Generalized mixed integer and VNS heuristic approach to solving the multisize containers drayage problem

This paper addresses the problem of vehicle routing in drayage operations, where vehicles can carry containers of different sizes. The multisize container drayage problem with time windows is modeled as a multiple matching problem and formulated as a mixed integer linear program (MILP) model. The proposed MILP model determines optimal pickup and delivery routes of vehicles and is applicable to any type of vehicles capable of simultaneously transporting any arbitrary number of 20‐ and 40‐ft containers. To solve larger sized problems, we proposed a variable neighborhood search (VNS) heuristic. Both MILP and VNS approaches have been tested on numerous test instances and their performances are reported.     

A hybrid column generation approach for an industrial waste collection routing problem

This paper presents a practical roll-on/roll-off routing (ROROR) problem arising in the collection of industrial waste. Skip containers, which are used for the waste collection, need to be distributed between, and collected from, a set of customers. Full containers must be driven to dump sites, while empty containers must be returned to the depot to await further assignments. Unlike, the traditional ROROR problem, where vehicles may transport one skip container at a time regardless of whether it is full or not, we consider cases in which a vehicle can transport up to eight containers, at most two of which can be full. We propose a generalized set partitioning formulation of the problem and describe a hybrid column generation procedure to solve it. A fast Tabu Search heuristic is used to generate new columns. The proposed methodology is tested on nine data sets, four of which are actual, real-world problem instances. Results indicate that the hybrid column generation outperforms a purely heuristic approach in terms of both running time and solution quality. High quality solutions to problems containing up to 100 orders can be solved in approximately 15 min.     

车辆路径问题模型及其智能启发式算法

车辆路径问题是运筹学研究的一个重要分支。描述了 6种典型的车辆路径问题的模型 ,并列举了当前的 9种启发式算法     

Minimum cost VRP with time-dependent speed data and congestion charge

A heuristic algorithm, called LANCOST, is introduced for vehicle routing and scheduling problems to minimize the total travel cost, where the total travel cost includes fuel cost, driver cost and congestion charge. The fuel cost required is influenced by the speed. The speed for a vehicle to travel along any road in the network varies according to the time of travel. The variation in speed is caused by congestion which is greatest during morning and evening rush hours. If a vehicle enters the congestion charge zone at any time, a fixed charge is applied. A benchmark dataset is designed to test the algorithm. The algorithm is also used to schedule a fleet of delivery vehicles operating in the London area.     

The multi-depot split delivery vehicle routing problem: An integer programming-based heuristic, new test problems, and computational results

The multi-depot split delivery vehicle routing problem combines the split delivery vehicle routing problem and the multiple depot vehicle routing problem. We define this new problem and develop an integer programming-based heuristic for it. We apply our heuristic to 30 instances to determine the reduction in distance traveled that can be achieved by allowing split deliveries among vehicles based at the same depot and vehicles based at different depots. We generate new test instances with high-quality, visually estimated solutions and report results on these instances.     

Vehicle routing under time-dependent travel times: The impact of congestion avoidance

Daily traffic congestion forms a major problem for businesses such as logistic service providers and distribution firms. It causes late arrivals at customers and additional costs for hiring the truck drivers. Such costs caused by traffic congestion can be reduced by taking into account and avoiding predictable traffic congestion within vehicle route plans. In the literature, various strategies are proposed to avoid traffic congestion, such as selecting alternative routes, changing the customer visit sequences, and changing the vehicle-customer assignments. We investigate the impact of these and other strategies in off-line vehicle routing on the performance of vehicle route plans in reality. For this purpose, we develop a set of vehicle routing problem instances on real road networks, and a speed model that reflects the key elements of peak hour traffic congestion. The instances are solved for different levels of congestion avoidance using a modified Dijkstra algorithm and a restricted dynamic programming heuristic. Computational experiments show that 99% of late arrivals at customers can be eliminated if traffic congestion is accounted for off-line. On top of that, about 87% of the extra duty time caused by traffic congestion can be eliminated by clever congestion avoidance strategies.     

Vehicle routing problems with split deliveries

This paper is a survey on the vehicle routing problems with split deliveries, a class of routing problems where each customer may be served by more than one vehicle. Starting from the most classical routing problems, we introduce the split delivery vehicle routing problem (SDVRP). We review a formulation, the main properties and exact and heuristic solution approaches for the SDVRP. Then, we present a general overview of several variants of the SDVRP and of the literature available.     

A novel two-phase heuristic method for vehicle routing problem with backhauls

This paper raises a novel two-phase heuristic method to solve vehicle routing problems with backhauls. Differing from other vehicle routing problems, we consider the travel speed of vehicle to be time dependent, which will be used for the model of rush hour in an urban city. In the first phase, the original solution is generated by extending traditional heuristic methods and in the second phase, the reactive tabu search algorithm is used to optimize the original solution. We verified that this algorithm is efficient in a number of standard test cases. After comparison with the closest neighboring search algorithm, we found that the results of two-phase heuristic methods are more reasonable.     

A general heuristic for vehicle routing problems

We present a unified heuristic which is able to solve five different variants of the vehicle routing problem: the vehicle routing problem with time windows (VRPTW), the capacitated vehicle routing problem (CVRP), the multi-depot vehicle routing problem (MDVRP), the site-dependent vehicle routing problem (SDVRP) and the open vehicle routing problem (OVRP).     

基于启发式蚁群算法的VRP问题研究

针对蚁群算法求解VRP问题时收敛速度慢,求解质量不高的缺点,把城市和仓库间的距离矩阵和路径节约矩阵信息融入到初始信息素矩阵中作为启发式信息引入到蚁群算法中用于求解有容量限制的车辆路径规划问题（CVRP）,在三个基准数据集上的实验研究表明,基于启发式信息的蚁群算法与基本蚁群算法相比能够以较快的速度收敛到较好的解。     

确定区域详细布线算法

提出了一种确定区域的详细布线算法,它能对不同设计模式进行布线。该算法能适用于任意多层布线情况,并且支持不同布线层具有的不同工艺参数,在构造布线树时,考虑芯片当前的走线拥挤度,使布线比较平均,并加快了算法运行速度、改善了布线质量,在连接两点线网时,构造基于二维迷宫布线结果的分层图,提出了一种对分层图的启发式染色算示来进行布线层分配,大大提高算法布线速度,采用拆线重布的方法来处理布线失败的线网。     

Network service scheduling and routing

Real‐life vehicle routing problems generally have both routing and scheduling aspects to consider. Although this fact is well acknowledged, few heuristic methods exist that address both these complicated aspects simultaneously. We present a graph theoretic heuristic to determine an efficient service route for a single service vehicle through a transportation network that requires a subset of its edges to be serviced, each a specified (potentially different) number of times. The times at which each of these edges are to be serviced should additionally be as evenly spaced over the scheduling time window as possible, thus introducing a scheduling consideration to the problem. Our heuristic is based on the tabu search method, used in conjunction with various well‐known graph theoretic algorithms, such as those of Floyd (for determining shortest routes) and Frederickson (for solving the rural postman problem). This heuristic forms the backbone of a decision support system that prompts the user for certain parameters from the physical situation (such as the service frequencies and travel times for each network link as well as bounds in terms of acceptability of results) after which a service routing schedule is suggested as output. The decision support system is applied to a special case study, where a service routing schedule is sought for the South African national railway system by Spoornet (the semi‐privatised South African national railways authority and service provider) as part of their rationalisation effort, in order to remain a lucrative company.     

An iterated tabu search for the multi-compartment vehicle routing problem

We study a variant of the vehicle routing problem that allows vehicles with multiple compartments. The need for multiple compartments frequently arises in practical applications when there are several products of different quality or type, that must be kept or handled separately. The resulting problem is called the multi-compartment vehicle routing problem (MCVRP). We propose a tabu search heuristic and embed it into a iterated local search to solve the MCVRP. In several experiments we analyze the performance of the iterated tabu search and compare it to results from the literature. We find that it consistently produces solutions that are better than existing heuristic algorithms.     

ITS和GPS系统下车辆实时调度问题的研究

ITS和GPS技术的发展使得道路交通状况和车辆位置能够被实时监控,从而车辆调度中心能够根据道路交通状况实时调整车辆行驶路线,以避开交通拥挤路段,降低车辆的行驶时间。建立了ITS和GPS系统下车辆实时调度模型,并且就车辆行驶过程中如何降低问题的状态空间进行了研究。     

A fast two-level variable neighborhood search for the clustered vehicle routing problem

In this paper, we present an improved two-level heuristic to solve the clustered vehicle routing problem (CluVRP). The CluVRP is a generalization of the classical capacitated vehicle routing problem (CVRP) in which customers are grouped into predefined clusters, and all customers in a cluster must be served consecutively by the same vehicle. This paper contributes to the literature in the following ways: (i) new upper bounds are presented for multiple benchmark instances, (ii) good heuristic solutions are provided in much smaller computing times than existing approaches, (iii) the CluVRP is reduced to its cluster level without assuming Euclidean coordinates or distances, and (iv) a new variant of the CluVRP, the CluVRP with weak cluster constraints, is introduced. In this variant, clusters are allocated to vehicles in their entirety, but all corresponding customers can be visited by the vehicle in any order.The proposed heuristic solves the CluVRP by combining two variable neighborhood search algorithms, that explore the solution space at the cluster level and the individual customer level respectively. The algorithm is tested on different benchmark instances from the literature with up to 484 nodes, obtaining high quality solutions while requiring only a limited calculation time.     

Variable Neighborhood Search heuristic for the Inventory Routing Problem in fuel delivery

In this paper we observe the extension of the vehicle routing problem (VRP) in fuel delivery that includes petrol stations inventory management and which can be classified as the Inventory Routing Problem (IRP) in fuel delivery. The objective of the IRP is to minimize the total cost of vehicle routing and inventory management. We developed a Variable Neighborhood Search (VNS) heuristic for solving a multi-product multi-period IRP in fuel delivery with multi-compartment homogeneous vehicles, and deterministic consumption that varies with each petrol station and each fuel type. The stochastic VNS heuristic is compared to a Mixed Integer Linear Programming (MILP) model and the deterministic “compartment transfer” (CT) heuristic. For three different scale problems, with different vehicle types, the developed VNS heuristic outperforms the deterministic CT heuristic. Also, for the smallest scale problem instances, the developed VNS was capable of obtaining the near optimal and optimal solutions (the MILP model was able to solve only the smallest scale problem instances).     

Skewed general variable neighborhood search for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem (CCVRP) is a relatively new version of the classical capacitated vehicle routing problem, and it is equivalent to a traveling repairman problem with capacity constraints and a homogeneous vehicle fleet, which aims to minimize the total arrival time at customers. Many real‐world applications can be modeled by this problem, such as the important application resulting from the humanitarian aid following a natural disaster. In this paper, two heuristics are proposed. The first one is a constructive heuristic to generate an initial solution and the second is the skewed variable neighborhood search (SVNS) heuristic. The SVNS algorithm starts with the initial solution. At each iteration, the perturbation phase and the local search phase are used to improve the solution of the CCVRP, and the distance function in acceptance criteria phase is used to improve the exploration of faraway valleys. This algorithm is applied to a set of benchmarks, and the comparison results show that the proposed algorithms provide better solutions than those reported in the previous literature on memetic algorithms and adaptive large neighborhood search heuristics.     

A tabu search heuristic for the quay crane scheduling problem

This paper proposes a tabu search heuristic for the Quay Crane Scheduling Problem (QCSP), the problem of scheduling a fixed number of quay cranes in order to load and unload containers into and from a ship. The optimality criterion considered is the minimum completion time. Precedence and non-simultaneity constraints between tasks are taken into account. The former originate from the different kind of operations that each crane has to perform; the latter are needed in order to avoid interferences between the cranes. The QCSP is decomposed into a routing problem and a scheduling problem. The routing problem is solved by a tabu search heuristic, while a local search technique is used to generate the solution of the scheduling problem. This is done by minimizing the longest path length in a disjunctive graph. The effectiveness of our algorithm is assessed by comparing it to a branch-and-cut algorithm and to a Greedy Randomized Adaptive Search Procedure (GRASP).     

Efficient stochastic hybrid heuristics for the multi-depot vehicle routing problem

The paper addresses the problem of multi-depot vehicle routing in order to minimize the delivery time of vehicle objective. Three hybrid heuristics are presented to solve the multi-depot vehicle routing problem. Each hybrid heuristic combines elements from both constructive heuristic search and improvement techniques. The improvement techniques are deterministic, stochastic and simulated annealing (SA) methods. Experiments are run on a number of randomly generated test problems of varying depots and customer sizes. Our heuristics are shown to outperform one of the best-known existing heuristic. Statistical tests of significance are performed to substantiate the claims of improvement.     

Heuristics for the robust vehicle routing problem with time windows

Uncertainty is frequently present in logistics and transportation, where vehicle routing problems play a crucial role. However, due to the complexity inherent in dealing with uncertainty, most research has been devoted to deterministic problems. This paper considers a robust version of the vehicle routing problem with hard time windows, in which travel times are uncertain. A budget polytope uncertainty set describes the travel times, to limit the maximum number of sailing legs that can be delayed. This makes sure that improbable scenarios are not considered, while making sure that solutions are immune to delays on a given number of sailing legs. Existing exact methods are only able to solve small instances of the problem and can be computationally demanding. With the aim of solving large instances with reduced running times, this paper proposes an efficient heuristic based on adaptive large neighborhood search. The computational study performed on instances with different uncertainty levels compares and analyzes the performance of four versions of the heuristic and shows how good quality solutions can be obtained within short computational times.