An iterative three-component heuristic for the team orienteering problem with time windows

This paper studies the team orienteering problem with time windows, the aim of which is to maximize the total profit collected by visiting a set of customers with a limited number of vehicles. Each customer has a profit, a service time and a time window. A service provided to any customer must begin in his or her time window. We propose an iterative framework incorporating three components to solve this problem. The first two components are a local search procedure and a simulated annealing procedure. They explore the solution space and discover a set of routes. The third component recombines the routes to identify high quality solutions. Our computational results indicate that this heuristic outperforms the existing approaches in the literature in average performance by at least 0.41%. In addition, 35 new best solutions are found.     

Customer acceptance mechanisms for home deliveries in metropolitan areas

Efficient and reliable home delivery is crucial for the economic success of online retailers. This is especially challenging for attended home deliveries in metropolitan areas where logistics service providers face congested traffic networks and customers expect deliveries in tight delivery time windows. Our goal is to develop and compare strategies that maximize the profits of a logistics service provider by accepting as many delivery requests as possible, while assessing the potential impact of a request on the service quality of a delivery tour. Several acceptance mechanisms are introduced, differing in the amount of travel time information that is considered in the decision of whether a delivery request can be accommodated or not. A real-world inspired simulation framework is used for comparison of acceptance mechanisms with regard to profits and service quality. Computational experiments utilizing this simulation framework investigate the effectiveness of acceptance mechanisms and help identify when more advanced travel time information may be worth the additional data collection and computational efforts.     

Efficient elementary and restricted non-elementary route pricing

Column generation is involved in the current most efficient approaches to routing problems. Set partitioning formulations model routing problems by considering all possible routes and selecting a subset that visits all customers. These formulations often produce tight lower bounds and require column generation for their pricing step. The bounds in the resulting branch-and-price are tighter when elementary routes are considered, but this approach leads to a more difficult pricing problem. Balancing the pricing with route relaxations has become crucial for the efficiency of the branch-and-price for routing problems. Recently, the ng-routes relaxation was proposed as a compromise between elementary and non-elementary routes. The ng-routes are non-elementary routes with the restriction that when following a customer, the route is not allowed to visit another customer that was visited before if they belong to a dynamically computed set. The larger the size of these sets, the closer the ng-route is to an elementary route. This work presents an efficient pricing algorithm for ng-routes and extends this algorithm for elementary routes. Therefore, we address the Shortest Path Problem with Resource Constraint (SPPRC) and the Elementary Shortest Path Problem with Resource Constraint (ESPPRC). The proposed algorithm combines the Decremental State-Space Relaxation technique (DSSR) with completion bounds. We apply this algorithm for the Generalized Vehicle Routing Problem (GVRP) and for the Capacitated Vehicle Routing Problem (CVRP), demonstrating that it is able to price elementary routes for instances up to 200 customers, a result that doubles the size of the ESPPRC instances solved to date.     

Performance-based routing

In many-server systems with heterogeneous servers, the Fastest-Server-First (FSF) policy is known for its excellent performance. However, when service rates are unknown and/or time-varying, implementing FSF routing is not straightforward. We analyze an algorithm that approximates FSF routing: servers are ranked in a dynamic list, where the shorter the actual service times that a server exhibits—the closer the server is to the head of the list; a customer is then routed to the lowest-index (highest-in-the-list) idle server.     

A two-stage solution approach for the Directed Rural Postman Problem with Turn Penalties

In this paper, we consider the Directed Rural Postman Problem with Turn Penalties (DRPP-TP). A solution is a tour that traverses all required arcs of the graph. The total cost of the tour is the sum of the lengths of the traversed arcs plus the penalties associated with the turns. One solution approach involves transforming the arc routing problem into an equivalent node routing problem. An alternative direct approach (without graph transformation) that involves two stages has been proposed in the literature. In the first part of this paper, we investigate the applicability of the direct approach. We identify several characteristics of the input instance that make this approach effective and present several limitations of this approach. In the second part of this paper, we describe an integer linear program that is combined with a local search algorithm. This combination produces high-quality solutions to the DRPP-TP in a reasonable amount of computing time.     

A branch-and-price algorithm for a vehicle routing with demand allocation problem

We investigate the vehicle routing with demand allocation problem where the decision-maker jointly optimizes the location of delivery sites, the assignment of customers to (preferably convenient) delivery sites, and the routing of vehicles operated from a central depot to serve customers at their designated sites. We propose an effective branch-and-price (B&P) algorithm that is demonstrated to greatly outperform the use of commercial branch-and-bound/cut solvers such as CPLEX. Central to the efficacy of the proposed B&P algorithm is the development of a specialized dynamic programming procedure that extends works on elementary shortest path problems with resource constraints in order to solve the more complex column generation pricing subproblem. Our computational study demonstrates the efficacy of the proposed approach using a set of 60 problem instances. Moreover, the proposed methodology has the merit of providing optimal solutions in run times that are significantly shorter than those reported for decomposition-based heuristics in the literature.     

A labeling method for dynamic driver-task assignment with uncertain task durations

The problem of assigning drivers to cover tasks with service time windows and uncertain task durations is formulated as a dynamic stochastic decision model. We develop an adaptive labeling solution procedure that can incorporate various practical constraints and work rules. Experiments are conducted to evaluate the procedure's performance and compare the stochastic and deterministic formulations.     

智能光网络中一种新型的分布式恢复方法

提出了一种基于固定备用路由和目的端触发的智能光网络中的分布式恢复方法,所选择的任一对源端节点和目的的端节点之间的固定备用路由包括该节点对之间的k条最短路径路由和基于部分链路无关的路由.目的端节点一旦探测到业务通道的失效将立即启动恢复进程,根据网络当前的资源使用情况,在这些备用路由中按照一定的次序选择一条恢复路由并为此分配一条可用的波长(对于没有波长变换器的光网络),然后将恢复通道的建立请求消息发送给该恢复通道的相关节点,这些相关节点可以锁定相关的网络资源,以避免来自其他恢复通道的竞争.所提出的算法增加了固定备用路由的数量,从仿真结果可以看出,该方法明显改善了失效连接的阻塞性能.     

Wavelength routing of uniform instances in all-optical rings

We consider the problem of routing uniform communication instances in switched optical rings that use wavelength-division multiplexing technology. A communication instance is called uniform if it consists exactly of all pairs of nodes in the graph whose distance is equal to one from a specified set S={d₁,d₂,…,d_k}. When k=1 or 2, we prove necessary and sufficient conditions on the values in S relative to n for the optimal wavelength index to be equal to the optimal load in the ring R_n. When k=2, we show that for any uniform instance specified by {d₁,d₂}, there is an optimal wavelength assignment on the ring R_n, if n>(d₁/q-2)d₁+(d₁/q-1)d₂, where q=GCD(d₁,d₂). For general k and n, we show a -approximation for the optimal wavelength index; this is the best possible for arbitrary S. We also show that an optimal assignment can always be obtained provided n is large enough compared to the values in S.     

Routing open shop and flow shop scheduling problems

We consider a generalization of the classical open shop and flow shop scheduling problems where the jobs are located at the vertices of an undirected graph and the machines, initially located at the same vertex, have to travel along the graph to process the jobs. The objective is to minimize the makespan. In the tour-version the makespan means the time by which each machine has processed all jobs and returned to the initial location. While in the path-version the makespan represents the maximum completion time of the jobs. We present improved approximation algorithms for various cases of the open shop problem on a general graph, and the tour-version of the two-machine flow shop problem on a tree. Also, we prove that both versions of the latter problem are NP-hard, which answers an open question posed in the literature.