生鲜电商配送的TDVRPTW研究: 基于经济成本与环境成本兼顾的视角

基于经济成本与环境成本兼顾的视角,研究时变网络下生鲜电商配送的带时间窗车辆路径问题(TDVRPTW),综合考虑车辆时变行驶速度、车辆油耗、碳排放、生鲜农产品的易腐易损性、客户时间窗与最低新鲜度限制等因素,设计跨时间段的路段行驶时间计算方法,引入农产品新鲜度度量函数与碳排放率度量函数.在此基础上,以经济成本与环境成本之和最小为目标构建具有最低新鲜度限制的TDVRPTW数学模型,并根据模型特点设计一种自适应改进蚁群算法求解.最后采用案例验证所提出方法能有效规避交通拥堵时间段、降低总配送成本、促进物流配送领域的节能减排.     

Lifted and Local Reachability Cuts for the Vehicle Routing Problem with Time Windows

The Vehicle Routing Problem with Time Windows (VRPTW) requires to design minimum cost routes for a fleet of vehicles with identical capacities to serve a set of customers within given time windows. Each customer must be visited exactly once and the load of a vehicle must not exceed its capacity.     

Optimal control of parallel queues with impatient customers

We consider a queueing system with a number of identical exponential servers. Each server has its own queue with unlimited capacity. The service discipline in each queue is first-come-first-served (FCFS). Customers arrive according to a state-dependent Poisson process with an arrival rate which is a non-increasing function of the number of customers in the system. Upon arrival, a customer must join a server’s queue according to a stationary state-dependent policy, where the state is taken to be the number of customers in servers’ queues. No jockeying among queues is allowed. Each arriving customer is limited to a generally distributed patience time after which it must depart the system and is considered lost. Two models of customer behavior are considered: deadlines until the beginning of service and deadlines until the end of service. We seek an optimal policy to assign an arriving customer to a server’s queue. We show that, when the distribution of customer impatience satisfies certain property, the policy of joining shortest queue (SQ) stochastically minimizes the number of lost customers during any finite interval in the long run. This property is shown to always hold for the case of deterministic customer impatience.     

The dynamic multiperiod vehicle routing problem with probabilistic information

This paper introduces the Dynamic Multiperiod Vehicle Routing Problem with Probabilistic Information, an extension of the Dynamic Multiperiod Vehicle Routing Problem in which, at each time period, the set of customers requiring a service in later time periods is unknown, but its probability distribution is available. Requests for service must be satisfied within a given time window that comprises several time periods of the planning horizon. We propose an adaptive service policy that aims at estimating the best time period to serve each request within its associated time window in order to reduce distribution costs. The effectiveness of this policy is compared with that of two alternative basic policies through a series of computational experiments.     

新颖的离散时间队列系统模型(英文)

带有正负顾客的连续时间单台服务器的队列系统得到了深入研究且已应用于多agent服务系统和计算机网络系统,而带有正负顾客的离散时间Geo/Geo/1队列研究在最近才出现。在拓展离散时间单台服务器Geo/Geo/1队列的基础上,提出了一个具有正负几何到达顾客的离散时间单台服务器GI/M/1队列模型,分析了队列静态长度分布和在RCH与RCE情况下的等待时间长度分布。     

On Walrasian Price of CPU Time

We study a Walrasian equilibrium model to determine the price of CPU time. The customers have jobs that require a given length of CPU slot allocation with their valuations dependent on the assigned time slots. The owner of the CPU processing time receives compensation for time slots sold to the customers, subject to the condition that the slots sold to a customer are those that the customer most desires, given the price structure for the time slots. We establish conditions for jobs to have Walrasian equilibrium, and obtain complexity results to determine the Walrasian equilibrium. In particular, the issues of excessive supply of CPU time and price dynamics are discussed under our model.     

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。     

客户可控制状态的银行客户账户管理系统

根据时间客户账户分为流动期和冻结期,在流动期内客户可进行账户查询、修改口令、取现、转账、冻结账户、退出管理全功能操作。在冻结期客户只能进行除取现和转账之外的半功能操作。如果账户处于流动期,客户可通过银行提供的客户账户管理终端设备随时将自己的账户转入冻结状态。如果客户希望自己的账户在t时处于活动状态,客户必须在t-T时之前通过银行客户服务终端将自己的账户解冻。     

Time constrained maximal covering salesman problem with weighted demands and partial coverage

In a routing framework, it may not be viable to visit every single customer separately due to resource limitations or efficiency concerns. In such cases, utilizing the notion of coverage; i.e., satisfying the demand of multiple customers by visiting a single customer location, may be advantageous. With this motivation, we study the time constrained maximal covering salesman problem (TCMCSP) in which the aim is to find a tour visiting a subset of customers so that the amount of demand covered within a limited time is maximized. We provide flow and cut formulations and derive valid inequalities. Since the connectivity constraints and the proposed valid inequalities are exponential in the size of the problem, we devise different branch-and-cut schemes. Computational experiments performed on a set of problem instances demonstrate the effectiveness of the proposed valid inequalities in terms of strengthening the linear relaxation bounds as well as speeding up the solution procedure. Moreover, the results indicate the superiority of using a branch-and-cut methodology over a flow-based formulation. Finally, we discuss the relation between the problem parameters and the structure of optimal solutions based on the results of our experiments.     

A template-based Tabu Search algorithm for the Consistent Vehicle Routing Problem

This paper presents a generic template-based solution framework and its application to the so-called Consistent Vehicle Routing Problem (ConVRP). The ConVRP is an NP-hard combinatorial optimization problem and involves the design of a set of minimum cost vehicle routes to service a set of customers with known demands over multiple days. Customers may receive service either once or with a predefined frequency; however frequent customers must receive consistent service, i.e., must be visited by the same driver over approximately the same time throughout the planning period. The proposed solution framework adopts a two-level master-slave decomposition scheme. Initially, a master template route schedule is constructed in an effort to determine the service sequence and assignment of frequent customers to vehicles. On return, the master template is used as the basis to design the actual vehicle routes and service schedules for both frequent and non-frequent customers over multiple days. To this end, a Tabu Search improvement method is employed that operates on a dual mode basis and modifies both the template routes and the actual daily schedules in a sequential fashion. Computational experiments on benchmark data sets illustrate the competitiveness of the proposed approach compared to existing results.