A Variable Neighborhood Search for the Multi Depot Vehicle Routing Problem with Time Windows

The aim of this paper is to propose an algorithm based on the philosophy of the Variable Neighborhood Search (VNS) to solve Multi Depot Vehicle Routing Problems with Time Windows. The paper has two main contributions. First, from a technical point of view, it presents the first application of a VNS for this problem and several design issues of VNS algorithms are discussed. Second, from a problem oriented point of view the computational results show that the approach is competitive with an existing Tabu Search algorithm with respect to both solution quality and computation times.     

The Capacitated Team Orienteering Problem: A Bi-level Filter-and-Fan method

This paper focuses on vehicle routing problems with profits and addresses the so-called Capacitated Team Orienteering Problem. Given a set of customers with a priori known profits and demands, the objective is to find the subset of customers, for which the collected profit is maximized, and to determine the visiting sequence and assignment to vehicle routes assuming capacity and route duration restrictions. The proposed method adopts a hierarchical bi-level search framework that takes advantage of different search landscapes. At the upper level, the solution space is explored on the basis of the collected profit, using a Filter-and-Fan method and a combination of profit oriented neighborhoods, while at the lower level the routing of customers is optimized in terms of traveling distance via a Variable Neighborhood Descent method. Computational experiments on benchmark data sets illustrate the efficiency and effectiveness of the proposed approach. Compared to existing results, new upper bounds are produced with competitive computational times.     

Team Orienteering Problem with Decreasing Profits

Team Orienteering with Decreasing Profits (DP-TOP) extends the classical Team Orienteering problem (TOP) by considering the profit of each client as a decreasing function of time. It consists of maximizing the sum of collected profit by a fixed number K of vehicles, visiting each client at most once. In this work, we present lower bounds based on a Dantzig-Wolfe decomposition and column generation as well as upper bounds obtained by an evolutionary local search approach (ELS).     

Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search

This paper proposes a way to combine the Mesh Adaptive Direct Search (MADS) algorithm, which extends the Generalized Pattern Search (GPS) algorithm, with the Variable Neighborhood Search (VNS) metaheuristic, for nonsmooth constrained optimization. The resulting algorithm retains the convergence properties of MADS, and allows the far reaching exploration features of VNS to move away from local solutions. The paper also proposes a generic way to use surrogate functions in the VNS search. Numerical results illustrate advantages and limitations of this method.     

Cooperative Parallel Variable Neighborhood Search for the p-Median

We propose a cooperative multi-search method for the Variable Neighborhood Search (VNS) meta-heuristic based on the central-memory mechanism that has been successfully applied to a number of difficult combinatorial problems. In this approach, several independent VNS meta-heuristics cooperate by asynchronously exchanging information about the best solutions identified so far, thus conserving the simplicity of the original, sequential VNS ideas. The p-median problem (PM) serves as test case. Extensive experimentations have been conducted on the classical TSPLIB benchmark problem instances with up to 11948 customers and 1000 medians, without any particular calibration of the parallel method. The results indicate that, compared to sequential VNS, the cooperative strategy yields significant gains in terms of computation time without a loss in solution quality.     

Tabu Search heuristics for the Vehicle Routing Problem with Time Windows

This paper surveys the research on the Tabu Search heuristics for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes for a fleet of vehicles from one 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. In addition to describing basic features of each method, experimental results for Solomon’s benchmark test problems are presented and analyzed. This work was partially supported by the Emil Aaltonen Foundation, Liikesivistysrahasto Foundation, the Canadian Natural Science and Engineering Research Council and the TOP program funded by the Research Council of Norway. This support is gratefully acknowledged.     

一种带时间窗和容量约束的车辆路线问题及其Tabu Search算法

本文提出一种带时间窗和容量约束的车辆路线问题（CVRPTW），并利用Tabu Search快速启式算法，针对Solomon提出的几个标准问题，快捷地得到了优良的数值结果。     

蚂蚁算法在带时间窗车辆路径问题中的应用研究

蚂蚁算法是近年来新出现的一种随机型搜索寻优算法.自从在旅行商等著名问题中得到富有成效的应用之后,已引起人们越来越多的关注和重视.本文将这种新型的生物优化思想扩展到物流管理中的带时间窗车辆路径问题,从数值计算上探索了蚂蚁算法的优化能力,获得了满意的效果.     

The Delivery Man Problem with time windows

In this paper, a variant of the Traveling Salesman Problem with Time Windows is considered, which consists in minimizing the sum of travel durations between a depot and several customer locations. Two mixed integer linear programming formulations are presented for this problem: a classical arc flow model and a sequential assignment model. Several polyhedral results are provided for the second formulation, in the special case arising when there is a closed time window only at the depot, while open time windows are considered at all other locations. Exact and heuristic algorithms are also proposed for the problem. Computational results show that medium size instances can be solved exactly with both models, while the heuristic provides good quality solutions for medium to large size instances.     

带时间窗的某物流配送车辆调度问题的方案优化分析

采用带时间窗的车辆调度优化物流配送系统,建立了带时间窗的多目标约束条件下的派车优化问题的数学模型,并针对某大型连锁超市物流配送中心的车辆调度问题进行了求解,经过对比分析,新方案实现了一定程度多目标优化.     

A branch-and-price algorithm for the Vehicle Routing Problem with Deliveries,Selective Pickups and Time Windows

In the Vehicle Routing Problem with Deliveries, Selective Pickups and Time Windows, the set of customers is the union of delivery customers and pickup customers. A fleet of identical capacitated vehicles based at the depot must perform all deliveries and profitable pickups while respecting time windows. The objective is to minimize routing costs, minus the revenue associated with the pickups. Five variants of the problem are considered according to the order imposed on deliveries and pickups. An exact branch-and-price algorithm is developed for the problem. Computational results are reported for instances containing up to 100 customers.