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。     

多式联运分运人选择的模型和算法

在对多式联运分运人选择问题分析的基础上,运用图论技术构建了基于联运分运人选择的多式联运网络。综合考虑了运输的规模经济性、时效性、风险性和联运联盟的稳定性,建立了基于综合运输成本最小、运输风险最小和合作强度最大的多目标优化选择模型。通过主要目标法,将模型转化为单目标模型。借鉴生物免疫原理,设计了基于克隆增扩的人工免疫算法对问题进行求解。最后通过算例对方法进行了验证。     

碳排放限制下的冷藏集装箱多式联运路径优化

为了降低碳排放限制下的冷藏集装箱多式联运成本,实现节能减排的目的,高效的路径选择至关重要.该文基于碳排放限制的视角,针对多式联运网络中铁路和水路运输具有发班时间限制,以及冷藏集装箱需要考虑制冷费用、货损货差的特点,建立了在碳排放限制下以总成本最低为目标的优化模型.构建总成本时不仅考虑了运输费用和转运费用,还考虑了受发班时间影响而动态变化的冷藏费用和货损费用.设计了遗传算法求解,并进行了算例分析.结果表明:通过该模型和算法,可根据决策者的要求快速地选出成本最少的运输方案,为决策者提供决策支持.     

基于新风险度量方式的危险品车辆路径双目标优化模型

针对危险品车辆路径问题中车辆访问多个需求点的特性,在风险度量方式上考虑了运输过程中车辆载重量变化,建立了最小化总运输距离以及最小化总运输风险的双目标优化模型.采用改进的蚁群算法对模型进行求解并获得优化问题的非支配解,数值实验说明改进的风险度量方式更适合于危险化学品车辆路径问题,改进的蚁群算法能够有效率地对模型进行求解.     

基于均值-方差模型的爆炸品运输策略选择研究

对于有害物品运输问题,已有的研究集中在考虑路径风险情形下的路径选择问题,对路径既定时运输策略的选择问题涉及较少.针对一定量爆炸品应分成多少次运输的问题,分析了路径既定情形下采用均值-方差模型度量运输风险时,决策者的最优选择行为.结果表明,决策者越厌恶大事故,其越倾向于选择小载重量多次数的运输策略.     

危险品物流中的多目标定位-路径问题

随着经济的增长,危险化学品需求呈日益增长的趋势,其安全储运问题已成为政府和企业关注的焦点。本文通过建立危险品物流中多配送中心、多目标定位—路径问题优化模型,并设计混合遗传算法对模型进行求解,最后通过算例验证该模型和算法的可行性和有效性。结果表明:与只考虑系统总成本的结果相比,考虑运输风险的结果能够以较小的风险和成本、较少的人力资源配备实现危险品的储运;所设计混合遗传算法具有良好的性能。     

考虑订单合并和货物转运的多式联运路径优化研究

为了获得运输的规模经济效应,本文研究了一种考虑订单合并和货物转运的零担多式联运路径优化问题。首先,以总运输成本为目标函数,以网络中的运输工具容量、可以提供的运输工具最大数量、运输工具服务的关闭时间以及订单时间窗为约束,构建混合整数规划模型,在模型中允许多个订单进行合并运输并考虑运输过程中的转运成本。其次,由于多式联运路径优化问题是典型的NP-hard问题,为了快速求解该模型,开发了一种可以快速为该问题提供近似最优解和下界的列生成启发式算法。最后,生成并测试了大量算例,结果表明所开发的列生成启发式算法可以在较短的时间内提供高质量的近似最优解。文章所构建的模型和开发的列生成启发式算法可以为零担自营多式联运物流企业提供高效的决策支持。     

空铁联运网络的模型构建与算法设计

针对空铁联运网络具体联运路径的设计问题,借鉴枢纽航线网络p-枢纽中位问题的研究思想,将非枢纽城市间可以直航考虑进去,以联运网络总成本最低为目标函数,构建了允许直航的空铁联运网络混合整数规划模型,并设计了基于遍历搜索的最短路算法来求解模型.最后选取样本城市对模型和算法进行算例分析,给出了不同参数组合下的最优目标值和具体联运路径,设计了中国14个城市的空铁联运网络.算例结果表明联运总成本大小和联运路径的数目与枢纽数目m、折扣系数ρ紧密相关:m越大,ρ越小,联运总成本越小,联运路径数目越多;反之亦然.     

重大事故规避的危险品运输车辆路径优化研究

基于重大事故规避的思想,建立以最大事故后果最小及运输成本最小为双目标,且事故后果基于实时装载量的危险品运输车辆路径优化模型。基于ε-约束法,设计可求得帕累托最优解的精确算法,该算法包含通过性质求ε下界、规避被支配解的预处理及不可行路径禁止约束3处改进。进一步设计处理大规模问题的多项式时间近似算法,并分析了算法的近似比。最后通过算例对模型和算法进行测试,并通过出灵敏度分析给出管理启示。     

路径既定条件下有害物品运输策略选择研究

对有害物品运输问题的研究,大多集中在考虑路径风险情形下的路径选择问题方面,对路径既定条件下运输策略的选择问题尚未涉及.利用概率分析方法,比较了路径既定条件下一定量有害物品选择一次运输策略和选择多次运输策略在发生事故的可能性、路径风险两方面的差异.结果发现,一次运输情形下,发生事故的可能性较小,路径风险也较小,要使可能损失赔偿与运输成本之和最小,决策者应尽可能选择大载重量运输车辆进行有害物品运输.     

Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks

This paper illustrates a dynamic model of conditional value-at-risk (CVaR) measure for risk assessment and mitigation of hazardous material transportation in supply chain networks. The well-established market risk measure, CVaR, which is commonly used by financial institutions for portfolio optimizations, is investigated. In contrast to previous works, we consider CVaR as the main objective in the optimization of hazardous material (hazmat) transportation network. In addition to CVaR minimization and route planning of a supply chain network, the time scheduling of hazmat shipments is imposed and considered in the present study. Pertaining to the general dynamic risk model, we analyzed several scenarios involving a variety of hazmats and time schedules with respect to optimal route selection and CVaR minimization. A solution algorithm is then proposed for solving the model, with verifications made using numerical examples and sensitivity analysis.     

不同风险偏好下虚拟企业风险规划研究

针对虚拟企业风险规划问题,在分析其各种风险具有随机性的特点的基础上,运用随机规划理论,分别建立风险规划的期望值模型和机会约束规划模型来描述决策者在不同风险偏好下的决策行为。针对所建立的模型,分别设计了基于蒙特卡罗模拟的粒子群优化算法、遗传算法和蚁群算法对其进行求解。仿真分析表明期望值模型较好地描述了风险中性决策者的决策行为,机会约束规划模型随着其偏好系数取值的不同描述了不同风险偏好(风险厌恶、风险中性、风险爱好)决策者的决策行为。通过对三种算法仿真结果的比较分析,表明基于蒙特卡罗模拟的粒子群优化算法在寻优能力、稳定性和收敛速度等方面优于其余两种算法,是解决此类风险规划问题的有效手段。     

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。     

Integrated fleet mix and routing decision for hazmat transportation: A developing country perspective

In developing countries, truck purchase cost is the dominant criteria for fleet acquisition-related decisions. However, we contend that other cost factors such as loss due to the number of en route truck stoppages based on a truck type and recovery cost associated with a route choice decision, should also be considered for deciding the fleet mix and minimizing the overall costs for long-haul shipments. The resulting non-linear model, with integer variables for the number and type of trucks, and the route choices, is solved via genetic algorithm. Using real data from a bulk liquid hazmat transporter, the trade-offs between the cost of travel, loss due to number of truck stoppages, and the long-term recovery cost associated with the risk of exposure due to a hazmat carrier accident are discussed. The numerical experiments show that when factors related to public safety and truck stoppages are taken into account for transportation, the lowest total cost and optimal route choice do not align with the cheapest truck type option; rather, the optimal solution corresponds to another truck type and route with total costs significantly less than the total costs associated with the cheapest truck type. Our model challenges the current truck purchasing strategy adopted in developing countries using the cheapest truck criteria.     

Incorporating transfer reliability into equilibrium analysis of railway passenger flow

Passenger’s transfer route choice behavior is one of the prominent research topics in the field of railway transportation. Existing traffic assignment approaches do not properly account for passenger’s expectation for transfer reliability. In this study, the transfer reliability is explicitly defined and a multi-class user equilibrium model is established, given which passengers choose the minimal-cost path based on their expected reliability thresholds. In particular, a path-based traffic assignment algorithm which combines a k-shortest path algorithm and the method of successive averages is proposed. The validity of the proposed approach is verified by an illustrative example. Using the proposed modeling approach, it is possible to determine the passenger’s collective route choice behavior based on the user equilibrium pattern. Moreover, the railway timetables can be evaluated and optimized based on the cost-based level of service estimation.     

Intermodal shipments as recourse in logistics disruptions

This research deals with the flexibility of transportation systems when faced with disruptions. A network optimization model is used to investigate the feasibility of using intermodal shipments as recourse to disruptions in a transportation network. In a study of US interstate highways and intermodal rail networks, performance of over-the-road and intermodal shipments is compared under different disruption scenarios. The results show that the topology of US transportation system and locations of existing intermodal terminals provide required path redundancies and a strategic benefit for intermodal shipments to bypass disrupted regions with lower costs and competitive shipment times compared to rerouted road shipments.     

Hub location–allocation in intermodal logistic networks

Within the context of intermodal logistics, the design of transportation networks becomes more complex than it is for single mode logistics. In an intermodal network, the respective modes are characterized by the transportation cost structure, modal connectivity, availability of transfer points and service time performance. These characteristics suggest the level of complexity involved in designing intermodal logistics networks. This research develops a mathematical model using the multiple-allocation p-hub median approach. The model encompasses the dynamics of individual modes of transportation through transportation costs, modal connectivity costs, and fixed location costs under service time requirements. A tabu search meta-heuristic is used to solve large size (100 node) problems. The solutions obtained using this meta-heuristic are compared with tight lower bounds developed using a Lagrangian relaxation approach. An experimental study evaluates the performance of the intermodal logistics networks and explores the effects and interactions of several factors on the design of intermodal hub networks subject to service time requirements.     

基于GMCR-NPAWLAK混合模型的冲突分析研究

冲突分析图模型中,决策者的态度只有肯定和否定两种,实际问题中往往不止两种;新PAWLAK冲突模型(NPAWLAK模型)将冲突系统中决策者的三种态度扩展到决策争端的三种程度,符合实际情况,因而研究冲突系统中决策者的偏好排序和全局可行方案对决策者的策略选择具有重要意义。本文在NPAWLAK模型的基础上,引入冲突分析图模型理论(GMCR),提出GMCR-NPAWLAK冲突分析混合模型。该混合模型首先拓展和改进的策略优先排序法,实现了冲突系统中各决策者的客观偏好排序;同时,模型给出了全局可行方案的算法,该算法依据决策者的偏好排序分析结果找出系统的全局可行方案。最后,本文以某企业劳资关系的NPAWLAK冲突为例,对冲突系统进行建模和偏好分析,得到了冲突各方的偏好序列和全局可行方案,同时验证了混合模型的有效性。