考虑均衡性的不确定时间车辆调度问题研究

本文针对车辆调度实际运行过程中时间的不确定性问题,提出了包含时间窗口、车辆容量约束的配送服务线路随机规划模型,以最小化调用的车辆数目和运行距离,降低顾客的不满意度并且尽可能保证每条路线的均衡性。结合模型,给出了基于禁忌搜索的混合启发式算法,并且生成多个算例,依据算例结果说明模型和算法优越性,同时说明可以在不降低顾客满意度和不提高总运输成本的基础上,降低各条线路之间的时间差异。     

多行程带时间窗口的车辆调度问题研究北大核心

为了提高车辆的使用率,企业往往会安排车辆在单位周期内,执行多次配送任务.为了研究多行程带时间窗口的车辆配送(VRPTW)中的车辆调度问题.模型以车辆的固定费用、车辆行驶过程中的等待费用、司机的工作小时费最小为目标,同时也融合了司机在执行不同路线时,由于熟悉的过程所弓I起的费用.通过对路线的时间窗口性质的分析,建立了调度问题的模型.     

多行程带时间窗口的车辆调度问题研究

为了提高车辆的使用率,企业往往会安排车辆在单位周期内,执行多次配送任务.为了研究多行程带时间窗口的车辆配送(VRPTW)中的车辆调度问题.模型以车辆的固定费用、车辆行驶过程中的等待费用、司机的工作小时费最小为目标,同时也融合了司机在执行不同路线时,由于熟悉的过程所弓I起的费用.通过对路线的时间窗口性质的分析,建立了调度问题的模型.     

一类新的车辆路径问题及其两阶段算法

本文结合汽车零部件第三方物流业的实际背景,提出了一类新的车辆路径问题,它是一种带时间窗约束的分车运输同时收发车辆路径问题(简称SVRPSPDTW).接着给出了问题的模型,并提出求解问题的启发式算法：两阶段算法. 最后在改进的Solomn的算例的基础上,进行了数值试验.     

带缓冲库存的物流直通配送运作时间优化

针对直通配送需要尽可能缩短配送中心内运作时间的问题.利用总运作时间与完成所有操作所需时间最长的出站口的操作时间等价的关系,构建了以最小化总运作时间为目标的进出车辆分配问题模型,同时解决了进出车辆的分配问题和分配到同一进(出)站口的车辆的排序问题.利用遗传算法进行实验,结果表明模型能够对配送中心内的运作时间进行有效优化,且在时间优化方面,与以最小化配送中心内搬运工具移动距离为目标相比更为有效.     

两类加工时间是一般函数的单机排序问题

考虑了两类有一般加工时间函数的排序问题. 工件的加工时间分别为基本加工时间与开工时间函数、位置函数的和. 对加工时间依赖开工时间的模型,证明了一定条件下极小化最大完工时间和极小化总完工时间是多项式可解的. 对加工时间依赖开工位置的模型,给出极小化最大完工时间和极小化总完工时间的最优序,同时证明了极小化加权总完工时间的一个最优排序性质并给出一个贪婪算法.     

考虑城市住房分布的二维连续型动态交通分配模型及其污染物排放估计

将城市交通路网抽象为二维连续的平面,提出一个考虑城市住房分布的二维连续型动态交通分配模型,并估计了城市交通污染物排放水平.该模型中,住房分布影响交通需求,出行者根据用户最优准则选择出发时间和路径,可以用于模拟城市交通运行的动态演化过程,得到密度、速度和流量等物理量.采用基于车辆速度和加速度的微观污染物排放模型VT-micro估计城市交通CO2动态排放水平.基于三角网格,采用有限体积法、投影算法和相继平均法进行数值求解,给出一个数值算例验证了模型和算法的有效性.     

带时间窗口的超市物流配送不确定规划模型

主要研究了不确定环境下带时间窗口的超市物流配送问题。假设超市的日需求量是不确定变量,在配送过程中车辆的行驶时间也为不确定变量。为了最小化配送过程中车辆行驶时间,建立了不确定机会约束模型。然后应用不确定变量的运算法则对模型进行等价转化,并为求解模型设计了算法。最后给出了一个数值算例来说明模型的实际应用。     

带时间窗车辆路径问题的改进节约算法

对节约算法进行了改进,并利用改进的节约算法解决了带时间窗约束的多类型车辆路径问题．首先讨论了带时间窗约束的单类型车辆路径问题,给出其模型,并归纳了几种通过改进传统的节约算法得到的用于求解带有具体约束车辆路径问题的改进节约算法．     

多时间窗车辆路径问题的智能水滴算法

研究了多时间窗车辆路径问题,考虑了车容量、多个硬时间窗限制等约束条件,以动用车辆的固定成本和车辆运行成本之和最小为目标,建立了整数线性规划模型。根据智能水滴算法的基本原理,设计了求解多时间窗车辆路径问题的快速算法,利用具体实例进行了模拟计算,并与遗传算法的计算结果进行了对比分析,结果显示,利用智能水滴算法求解多时间窗车辆路径问题,能够以很高的概率得到全局最优解,是求解多时间窗车辆路径问题的有效算法。     

The loader problem: formulation,complexity and algorithms

In this paper we consider a new transportation model, called the loader problem, which is frequently encountered by third-party logistics service providers in practice. It is a tactical staff-planning problem with the objective of minimizing the total labour cost of staffing a sufficient number of loaders on a given fleet of trucks that serve a given set of customer sites. We formulate the problem as an integer program and show that it is strongly NP-hard. We then consider two special cases of the loader problem that occur in certain practical situations, and propose polynomial and pseudo-polynomial time algorithms for solving these cases. We also propose a linear programming relaxation-based random rounding algorithm for the general problem and report the computational results of the algorithm.     

Complexity of flow time minimization in a crossdock truck scheduling problem with asymmetric handover relations

We address a novel truck scheduling problem arising in crossdocking logistics, in which inbound trucks carry items (pallets) which must be sorted and loaded onto outbound trucks. We minimize the utilisation of the warehouse by focusing on the synchronisation between the different related trucks. The problem is to assign the trucks to the doors of the warehouse and sequence them, in order to minimize the total time spent in the system by the pallets. We discuss the complexity of the problem, showing that even with a single door the problem is NP-hard in general, and discuss some special cases.     

A heuristic algorithm for the truckload and less-than-truckload problem

The delivery of goods from a warehouse to local customers is an important and practical problem of a logistics manager. In reality, we are facing the fluctuation of demand. When the total demand is greater than the whole capacity of owned trucks, the logistics managers may consider using an outsider carrier.Logistics managers can make a selection between a truckload (a private truck) and a less-than-truckload carrier (an outsider carrier). Selecting the right mode to transport a shipment may bring significant cost savings to the company.In this paper, we address the problem of routing a fixed number of trucks with limited capacity from a central warehouse to customers with known demand. The objective of this paper is developing a heuristic algorithm to route the private trucks and to make a selection of less-than-truckload carriers by minimizing a total cost function. Both the mathematical model and the heuristic algorithm are developed. Finally, some computational results and suggestions for future research are presented.     

Scheduling incoming and outgoing trucks at cross docking terminals to minimize the storage cost

Cross docking terminals allow companies to reduce storage and transportation costs in a supply chain. At these terminals, products of different types from incoming trucks are unloaded, sorted, and loaded to outgoing trucks for delivery. If the designated outgoing truck is not immediately available for some products, they are temporarily stocked in a small storage area available at the terminal. This paper focuses on the operational activities at a cross docking terminal with two doors: one for incoming trucks and another one for outgoing trucks. We consider the truck scheduling problem with the objective to minimize the storage usage during the product transfer inside the terminal. Our interest in this problem is mainly theoretical. We show that it is NP-hard in the strong sense even if there are only two product types. For a special case with fixed subsequences of incoming and outgoing trucks, we propose a dynamic programming algorithm, which is the first polynomial algorithm for this case. The results of numerical tests of the algorithm on randomly generated instances are also presented.     

Automated self-organising vehicles for Barclays Cycle Hire

Self-organisation is a distributed and asynchronous process in which global pattern or behaviour emerge from local components of the system. Neither central control nor external intervention is necessary during this process. Self-organising systems are adaptive and robust, which are appealing properties from a design and engineering point of view. In this paper, we present an innovative self-organisation approach for a dynamic vehicle routing problem, the Barclay Cycle Hire truck dispatch. In addition, we introduce an evolutionary algorithm capable of automatically configuring the “self-organising trucks”. Experimental results show the evolutionary algorithm improves the overall fitness of the self-organising trucks; and we observe global emergent behaviour in the way trucks self-organise.     

A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems

This paper considers a transportation problem for moving empty or laden containers for a logistic company. Owing to the limited resource of its vehicles (trucks and trailers), the company often needs to sub-contract certain job orders to outsourced companies. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the jobs with minimum routing distance and number of trucks, subject to a number of constraints such as time windows and availability of trailers. To solve such a multi-objective and multi-modal combinatorial optimization problem, a hybrid multi-objective evolutionary algorithm (HMOEA) featured with specialized genetic operators, variable-length representation and local search heuristic is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multi-objective optimization results as well as to examine the correlations among different variables, such as the number of trucks and trailers, the trailer exchange points, and the utilization of trucks in the routing solutions. It has been shown that the HMOEA is effective in solving multi-objective combinatorial optimization problems, such as finding useful trade-off solutions for the TTVRP routing problem.     

Improving fleet utilization for carriers by interval scheduling

Carriers are under increasing pressure to offset rising fuel charges with cost cutting or revenue generating schemes. One opportunity for cost reduction lies in asset management. This paper presents resource allocation scheduling models that can be used to assign truck loads to delivery times and trucks when delivery times are flexible. The paper makes two main contributions. First, we formulate the problem as a multi-objective optimization model — minimizing the number of trucks needed as well as the costs associated with tardiness or earliness — and demonstrate how improvements in fleet usage translate into savings which carriers can use as incentives to promote flexible delivery times for customers. Second, we show that a two-phase model with a polynomial algorithm in the second phase is able to produce optimal schedules in a reasonable time.     

An exact algorithm for the petrol station replenishment problem

In the petrol station replenishment problem (PSRP), the aim is to deliver petroleum products to petrol stations by means of an unlimited heterogeneous fleet of compartmented tank trucks. The problem consists of jointly determining quantities to deliver within a given interval, of allocating products to tank truck compartments and of designing delivery routes to stations. This article describes an exact algorithm which decomposes the PSRP into a truck loading problem and a routing problem. An algorithm which makes use of assignment, optimality tests and possibly standard ILP algorithm is proposed to solve the loading problem. The routing problem is handled using two different strategies, based either on a matching approach or on a column generation scheme. This algorithm was extensively tested on randomly generated data and on a real-life case arising in Eastern Quebec.     

A simulation-based genetic algorithm approach for reducing emissions from import container pick-up operation at container terminal

Emissions from idle truck engines are a main source of pollution at container terminals. In this study, we focus on reducing such emission from waiting trucks as well as the related crane operations with a new truck arrival control method that gives individual truck limited time slots for entry. We develop a method to optimize the time slot assignment for individual trucks, aiming at minimizing total emissions from trucks and cranes at import yards. The method applies discrete event simulation to estimate total truck waiting times and crane moving distance, and then applies a genetic algorithm to minimize the generated emissions from these trucks and cranes. The experiment result shows that the truck arrivals should be controlled based on the stacking of import containers, and that such control is necessary for reducing truck idling emissions at a congested container terminal.     

An adaptive guidance meta-heuristic for the vehicle routing problem with splits and clustered backhauls

This paper presents the case study of an Italian carrier, Grendi Trasporti Marittimi, which provides freight transportation services by trucks and containers. Its trucks deliver container loads from a port to import customers and collect container loads from export customers to the same port. In this case study, all import customers in a route must be serviced before all export customers, each customer can be visited more than once and containers are never unloaded or reloaded from the truck chassis along any route. We model the problem using an Integer Linear Programming formulation and propose an Adaptive Guidance metaheuristic. Our extensive computational experiments show that the adaptive guidance algorithm is capable of determining good-quality solutions in many instances of practical or potential interest for the carrier within 10?min of computing time, whereas the mathematical formulation often fails to provide the first feasible solution within 3?h of computing time.