乘用车物流运输装载模型研究

对乘用车物流运输计划问题进行了研究,首先,建立了以单辆轿运车实载率最大为目标的单车最佳装载方案模型,以该方案配送乘用车辆直到运输任务完成以获得初始轿运车数,其次以空载率最小为目标设计了数量调整模型对初始轿运车数进行优化,得到最优装载方案模型.基于上述优化模型计算最小的轿运车数,建立在轿运车数量限制下的最短行驶里程模型,选择总行驶里程最小的装载方案作为最优的配送方案.最后建立基于路径的物流运输装载模型,运用Floyd算法计算任意起讫点间的最短路径,设计了一种全局搜索算法得到一种合理的配送方案,并以空载率最小为目标对方案进行优化.     

第三方库存-路线问题的模型及混合遗传算法设计

根据第三方库存-路线问题的特点,以车辆租赁费用和运行费用之和为目标函数,不限制客户每次的配送量小于车辆容量,建立了满载运输和非满载运输混合的整数规划模型.针对第三方库存-路线问题的复杂性,本文设计嵌入禁忌搜索的遗传算法来同时决策库存和路线问题.首先对配送间隔进行编码,然后用禁忌搜索法计算每天需要配送的车辆路线问题.最后与其下界值进行比较,结果表明该算法是一个有效的算法,不但第三方能取得较低的运营总成本和较高的车辆利用率,而且也能为客户节约库存空间.     

考虑车辆限速区间的危险品运输网络优化

由于危险品在运输过程中存在极大的危害性,为了降低危险品运输风险,政府可以通过对不同路段设置不同的限速区间来引导危险品运输车辆的路径选择,从而导致不同的运输网络总风险和鲁棒成本。首先基于车辆限速区间的方法,构建了危险品运输网络优化的双层规划模型,上层规划以最大运输网络总风险值最小化为目标,下层规划以危险品运输企业的鲁棒成本最小化为目标;然后,设计了粒子群优化算法求解了该模型;最后,通过两个算例验证了模型和算法的有效性。计算结果表明政府部门运用车辆限速区间的方法不仅能够非常有效地降低危险品运输网络总风险,而且更具有鲁棒性和现实可操作性。     

综合考虑碳排放与运输费用的配送路径优化

针对日益严峻的环境问题,在传统的物流配送路径优化过程中,进行碳排放量计算,并转换成相应的经济效益,形成一个综合考虑碳排放和运输费用的配送路径优化模型并通过遗传算法进行求解.以厦门市某物流配送企业为例进行初步应用研究.结果表明:传统的配送路径安排中存在很大的碳排放改善空间;综合考虑碳排放和运输费用的车辆路径问题在有效实现绿色物流的同时能降低运输成本.     

基于遗传算法的成品油二次配送车辆路径问题研究

研究了加油站需求已知前提下带时间窗的具有满隔舱运输约束的多车型成品油二次配送车辆路径问题.首先以总费用极小化为目标建立了具有满载运输约束的多车型成品油二次配送车辆路径问题的混合整数规划模型,其中总费用包括动用车辆的固定费用、车辆的运输费用、以及不满足时间窗约束的等待成本和惩罚成本等.然后基于成品油二次配送车辆路径问题的特点设计了求解模型的遗传算法,通过对车辆和加油站分别采用自然数编码方式、解码时考虑约束条件等策略有效避免了不可行解的产生.最后利用一个实际案例进行了模拟计算,结果显示根据方法得到的配送方案明显优于实际中凭经验得到的配送方案,总配送成本大约降低了9%.模型和算法为制订成品油二次配送方案提供了决策依据.     

满意度优化运输问题

传统运输问题只考虑配送方案的效率, 而不考虑参与者对配送方案的满意度. 通过引入参与者对配送方案的满意度这一概念, 提出了满意度优化运输问题, 构建了以最大化相对公平为目标的满意度优化运输模型, 并证明了: (1) 当运输问题的可行域不空时, 新模型的解集非空; (2) 从满意度的角度来看, 新模型的解是唯一的. 另外, 还给出了新模型的求解方法. 研究结果进一步丰富了运输问题的类型, 可为解决其他类型运输问题提供借鉴.     

基于遗传算法的汽车整车销售物流配送路径优化

为了优化汽车整车销售物流配送网络,提高配送服务质量,构建了以配送费用最小为目标的带时间窗的整车配送路径优化模型,采用改进遗传算法对模型进行求解,结合上汽通用五菱公司的配送实例,对其整车销售物流配送路径进行研究,并将改进遗传算法所确定的优化路径、节约里程法的优化路径、企业实际的配送路径进行比较,改进遗传算法确定的最优路径其配送费用比其它两种路径的配送费用降低了5.5%和8.9%,研究结果可以为企业确定经济、合理的配送路径提供参考.     

区间数型多目标联运路线优化问题的模型与算法

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及属性值为区间数的多属性决策方法引入适应度函数的设计中,提出了一种求解区间数型联合运输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、适应度函数定义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.     

基于海空协同的群岛救援方案优化模型及算法

针对边远群岛的物资供给受突发事件影响可能出现中断,需要开展紧急救援的实际情况,以中心岛屿为救援出发地,采用海空协同运输方式,以选择救援路线和分配救援物资批量为优化内容,对中心岛屿周边各岛救援用时最短为目标,建立了考虑海空协同的群岛应急救援模型。根据所建模型的特点,对基于运输点划分的遗传算法(PB-GA)进行进一步的改进,提出一种能够同时考虑两种运输方式、多批次运输的双层搜索遗传算法进行求解。最后,以南海群岛开展紧急救援为算例进行了优化分析。采用不同算法分别进行比较后显示,本文算法在优化结果、运算时间等方面均更优,从而验证了所建模型和算法的合理性与有效性。本文研究为制定群岛海空联合救援的应急预案提供了分析方法。     

循环取货模式下的多车场协同运输问题研究

分析循环取货模式和协同运输问题的关联性,提出了供需节点分离下的多车场一体化协同运输路线优化问题,考虑运输价值和运输成本,引入节点-弧流量的概念,通过比较流量大小确定节点集合,构建了问题的多供应点、多需求点运输模型.考虑取货的单向性和送货的闭合性,构造了求解模型的两阶段算法,运用动态规划的递推解法确定取货最优路线,然后基于余弦定理的几何法求解出发点和返回点不相同的送货路径优化问题,最后通过算例分析,说明了模型的合理性和算法的有效性.     

Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach

Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra’s algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm.

     

On the enrouting protocol problem under uncertainty

In this paper we present a two-stage stochastic mixed 0–1 dynamic multicommodity model and algorithm for determining the enrouting protocol in the telecommunications network under uncertainty. Given the network connectivity, node processing and buffer and arc flow capacity, the aim is to determine the outgoing arc for the information flow reaching a given node for each destination terminal node (i.e., obtaining the route to be followed by the information flow from each origin terminal node to each destination terminal node). The origin–destination (O–D) flow matrix is given by the number of information packets to be sent from the origin terminal nodes to the destination terminal nodes along a given time horizon, i.e., a call scale. The uncertainty in the O–D flow matrix is treated via a scenario tree approach. The main goal is to minimize a composite function of the expected lost information, a penalization of the deviation from the FIFO strategy on the information flow entering the network, and the expected number of nodes visited by the information packets. A mixture of an enrouting arc generation scheme and a genetic algorithm for obtaining the enrouting protocols over the scenarios is presented. The tool presented in this paper could be used for simulating the enrouting protocols to analyze the saturation of the network, but it has a time constraint for real time operation. Faster algorithms are needed to define the routing tables during the operation stage. Computational experience is reported.     

智能网联下无人车配送路径优化

结合智能网联无人车实时信息共享与路径选择的特点,研究其配送路径优化问题。通过引进关键点更新策略,制定路径预规划阶段和路径实时调整阶段无人车路径选择策略,提出智能网联环境下基于实时交通信息的车辆路径问题两阶段模型。其中,路径预规划阶段模型确定初始路径与每辆车服务的客户点,路径实时调整阶段模型对每辆车的路径实时调整。对于该优化模型设计遗传算法进行求解,并通过算例验证了模型与算法的可行性。研究结果表明,本文构建的无人车配送优化模型,有效的结合了无人车实时通信与路径选择的特点,节省了无人车配送时间。研究对于无人车在第三方物流配送领域的推广应用具有一定的探索意义。     

A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge

This paper considers a two-stage distribution problem of a supply chain that is associated with a fixed charge. Two kinds of cost are involved in this problem: a continuous cost that linearly increases with the amount transported between a source and a destination, and secondly, a fixed charge, that incurs whenever there exists a transportation of a non-zero quantity between a source and a destination. The objective criterion is the minimisation of the total cost of distribution. A genetic algorithm (GA) that belongs to evolutionary search heuristics is proposed and illustrated. The proposed methodology is evaluated for its solution quality by comparing it with the approximate and lower bound solutions. Thus, the comparison reveals that the GA generates better solution than the approximation method and is capable of providing solution either equal or closer to the lower bound solution of the problem.     

Network design problem with relays: A genetic algorithm with a path-based crossover and a set covering formulation

The network design problem with relays arises in telecommunications and distribution systems where the payload must be reprocessed at intermediate stations called relays on the route from its origin to its destination. In fiber-optic networks, for example, optical signals may be regenerated several times to overcome signal degradation because of attenuation and other factors. Given a network and a set of commodities, the network design problem with relays involves selecting network edges, determining a route for each commodity, and locating relays to minimize the network design cost. This paper presents a new formulation to the problem based on set covering constraints. The new formulation is used to design a genetic algorithm with a specialized crossover/mutation operator which generates a feasible path for each commodity, and the locations of relays on these paths are determined by solving the corresponding set covering problem. Computational experiments show that the proposed approach can outperform other approaches, particularly on large size problems.     

考虑路网可靠性和空间公平性的次优拥挤收费模型

根据随机路网环境下出行者规避风险的路径选择行为,提出了一种考虑路网可靠性和空间公平性的次优拥挤收费双层规划模型。其中,上层模型以具有空间公平性约束条件下最大化路网的社会福利为目标,下层模型是实施拥挤收费条件下考虑行程时间可靠性的弹性需求用户平衡模型。鉴于双层规划模型的复杂性,设计了基于遗传算法和FrankWolfe算法的组合式算法来求解提出的模型。算例结果表明:考虑行程时间可靠性的次优拥挤收费会产生不同于传统次优拥挤收费的平衡流量分布模式,表明出行者的路径选择行为对拥挤收费结果会产生直接影响;此外,算例结果还说明遗传算法对参数设置具有很强的鲁棒性。     

Improved modeling and solution methods for the multi-resource routing problem

This paper presents modeling and solution method improvements for the Multi-Resource Routing Problem (MRRP) with flexible tasks. The MRRP with flexible tasks is used to model routing and scheduling problems for intermodal drayage operations in which two resources (tractors and trailers) perform tasks to transport loaded and empty equipment. Tasks may be either well defined, in which both the origin and the destination of a movement are given, or flexible, in which the origin or the destination is chosen by the model. This paper proposes methods to effectively manage the number of options considered for flexible tasks (either feasible origins for a known destination or feasible destinations for a known origin). This modeling change generates sufficient options to allow for low-cost solutions while maintaining reasonable computational effort. We also propose a new solution method that uses randomized route generation. Computational results from test cases show that these changes improve the quality of solutions by at least 5% in the test cases as compared to methods from previous studies.     

基于一体化的多层次公交线网优化研究

探讨一体化公交系统下多模式、多层次公交网络的优化方法,采用双层规划模型对公交线网进行一体化规划,并利用遗传算法和公交线路的规划模型确定最优线路集合.通过对交通网络的分析,确定公交网络的分布方法.最后通过实例以及方案的对比分析,获得了较好的优化结果.