representation. Contrary to the findings in previous works, considering the genetic algorithm (GA) based on spanning tree, we present a pioneer method to design a chromosome that does not need a repairing procedure for feasibility, i.e. all the produced chromosomes are feasible. Also, we correct the procedure provided in previous works, which designs transportation tree with feasible chromosomes. We show the previous procedure does not produce any transportation tree in some situations. Besides, some new crossover and mutation operators are developed and used in this work. Due to the significant role of crossover and mutation operators on the algorithm’s quality, the operators and parameters need to be accurately calibrated to ensure the best performance. For this purpose, various problem sizes are generated at random and then a robust calibration is applied to the parameters using the Taguchi method. In addition, two problems with different sizes are solved to evaluate the performance of the presented algorithm and to compare that performance with LINGO and also with the solution presented in previous work.     

基于免疫克隆选择算法的固定费用运输问题优化

固定费用运输问题（fcTP）是物流运输中的高级问题,属于NP难题,较难得到最优解。针对现有方法存在的不足,提出了基于免疫克隆选择的fcTP求解算法。在该算法中,抗体采用矩阵形式编码,初始抗体群随机产生,通过迭代进行克隆选择、克隆抑制、基因变异等操作,对潜在解空间进行寻优搜索。实验结果表明,对于fcTP的优化,免疫克隆选择算法能够快速收敛于全局最优解,克服了遗传算法收敛速度慢和容易陷入局部最优的缺点。