The symmetric traveling salesman polytope and its graphical relaxation: Composition of valid inequalities

The graphical relaxation of the Traveling Salesman Problem is the relaxation obtained by requiring that the salesman visit each city at least once instead of exactly once. This relaxation has already led to a better understanding of the Traveling Salesman polytope in Cornuéjols, Fonlupt and Naddef (1985). We show here how one can compose facet-inducing inequalities for the graphical traveling salesman polyhedron, and obtain other facet-inducing inequalities. This leads to new valid inequalities for the Symmetric Traveling Salesman polytope. This paper is the first of a series of three papers on the Symmetric Traveling Salesman polytope, the next one studies the strong relationship between that polytope and its graphical relaxation, and the last one applies all the theoretical developments of the two first papers to prove some new facet-inducing results.This work was initiated while the authors were visiting the Department of Statistics and Operations Research of New York University, and continued during several visits of the first author at IASI.     

Branch-and-bound for the Precedence Constrained Generalized Traveling Salesman Problem

The Precedence Constrained Generalized Traveling Salesman Problem (PCGTSP) combines the Generalized Traveling Salesman Problem (GTSP) and the Sequential Ordering Problem (SOP). We present a novel branching technique for the GTSP which enables the extension of a powerful pruning technique. This is combined with some modifications of known bounding methods for related problems. The algorithm manages to solve problem instances with 12–26 groups within a minute, and instances with around 50 groups which are denser with precedence constraints within 24 h.     

Advanced routing for city logistics service providers based on time-dependent travel times

Increasing traffic demand, recurring congestion and sophisticated e-commerce business models lead to enormous challenges for routing in city logistics. We introduce a planning system for city logistics service providers, which faces those challenges by more realistic vehicle routing considering time-dependent travel times. Time-dependent travel times arise from telematics-based traffic data collection city-wide. Well-known heuristics for the Traveling Salesman Problem and for the Vehicle Routing Problem are adapted to time-dependent planning data. Computational experiments allow for insights into the efficiency of individual heuristics, their adaptability to time-dependent planning data sets, and the quality and structure of resulting delivery tours.     

Memetic algorithm for the Traveling Car Renter Problem: an experimental investigation

The Traveling Car Renter Problem (CaRS) is a generalization of the Traveling Salesman Problem where the tour can be decomposed into contiguous paths that are travelled by different rented cars. When a car is rented in a city and delivered in another, the renter must pay a fee to return the car to its home city. Given a graph G and cost matrices associated to cars available for rent, the problem consists in determining the minimum cost Hamiltonian cycle in G, considering also the cost paid to deliver a car in a city different from the one it was rented. The latter cost is added to the cost of the edges in the cycle. This paper describes the general problem and some related variants. Two metaheuristic approaches are proposed to deal with CaRS: GRASP hybridized with Variable Neighborhood Descent and Memetic Algorithm. A set of benchmark instances is proposed for the new problem which is utilized on the computational experiments. The algorithms are tested on a set of 40 Euclidean and non-Euclidean instances.     

Routing problems: A bibliography

This bibliography contains 500 references on four classical routing problems: the Traveling Salesman Problem, the Vehicle Routing Problem, the Chinese Postman Problem, and the Rural Postman Problem. References are presented alphabetically under a number of subheadings.     

The traveling salesman problem with pickup and delivery: polyhedral results and a branch-and-cut algorithm

The Traveling Salesman Problem with Pickup and Delivery (TSPPD) is defined on a graph containing pickup and delivery vertices between which there exists a one-to-one relationship. The problem consists of determining a minimum cost tour such that each pickup vertex is visited before its corresponding delivery vertex. In this paper, the TSPPD is modeled as an integer linear program and its polyhedral structure is analyzed. In particular, the dimension of the TSPPD polytope is determined and several valid inequalities, some of which are facet defining, are introduced. Separation procedures and a branch-and-cut algorithm are developed. Computational results show that the algorithm is capable of solving to optimality instances involving up to 35 pickup and delivery requests, thus more than doubling the previous record of 15.      

Expanding neighborhood search–GRASP for the probabilistic traveling salesman problem

The Probabilistic Traveling Salesman Problem is a variation of the classic traveling salesman problem and one of the most significant stochastic routing problems. In probabilistic traveling salesman problem only a subset of potential customers need to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. In this paper, a variant of the well-known Greedy Randomized Adaptive Search Procedure (GRASP), the Expanding Neighborhood Search–GRASP, is proposed for the solution of the probabilistic traveling salesman problem. expanding neighborhood search–GRASP has been proved to be a very efficient algorithm for the solution of the traveling salesman problem. The proposed algorithm is tested on a numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in six out of ten cases the proposed algorithm gives a new best solution.     

A distributed exact algorithm for the multiple resource constrained sequencing problem

Sequencing problems arise in the context of process scheduling both in isolation and as subproblems for general scenarios. Such sequencing problems can often be posed as an extension of the Traveling Salesman Problem. We present an exact parallel branch and bound algorithm for solving the Multiple Resource Constrained Traveling Salesman Problem (MRCTSP), which provides a platform for addressing a variety of process sequencing problems. The algorithm is based on a linear programming relaxation that incorporates two families of inequalities via cutting plane techniques. Computational results show that the lower bounds provided by this method are strong for the types of problem generators that we considered as well as for some industrially derived sequencing instances. The branch and bound algorithm is parallelized using the processor workshop model on a network of workstations connected via Ethernet. Results are presented for instances with up to 75 cities, 3 resource constraints, and 8 workstations.     

Selective generalized travelling salesman problem

ABSTRACT

This paper introduces the Selective Generalized Traveling Salesman Problem (SGTSP). In SGTSP, the goal is to determine the maximum profitable tour within the given threshold of the tour’s duration, which consists of a subset of clusters and a subset of nodes in each cluster visited on the tour. This problem is a combination of cluster and node selection and determining the shortest path between the selected nodes. We propose eight mixed integer programming (MIP) formulations for SGTSP. All of the given MIP formulations are completely new, which is one of the major novelties of the study. The performance of the proposed formulations is evaluated on a set of test instances by conducting 4608 experimental runs. Overall, 4138 out of 4608 (~90%) test instances were solved optimally by using all formulations.     

Transgenetic algorithm for the Traveling Purchaser Problem

In this paper an evolutionary algorithm is presented for the Traveling Purchaser Problem, an important variation of the Traveling Salesman Problem. The evolutionary approach proposed in this paper is called transgenetic algorithm. It is inspired on two significant evolutionary driving forces: horizontal gene transfer and endosymbiosis. The performance of the algorithm proposed for the investigated problem is compared with other recent works presented in the literature. Computational experiments show that the proposed approach is very effective for the investigated problem with 17 and 9 new best solutions reported for capacitated and uncapacitated instances, respectively.     

Lin–Kernighan heuristic adaptations for the generalized traveling salesman problem

The Lin–Kernighan heuristic is known to be one of the most successful heuristics for the Traveling Salesman Problem (TSP). It has also proven its efficiency in application to some other problems.     

Multi-depot Multiple TSP: a polyhedral study and computational results

We study the Multi-Depot Multiple Traveling Salesman Problem (MDMTSP), which is a variant of the very well-known Traveling Salesman Problem (TSP). In the MDMTSP an unlimited number of salesmen have to visit a set of customers using routes that can be based on a subset of available depots. The MDMTSP is an NP-hard problem because it includes the TSP as a particular case when the distances satisfy the triangular inequality. The problem has some real applications and is closely related to other important multi-depot routing problems, like the Multi-Depot Vehicle Routing Problem and the Location Routing Problem. We present an integer linear formulation for the MDMTSP and strengthen it with the introduction of several families of valid inequalities. Certain facet-inducing inequalities for the TSP polyhedron can be used to derive facet-inducing inequalities for the MDMTSP. Furthermore, several inequalities that are specific to the MDMTSP are also studied and proved to be facet-inducing. The partial knowledge of the polyhedron has been used to implement a Branch-and-Cut algorithm in which the new inequalities have been shown to be very effective. Computational results show that instances involving up to 255 customers and 25 possible depots can be solved optimally using the proposed methodology.     

考虑充电设施重入的电动汽车旅行商问题研究

出于减少环境危害和响应相关法规的考虑,物流企业开始逐步将运输车辆转变为电动汽车;而由于电动汽车的续航里程有限,对电动汽车的路径优化也将涉及充电设施。充电设施的“重入”是指电动汽车返回之前已经通过的充电设施进行充能的现象,它的存在需要改变经典旅行商问题模型中的“子回路约束”。本文聚焦于充电设施的“重入”,构建了一个无需预估充电设施重入次数上限的电动汽车旅行商问题模型,并通过引入路径可行性判别方法和增加充电设施选择与重复策略,设计了一种改进蚁群算法对问题进行求解。结果表明:与未考虑重入的模型相比,本文提出的考虑充电设施重入的模型能在电动汽车电池容量较低的情况下求出最优解,同时也能够使充电设施承担多次充电任务,从而在较少充电设施情况下依然能够得到可行的最优路径。     

Optimal deterministic algorithms for some variants of Online Quota Traveling Salesman Problem

This paper is concerned with the Online Quota Traveling Salesman Problem. Depending on the symmetry of the metric and the requirement for the salesman to return to the origin, four variants are analyzed. We present optimal deterministic algorithms for each variant defined on a general space, a real line, or a half-line. As a byproduct, an improved lower bound for a variant of Online TSP on a half-line is also obtained.     

Metaheuristics for the team orienteering problem

The Team Orienteering Problem (TOP) is the generalization to the case of multiple tours of the Orienteering Problem, known also as Selective Traveling Salesman Problem. A set of potential customers is available and a profit is collected from the visit to each customer. A fleet of vehicles is available to visit the customers, within a given time limit. The profit of a customer can be collected by one vehicle at most. The objective is to identify the customers which maximize the total collected profit while satisfying the given time limit for each vehicle. We propose two variants of a generalized tabu search algorithm and a variable neighborhood search algorithm for the solution of the TOP and show that each of these algorithms beats the already known heuristics. Computational experiments are made on standard instances.     

A variable neighborhood search heuristic for periodic routing problems

The aim of this paper is to propose a new heuristic for the Periodic Vehicle Routing Problem (PVRP) without time windows. The PVRP extends the classical Vehicle Routing Problem (VRP) to a planning horizon of several days. Each customer requires a certain number of visits within this time horizon while there is some flexibility on the exact days of the visits. Hence, one has to choose the visit days for each customer and to solve a VRP for each day. Our method is based on Variable Neighborhood Search (VNS). Computational results are presented, that show that our approach is competitive and even outperforms existing solution procedures proposed in the literature. Also considered is the special case of a single vehicle, i.e. the Periodic Traveling Salesman Problem (PTSP). It is shown that slight changes of the proposed VNS procedure is also competitive for the PTSP.     

Improving Christofides' lower bound for the traveling salesman problem

In 1972 Christofides introduced a lower bound for the Traveling Salesman Problem (TSP). The bound is based on solving repeatedly a Linear Assignment Problem. We relate the bound to the Complete Cycle Problem; as a consequence the correctness of the bound is easier to prove.

Further we give improvements for the bound in the symmetric case and we deal with the influence of the triangle equation together with the identification of non-optimal edges for the TSP. The improvements are illustrated by examples and computational results for large problems.     

Efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem

The generalized traveling salesman problem (GTSP) is a well-known combinatorial optimization problem with a host of applications. It is an extension of the Traveling Salesman Problem (TSP) where the set of cities is partitioned into so-called clusters, and the salesman has to visit every cluster exactly once.     

Decision diagrams for solving traveling salesman problems with pickup and delivery in real time

The Traveling Salesman Problem with Pickup and Delivery seeks a minimum cost path with pickups preceding deliveries. It is important in on-demand last-mile logistics, such as ride sharing and meal delivery. We examine the use of low-width Decision Diagrams in a branch-and-bound with and without Assignment Problem inference duals as a primal heuristic for finding good solutions within strict time budgets. We show these diagrams can be more effective than similarly structured hybrid Constraint Programming techniques for real-time decision making.