Tabu search for solving the black-and-white travelling salesman problem

The black-and-white travelling salesman problem (BWTSP) is an extension to the well-known TSP by partitioning the set of vertices into black and white vertices, and imposing cardinality and length constraints between two consecutive black vertices in a Hamiltonian tour. BWTSP has various applications in aircraft routing, telecommunication network design and logistics. In this paper, we develop several tabu search (TS) heuristics for solving the BWTSP. Our TS is built upon a new efficient neighbourhood structure, which exploits both the permutation and knapsack features of BWTSP. We also embed our TS as a heuristic procedure to improve the upper bound in a mixed-integer linear programming method. Extensive computational experiment on both benchmark and randomly generated instances shows effectiveness and efficiency of our algorithms. Our algorithms are able to obtain optimal and near optimal solutions to small instances in seconds, and find feasible solutions to large instances that have not been solved by the existing methods in the literature.     

Solution of large-scale symmetric travelling salesman problems

In this paper we report on a cutting plane procedure with which we solved symmetric travelling salesman problems of up to 1000 cities to optimality. Our implementation is based on a fast LP-solver (IBM's MPSX) and makes effective use of polyhedral results on the symmetric travelling salesman polytope. We describe the important ingredients of our code and give an extensive documentation of its computational performance.Supported by DFG-Schwerpunkt Anwendungsbezogene Optimierung und Steuerung Universität Augsburg, Germany.Supported by SFB 303 (DFG), Forschungsinstitut für Diskrete Mathematik, Institut für Operations Research, Universität Bonn, Germany.     

On patching algorithms for random asymmetric travelling salesman problems

Let the arc-lengthsL _ij of a complete digraph onn vertices be independent uniform [0, 1] random variables. We consider the patching algorithm of Karp and Steele for the travelling salesman problem on such a digraph and give modifications which tighten the expected error. We extend these ideas to thek-person travelling salesman problem and also consider the case where cities can be visited more than once.     

Special cases of travelling salesman problems and heuristics

We discuss some classes of travelling salesman problems with sum and bottleneck objectives which can be solved efficiently and pose the question how such special cases can lead to good heuristics.Partial support from Austrian Science Foundation (Fonds zur Förderung der wissenschaftlichen Forschung), Projekt S 32/01 is gratefully acknowledged.     

Extending and relating different approaches for solving fuzzy quadratic problems

Quadratic programming problems are applied in an increasing variety of practical fields. As ambiguity and vagueness are natural and ever-present in real-life situations requiring solutions, it makes perfect sense to attempt to address them using fuzzy quadratic programming problems. This work presents two methods used to solve linear problems with uncertainties in the set of constraints, which are extended in order to solve fuzzy quadratic programming problems. Also, a new quadratic parametric method is proposed and it is shown that this proposal contains all optimal solutions obtained by the extended approaches with their satisfaction levels. A few numerical examples are presented to illustrate the proposed method.     

Universal conditions for algebraic travelling salesman problems to be efficiently solvable

We consider Travelling Salesman Problems (TSPs) where the cost of a tour is an algebraic composition of the cost coefficients that are elements of a totally ordered, commutative semigroup. Conditions for the cost matrix are stated which allow to solve these problems in polynomial time. In particular, we investigate conditions which guarantee that an optimal tour is pyramidal and can therefore be determined in O(n ²) time. Furthermore, we discuss TSP_s with Brownian as well as those with left-upper-triangular cost matrices.     

Probabilistic analysis of solving the assignment problem for the traveling salesman problem

This paper deals with probabilistic analysis of optimal solutions of the asymmetric traveling salesman problem. The exact distribution for the number of required next-best solutions of the assignment problem with random data in order to find an optimal tour is given. For every n-city asymmetric problem, there exists an algorithm such that (i) with probability 1 ? s, s?(0,1) the algorithm produces an optimal tour, (ii) it runs in time $\text{O}\text{(}\text{n}{}^4\text{3}\text{)}$, and (iii) it requires less than w((w + n ? 1)$\text{log}\text{(w + n ? 1) + w + 1) +}\text{1}\text{6}\text{w(n}{}^3\text{+ 3n}{}^2\text{+ 2n ? 6)}$ computational steps, where w = log(s)/log(1 ? E_n); E_n ?(0,1) is given by a simple mathematical formula. Additionally, the polynomial of (iii) gives the exact (deterministic) execution time to find w =1 ,2…. next-best solutions of the assignment problem.     

On two geometric problems related to the travelling salesman problem

The degree-K Minimum Spanning Tree (MST) problem asks for the minimum length spanning tree that has no vertex of degree greater than K. The Euclidean degree-K MST problem is known to be tractable for K ? 5; the degree-2 MST is simply the Euclidean path-TSP, which is NP-complete. It is proved that the Euclidean degree-3 MST problem is also NP-complete, thus leaving open only the case for K = 4. Among the most illustrious approximation algorithms is the heuristic for the Euclidean TSP due to Christofides. It is proved that implementing the “shortcutting phase” of Christofides' algorithm optimally is NP-hard (even so, Christofides' algorithm guarantees a tour which is no more than 50% longer than the optimal one).     

POPMUSIC for the travelling salesman problem

POPMUSIC— Partial OPtimization Metaheuristic Under Special Intensification Conditions — is a template for tackling large problem instances. This metaheuristic has been shown to be very efficient for various hard combinatorial problems such as p-median, sum of squares clustering, vehicle routing, map labelling and location routing. A key point for treating large Travelling Salesman Problem (TSP) instances is to consider only a subset of edges connecting the cities. The main goal of this article is to present how to build a list of good candidate edges with a complexity lower than quadratic in the context of TSP instances given by a general function. The candidate edges are found with a technique exploiting tour merging and the POPMUSIC metaheuristic. When these candidate edges are provided to a good local search engine, high quality solutions can be found quite efficiently. The method is tested on TSP instances of up to several million cities with different structures (Euclidean uniform, clustered, 2D to 5D, grids, toroidal distances). Numerical results show that solutions of excellent quality can be obtained with an empirical complexity lower than quadratic without exploiting the geometrical properties of the instances.     

Duality in fuzzy quadratic programming with exponential membership functions

In this paper, we have presented fuzzy primal-dual quadratic programming problems and proved appropriate duality results taking exponential membership function.     

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.     

A hierarchical strategy for solving traveling salesman problems using elastic nets

In this article, we focus on implementing the elastic net method to solve the traveling salesman problem using a hierarchical approach. The result is a significant speed-up, which is studied both analytically and experimentally.     

A parallel branch and bound algorithm for solving large asymmetric traveling salesman problems

A parallel branch and bound algorithm that solves the asymmetric traveling salesman problem to optimality is described. The algorithm uses an assignment problem based lower bounding technique, subtour elimination branching rules, and a subtour patching algorithm as an upper bounding procedure. The algorithm is organized around a data flow framework for parallel branch and bound. The algorithm begins by converting the cost matrix to a sparser version in such a fashion as to retain the optimality of the final solution. Computational results are presented for three different classes of problem instances: (1) matrix elements drawn from a uniform distribution of integers for instances of size 250 to 10 000 cities, (2) instances of size 250 to 1000 cities that concentrate small elements in the upper left portion of the cost matrix, and (3) instances of size 300 to 3000 cities that are designed to confound neighborhood search heuristics.     

A self-organising model for the travelling salesman problem

This work describes a new algorithm, based on a self-organising neural network approach, to solve the Travelling Salesman Problem (TSP). Firstly, various features of the available adaptive neural network algorithms for TSP are reviewed and a new algorithm is proposed. In order to investigate the performance of the algorithms, a comprehensive empirical study has been provided. The simulations, which are conducted on a series of standard data, evaluate the overall performance of this approach by comparing the results with the best known or the optimal solutions of the problems. The proposed algorithm shows significant advances in both the quality of the solution and computational effort for most of the experimental data. The deviation from the optimal solution of this algorithm was, in the worst case, around 2%. This fact indicates that the self-organising neural network may be regarded as a promising heuristic approach for optimisation problems.     

Gap functions and penalization for solving equilibrium problems with nonlinear constraints

The paper deals with equilibrium problems (EPs) with nonlinear convex constraints. First, EP is reformulated as a global optimization problem introducing a class of gap functions, in which the feasible set of EP is replaced by a polyhedral approximation. Then, an algorithm is given for solving EP through a descent type procedure, which exploits also exact penalty functions, and its global convergence is proved. Finally, the algorithm is tested on a network oligopoly problem with nonlinear congestion constraints.