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2009, Lecture Notes in Computer Science
Given a universal constant k, the multiple Stack Travelling Salesman Problem (kSTSP in short) consists in finding a pickup tour T 1 and a delivery tour T 2 of n items on two distinct graphs. The pickup tour successively stores the items at the top of k containers, whereas the delivery tour successively picks the items at the current top of the containers: thus, the couple of tours are subject to LIFO ("Last In First Out") constraints. This paper aims at finely characterizing the complexity of kSTSP in regards to the complexity of TSP. First, we exhibit tractable sub-problems: on the one hand, given two tours T 1 and T 2 , deciding whether T 1 and T 2 are compatible can be done within polynomial time; on the other hand, given an ordering of the n items into the k containers, the optimal tours can also be computed within polynomial time. Note that, to the best of our knowledge, the only family of combinatorial precedence constraints for which constrained TSP has been proven to be in P is the one of PQ-trees, [2]. Finally, in a more prospective way and having in mind the design of approximation algorithms, we study the relationship between optimal value of different TSP problems and the optimal value of kSTSP.
Networks, 2013
This article studies the double traveling salesman problem with two stacks. A number of requests have to be served where each request consists in the pickup and delivery of an item. All the pickup operations have to be performed before any delivery can take place. A single vehicle is available that starts from a depot, performs all the pickup operations and returns to the depot. Then, it performs all the delivery operations and returns to the depot. The items are loaded in two stacks, each served independently from the other with a last-in-first-out policy. The objective is the minimization of the total cost of the pickup and delivery tours. We propose a branchand-bound approach to solve the problem. The algorithm uses properties of the problem both to tighten the lower bounds and to avoid the exploration of redundant subtrees. Computational results performed on benchmark instances reveal that the algorithm outperforms the other exact approaches for this problem.
Lecture Notes in Computer Science, 2012
In the uncapacitated asymmetric traveling salesman with multiple stacks, we perform a hamiltonian circuit to pick up n items, storing them in a vehicle with k stacks satisfying last-in-first-out constraints, and then we deliver every item by performing a hamiltonian circuit. We are interested in the convex hull of the (arc-)incidence vectors of such couples of hamiltonian circuits. For the general case, we determine the dimension of this polytope, and show that every facet of the asymmetric traveling salesman polytope defines one of its facets. For the special case with two stacks, we provide an integer linear programming formulation whose linear relaxation is polynomial-time solvable, and we propose new families of valid inequalities to reinforce this linear relaxation.
Theoretical Computer Science, 2014
In TSP with profits we have to find an optimal tour and a set of customers satisfying a specific constraint. In this paper we analyze three different variants of TSP with profits that are NP-hard in general. We study their computational complexity on special structures of the underlying graph, both in the case with and without service times to the customers. We present polynomial algorithms for the polynomially solvable cases and fully polynomial time approximation schemes (fptas) for some NP-hard cases.
Journal of the Operations Research Society of Japan
We collsider a generalization of the classical traveling salesman problem (TSP) called the precedence constrained traveling salesman problem (PCTSP), i.e. given a directed complete graph GCV,E), a distance Dij on each arc (i,j') E E, precedeilce constraints K oii V, we want te find a minimum distance tour that starts node 1 E V, visits all the nodes in V-{1}, and returns iiode 1 again so that node i i's vislted befbre node j' when i K)', We present a branch and bound algorithm for the exact solutions to the PCTSP incorporating lovver bounds computed from the Lagrangean relaxation. Our Iower bounding procedure is a generalization of the restricted Lagrangean method that lxas been successfully adapted to the TSP by Balas and Christofides [2]. Our branch and bound algoritlun also incorporates several heuristics and variable reduction tests. The computational results with up to 49 nodes show that our algorithm computes exact solutions to severa} classes of precedence constraints within acceptable cornputational requirements.
INFORMS Journal on Computing, 2013
The double traveling salesman problem with multiple stacks is a variant of the pickup and delivery traveling salesman problem in which all pickups must be completed before any delivery. In addition, items can be loaded on multiple stacks in the vehicle, and each stack must obey the last-in-first-out policy. The problem consists of finding the shortest Hamiltonian cycles covering all pickup and delivery locations while ensuring the feasibility of the loading plan. We formulate the problem as two traveling salesman problems linked by infeasible path constraints. We also introduce several strengthenings of these constraints, which are used within a branch-and-cut algorithm. Computational results performed on instances from the literature show that the algorithm outperforms existing exact algorithms. Instances with up to 28 requests (58 nodes) have been solved to optimality.
Networks, 2015
In the traveling salesman problem with pickup, delivery, and ride-time constraints (TSPPD-RT), a vehicle located at a depot is required to service a number of requests where the requests are known before the route is formed. Each request consists of (i) a pickup location (origin), (ii) a delivery location (destination), and (iii) a maximum allowable travel time from the origin to the destination (maximum ride-time). The problem is to design a tour for the vehicle that (i) starts and ends at the depot, (ii) services all requests, (iii) ensures that each request's ride-time does not exceed its maximum ride-time, and (iv) minimizes the total travel time required by the vehicle to service all requests (objective function). A capacity constraint that may be present is that the weight or volume of the undelivered requests on the vehicle must always be no greater than the vehicle's capacity. In this article, we concurrently analyze the TSPPD-RT with capacity constraints and without capacity constraints. We describe two mathematical formulations of the problem. These formulations are used to derive new lower bounds on the solution to the problem. Then, we provide two exact methods for finding the optimal route that minimizes the total travel cost. Our extensive computational analysis on both versions of the TSPPD-RT shows that the proposed algorithms are capable of solving to optimality instances involving up to 50 requests.
Internet Mathematics, 2008
Let G = (V, E) be a graph modeling a network in which each edge is owned by a selfish agent, which establishes the cost for traversing its edge (i.e., assigns a weight to its edge) by pursuing only its personal utility. In such a setting, we aim at designing approximate truthful mechanisms for several NP-hard traversal problems on G, such as the graphical traveling salesman problem, the rural postman problem, and the mixed Chinese postman problem, each of which in general requires an edge of G to be used several times. Thus, in game-theoretic terms, these are one-parameter problems, but with a peculiarity: the workload of each agent is a natural number. In this paper we refine the classical notion of monotonicity of an algorithm so as to capture exactly this property, and we then provide a general mechanism-design technique that guarantees this monotonicity and that allows one to compute efficiently the corresponding payments. In this way, we show that the former two problems and the latter one admit a 3/2-and a 2-approximate truthful mechanism, respectively. Thus, for the first two problems we match the best known approximation ratios holding for their corresponding centralized versions, while for the third one we are only a 4/3-factor away from it. 1. The graphical traveling salesman problem (GTSP): assuming that G is undirected, find a minimum-cost spanning tour of G.
Networks, 2003
In this paper, we describe a new integer programming formulation for the Traveling Salesman Problem with mixed Deliveries and Collections (TSPDC) based on a two-commodity network flow approach. We present new lower bounds that are derived from the linear relaxation of the new formulation by adding valid inequalities, in a cutting-plane fashion. The resulting lower bounds are embedded in a branch-and-cut algorithm for the optimal solution of the TSPDC. Computational results on different classes of test problems taken from the literature indicate the effectiveness of the proposed method.
2011
The Double Traveling Salesman Problem with Multiple Stacks is a vehicle routing problem in which pickups and deliveries must be performed in two independent networks. The items are stored in stacks and repacking is not allowed. Given a pickup and a delivery tour, the problem of checking if there exists a valid distribution of items into s stacks of size h that is consistent with the given tours, is known as Pickup and Delivery Tour Combination (PDTC) problem.
Traveling Salesman Problem, Theory and Applications, 2010
Naval Research Logistics, 1999
We consider the Capacitated Traveling Salesman Problem with Pickups and Deliveries (CTSPPD). This problem is characterized by a set of n pickup points and a set of n delivery points. A single product is available at the pickup points which must be brought to the delivery points. A vehicle of limited capacity is available to perform this task. The problem is to determine the tour the vehicle should follow so that the total distance traveled is minimized, each load at a pickup point is picked up, each delivery point receives its shipment and the vehicle capacity is not violated. We present two polynomial-time approximation algorithms for this problem and analyze their worst-case bounds.
2003
In the Generalized Traveling Salesman Problem (GTSP), given a weighted complete digraph D and a partition V 1 ,. .. , V k of the vertices of D, we are to find a minimum weight cycle containing exactly one (at least one) vertex from each set V i , i = 1,. .. , k. Assignment Problem based approaches are extensively used for the Asymmetric TSP. To use analogs of these approaches for the GTSP, we need to find a minimum weight 1-regular subdigraph that contains exactly one (at least one) vertex from each V i. We prove that, unfortunately, the corresponding problems are NP-hard. In fact, we show the following stronger result: Let D = (V, A) be a digraph and let V 1 , V 2 ,. .. , V k be a partition of V. The problem of checking whether D has a 1-regular subdigraph containing exactly one vertex from each V 1 , V 2 ,. .. , V k is NP-complete even if |V i | ≤ 2 for every i = 1, 2,. .. , k.
We consider labeled Traveling Salesman Problems, defined upon a complete graph of n vertices with colored edges. The objective is to find a tour of maximum (or minimum) number of colors. We derive results regarding hardness of approximation, and analyze approximation algorithms for both versions of the problem. For the maximization version we give a 1 2 -approximation algorithm and show that it is APXhard. For the minimization version, we show that it is not approximable within n 1−ǫ for every ǫ > 0. When every color appears in the graph at most r times and r is an increasing function of n the problem is not O(r 1−ǫ )-approximable. For fixed constant r we analyze a polynomialtime (r + Hr)/2-approximation algorithm (Hr is the r-th harmonic number), and prove APX-hardness. Analysis of the studied algorithms is shown to be tight.
Discrete Applied Mathematics, 2009
Deciding whether or not a feasible solution to the Traveling Salesman Problem with Pickups and Deliveries (TSPPD) exists is polynomially solvable. We prove that counting the number of feasible solutions of the TSPPD is hard by showing the problem is #P-complete.
International Journal of Research, 2018
The Traveling Salesman Problem (TSP) is a classical combinatorial optimization problem, which is simple to state but very difficult to solve. The problem is to find the shortest tour through a set of N vertices so that each vertex is visited exactly once. This problem is known to be NP-hard, and cannot be solved exactly in polynomial time. Many exact and heuristic algorithms have been developed in the field of operations research (OR) to solve this problem. In this paper we provide overview of different approaches used for solving travelling salesman problem.
Operations Research Letters, 1999
We consider the Ordered Cluster Traveling Salesman Problem OCTSP. In this problem, a vehicle starting and ending at a given depot must visit a set of n points. The points are partitioned into K , K n, prespeci ed clusters. The vehicle must rst visit the points in cluster 1, then the points in cluster 2, : : : , and nally the points in cluster K so that the distance traveled is minimized. We present a 5 3-approximation algorithm for this problem which runs in On 3 time. We show that our algorithm can also be applied to the path version of the OCTSP: the Ordered Cluster Traveling Salesman Path Problem OCTSPP. Here the di erent starting and ending points of the vehicle may o r m a y not be prespeci ed. For this problem, our algorithm is also a 5 3-approximation algorithm.
ACM SIGACT News, 2009
We consider labeled Traveling Salesman Problems, defined upon a complete graph of n vertices with colored edges. The objective is to find a tour of maximum (or minimum) number of colors. We derive results regarding hardness of approximation, and analyze approximation algorithms for both versions of the problem. For the maximization version we give a 1 2 -approximation algorithm and show that it is APXhard. For the minimization version, we show that it is not approximable within n 1−ǫ for every ǫ > 0. When every color appears in the graph at most r times and r is an increasing function of n the problem is not O(r 1−ǫ )-approximable. For fixed constant r we analyze a polynomialtime (r + Hr)/2-approximation algorithm (Hr is the r-th harmonic number), and prove APX-hardness. Analysis of the studied algorithms is shown to be tight.
This paper introduces an additive branch-and-bound algorithm for a variant of the pickup and delivery traveling salesman problem in which loading and unloading operations have to be performed in a Last-In-First-Out (LIFO) order. Two relaxations are used within the additive approach: the assignment problem and the shortest spanning rarborescence problem. The quality of the lower bounds is further improved by a set of elimination rules applied at each node of the search tree to remove from the problem arcs that cannot belong to feasible solutions because of precedence relationships. The performance of the algorithm and the effectiveness of the elimination rules are assessed on instances from the literature.
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