Network flow assignment as a fixed point problem

This paper deals with the user equilibrium problem (flow assignment with equal journey time by alternative routes) and system optimum (flow assignment with minimal average journey time) in a network consisting of parallel routes with a single origin-destination pair. The travel time is simulated by arbitrary smooth nondecreasing functions. We prove that the equilibrium and optimal assignment problems for such a network can be reduced to the fixed point problem expressed explicitly. A simple iterative method of finding equilibriumand optimal flow assignment is developed. The method is proved to converge geometrically; under some fairly natural conditions the method is proved to converge quadratically.     

Optimizing departure times in vehicle routes

Most solution methods for the vehicle routing problem with time windows (VRPTW) develop routes from the earliest feasible departure time. In practice, however, temporary traffic congestion make such solutions non-optimal with respect to minimizing the total duty time. Furthermore, the VRPTW does not account for driving hours regulations, which restrict the available travel time for truck drivers. To deal with these problems, we consider the vehicle departure time optimization (VDO) problem as a post-processing of a VRPTW. We propose an ILP formulation that minimizes the total duty time. The results of a case study indicate that duty time reductions of 15% can be achieved. Furthermore, computational experiments on VRPTW benchmarks indicate that ignoring traffic congestion or driving hours regulations leads to practically infeasible solutions. Therefore, new vehicle routing methods should be developed that account for these common restrictions. We propose an integrated approach based on classical insertion heuristics.     

Routing relatively few customers per route

This paper presents a modification of the well-known Solomon (1987) sequential insertion heuristic I1 for the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW involves servicing customers within a pre-specified service time window by homogeneously capacitated vehicles from a single depot. By using two new measures for the additional time needed to insert a customer in the route, significantly better solutions are obtained for relatively short routes compared to the original Solomon (1987) sequential insertion heuristic I1. Because high-quality initial heuristics often allow local searches and metaheuristics to achieve better solutions more quickly, the new approach is likely to help generating better solutions to practical routing problems.     

An Heuristic Method for Solving Time-Sensitive Routeing Problems

Many vehicle scheduling situations require careful monitoring and control of the time a vehicle is on the road. Setting time limits as rigid constraints on a vehicle scheduling problem is usually not appropriate since minor violations of such constraints do not really imply that a solution is infeasible. An heuristic method for solving vehicle scheduling problems in which time is an important factor has been developed. The heuristic creates routes based on a "time density function" which identifies clusters of stops and chooses stops from these clusters to form a route. Important considerations balancing overtime costs, overnight route costs and travel costs to and from far clusters of stops are part of the heuristic. The method has been tested on 10 days of data from a large food distributor in the midwestern United States and found to affect significantly both variable and fixed costs in these commercial problems. The smallest of these problems had 110 stops, while the largest had 223. A modest amount of computer time (under 15 seconds in every case) was required to generate the assignment of stops to trucks and produce routes for the vehicles.     

Approximative solutions to the bicriterion Vehicle Routing Problem with Time Windows

The Vehicle Routing Problem with Time Windows (VRPTW) is a combinatorial optimization problem. It deals with route planning and the distribution of goods from a depot to geographically dispersed customers by a fleet of vehicles with constrained capacities. The customers’ demands are known and each customer has a time window in which it has to be supplied. The time windows are assumed to be soft, that means, violations of the time windows are allowed, but associated with penalties. The problem is to organize the vehicle routes optimally, i.e. to minimize the total costs, consisting of the number of used vehicles and the total distance, and the penalties simultaneously. Thus, the problem is formulated as a bicriterion minimization problem and heuristic methods are used to calculate approximations of the Pareto optimal solutions. Experimental results show that in certain cases the allowance of penalties leads to significant savings of the total costs.     

A Lagrangian relaxation-based heuristic for the vehicle routing with full container load

We address a problem of vehicle routing that arises in picking up and delivering full container load from/to an intermodal terminal. The substantial cost and time savings are expected by efficient linkage between pickup and delivery tasks, if the time of tasks and the suitability of containers for cargo allow. As this problem is NP-hard, we develop a subgradient heuristic based on a Lagrangian relaxation which enables us to identify a near optimal solution. The heuristic consists of two sub-problems: the classical assignment problem and the generalized assignment problem. As generalized assignment problem is also NP-hard, we employ an efficient solution procedure for a bin packing based problem, which replaces the generalized assignment problem. The heuristic procedure is tested on a wide variety of problem examples. The test results demonstrate that the procedure developed here can efficiently solve large instances of the problem.     

人力资源多维分配问题的混合算法

针对已有多维分配问题求解算法复杂、耗时长及精度低等问题,本文将二部图中寻求最优匹配的方法进行推广,运用试分配、饱和路调整和增广路调整对多维分配问题的最优解进行搜索,提出了求解人力资源多维分配问题的最小零面优先分配混合算法和随机试分配混合算法,对算法的有效性进行了理论证明,并分析了算法的时间和空间复杂度;同时通过这两种混合算法对初始零元素数不同的代价矩阵求解时间的计算,以及与Lagrangian松弛算法和剪枝法的耗时、精度的对比,分别得到了两种混合算法的适用性和高效性,最后通过算例验证了算法的有效性。     

Evolutionary Algorithms for the Vehicle Routing Problem with Time Windows

This paper surveys the research on evolutionary algorithms for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from a single depot to a set of geographically scattered points. The routes must be designed in such a way that each point is visited only once by exactly one vehicle within a given time interval. All routes start and end at the depot, and the total demands of all points on one particular route must not exceed the capacity of the vehicle. The main types of evolutionary algorithms for the VRPTW are genetic algorithms and evolution strategies. In addition to describing the basic features of each method, experimental results for the benchmark test problems of Solomon (1987) and Gehring and Homberger (1999) are presented and analyzed.     

The timetable constrained distance minimization problem

We define the timetable constrained distance minimization problem (TCDMP) which is a sports scheduling problem applicable for tournaments where the total travel distance must be minimized. The problem consists of finding an optimal home-away assignment when the opponents of each team in each time slot are given. We present an integer programming, a constraint programming formulation and describe two alternative solution methods: a hybrid integer programming/constraint programming approach and a branch and price algorithm. We test all four solution methods on benchmark problems and compare the performance. Furthermore, we present a new heuristic solution method called the circular traveling salesman approach (CTSA) for solving the traveling tournament problem. The solution method is able to obtain high quality solutions almost instantaneously, and by applying the TCDMP, we show how the solutions can be further improved.     

Lagrangean heuristic for primary routes assignment in survivable connection-oriented networks

Our discussion in this article centers on the application of a Lagrangean relaxation and a subgradient optimization technique to the problem of primary route assignment (PRA) in survivable connection-oriented networks. The PRA problem consists in a static optimization of primary routes minimizing the Lost Flow in Node (LFN) function. The major contribution of this work is a combination of the Lagrangean relaxation with other heuristic algorithms. We evaluate the performance of the proposed Lagrangean-based heuristic by making a comparison with their counterparts including evolutionary algorithm and GRASP using various network topologies and demand patterns. The results of simulation tests show that the new algorithm provides sub-optimal results, which are better than other heuristics.     

A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows

Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. The problem is solved by optimizing routes for the vehicles so as to meet all given constraints as well as to minimize the objectives of traveling distance and number of vehicles. This paper proposes a hybrid multiobjective evolutionary algorithm (HMOEA) that incorporates various heuristics for local exploitation in the evolutionary search and the concept of Pareto's optimality for solving multiobjective optimization in VRPTW. The proposed HMOEA is featured with specialized genetic operators and variable-length chromosome representation to accommodate the sequence-oriented optimization in VRPTW. Unlike existing VRPTW approaches that often aggregate multiple criteria and constraints into a compromise function, the proposed HMOEA optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace. The HMOEA is applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances, which yields 20 routing solutions better than or competitive as compared to the best solutions published in literature.     

A Multiple-rule Heuristic for Assembly Line Balancing

This paper introduces a four-rule heuristic method for production/assembly line balancing which seeks to minimize the number of workstations for a given cycle time. To evaluate its performance, the proposed method was compared with 15 other heuristic methods ranging in complexity from random assignment of work elements to Hoffmann's enumeration procedure. The results, based on both randomly generated problems and problems taken from the literature, indicate that the proposed heuristic outperformed other procedures. Further, the suggested method was able to find the optimal solution for 57 (85%) of the 67 literature problems.     

有时间窗的车辆路径问题的局部下降搜索算法

介绍了一个求解有时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)的启发式算法——基于λ-交换的局部下降搜索算法(Local search descent method based on λ-interchange).VRPTW是指合理安排车辆行驶路线,为一组预先设定有时间限制的客户运送货物,在不违反时间要求和车辆容量限制的条件下使得成本最小.它是一个典型的NP-难题,可以通过启发式算法获得近优解来解决.通过两个实验验证,显示了局部下降搜索算法的优良性能,取得了很好的效果,可以作为进一步研究复杂算法的基础.     

A school bus scheduling problem

This paper introduces a school bus scheduling problem wherein trips for each school are given. A trip consists of a sequence of bus stops and their designated school. Each school has its fixed time window within which trips should be completed. A school bus can serve multiple trips for multiple schools. The school bus scheduling problem seeks to optimize bus schedules to serve all the given trips considering the school time windows. We first model the problem as a vehicle routing problem with time windows (VRPTW) by treating a trip as a virtual stop. Two assignment problem based exact approaches are then proposed for special cases and a heuristic algorithm is proposed for more general cases. Benchmark problems and computational experiments are presented. Computational experiments show the effectiveness of the proposed approaches.     

Vehicle routing and scheduling with time-varying data: A case study

A heuristic algorithm is described for vehicle routing and scheduling problems to minimise the total travel time, where the time required for a vehicle to travel along any road in the network varies according to the time of travel. The variation is caused by congestion that is typically greatest during morning and evening rush hours. The algorithm is used to schedule a fleet of delivery vehicles operating in the South West of the United Kingdom for a sample of days. The results demonstrate how conventional methods that do not take time-varying speeds into account when planning, except for an overall contingency allowance, may still lead to some routes taking too long. The results are analysed to show that in the case study using the proposed approach can lead to savings in CO₂ emissions of about 7%.     

Heuristic Methods for the Data Placement Problem

Databases require a management system which is capable of retrieving and storing information as efficiently as possible. The data placement problem is concerned with obtaining an optimal assignment of data tuples onto secondary storage devices. Such tuples have complicated interrelationships which make it difficult to find an exact solution to our problem in a realistic time.We therefore consider heuristic methods—three of which are discussed and compared — the ‘greedy’ graph-collapsing method, the probabilistic hill-climbing method of simulated annealing and a third ‘greedy’ heuristic, the random improvement method, which is a local search heuristic. Overall, the best performance is obtained from the graph-collapsing method for the less complicated situations, but for larger-scale problems with complex interrelationships between tuples the simulated annealing and random improvement algorithms give better results.     

Simultaneous evacuation and entrance planning in complex building based on dynamic network flows

This paper presents mathematical models and a heuristic algorithm that address a simultaneous evacuation and entrance planning. For the simultaneous evacuation and entrance planning, four types of mathematical models based on the discrete time dynamic network flow model are developed to provide the optimal routes for evacuees and responders within a critical timeframe. The optimal routes obtained by the mathematical models can minimize the densification of evacuees and responders into specific areas. However, the mathematical model has a weakness in terms of long computation time for the large-size problem. To overcome the limitation, we developed a heuristic algorithm. We also analyzed the characteristics of each model and the heuristic algorithm by conducting case studies. This study pioneers area related to evacuation planning by developing and analyzing four types of mathematical models and a heuristic algorithm which take into account simultaneous evacuation and entrance planning.     

An exact algorithm for the capacitated facility location problems with single sourcing

Facility location problems are often encountered in many areas such as distribution, transportation and telecommunication. We describe a new solution approach for the capacitated facility location problem in which each customer is served by a single facility. An important class of heuristic solution methods for these problems are Lagrangian heuristics which have been shown to produce high quality solutions and at the same time be quite robust. A primal heuristic, based on a repeated matching algorithm which essentially solves a series of matching problems until certain convergence criteria are satisfied, is incorporated into the Lagrangian heuristic. Finally, a branch-and-bound method, based on the Lagrangian heuristic is developed, and compared computationally to the commercial code CPLEX. The computational results indicate that the proposed method is very efficient.     

分块分层优化的旅游路线规划问题研究

针对旅游路线规划决定着自驾旅游者的旅游成败问题,利用分块分层优化的思想解决了旅游路线规划这一网络优化问题。用赋权图和近邻聚类的思想构建分块网络加权图,建立考虑旅游时间、行车时间和游览时间的改进旅行商优化模型,规划区块内景点的自驾旅游路线;然后将各区块视为节点、区块间旅游时间作为时间权值之一,建立改进的多旅行商优化模型,并用模拟退火算法规划出区块间的自驾旅游路线;其次,用类比一维装箱问题的思想,建立了求最少旅游年数的一维装箱模型,并用交叉装填算法求得其最小值;最后,应用提出的方法为西安市的自驾旅游爱好者规划出了满足多种约束的游遍全国201个5A级景区的最佳旅游路线。     

A Multi-Class Dynamic User Equilibrium Model for Queuing Networks with Advanced Traveler Information Systems

This paper presents a formulation and solution algorithm for a composite dynamic user-equilibrium assignment problem with multi-user classes, in order to assess the impacts of Advanced Traveler Information Systems (ATIS) in general networks with queues. Suppose that users equipped with ATIS will receive complete information and hence be able to choose the best departure times and routes in a deterministic manner, while users not equipped with ATIS will have incomplete information and hence may make decisions on departure times and routes in a stochastic manner. This paper proposes a discrete-time, finite-dimensional variational inequality formulation that involves two criteria regarding the route and departure time choice behaviors, i.e., the deterministic dynamic user equilibrium and the nested logit-based stochastic dynamic user equilibrium. The formulation is then converted to an equivalent “zero-extreme value” minimization problem. A heuristic algorithm based on route/time-swapping process is proposed, which iteratively adjusts the route and departure time choices to reach closely to an extreme point of the minimization problem. A numerical example is used to demonstrate the effectiveness of the proposed approach for assessing the ATIS impacts such as changes in individual travel costs, departure times, route inflows, queuing peaks and total network travel cost. This revised version was published online in July 2006 with corrections to the Cover Date.