A new algorithm for the assignment problem

We propose a new algorithm for the classical assignment problem. The algorithm resembles in some ways the Hungarian method but differs substantially in other respects. The average computational complexity of an efficient implementation of the algorithm seems to be considerably better than the one of the Hungarian method. In a large number of randomly generated problems the algorithm has consistently outperformed an efficiently coded version of the Hungarian method by a broad margin. The factor of improvement increases with the problem dimensionN and reaches an order of magnitude forN equal to several hundreds.Work supported by Grant NSF ENG-7906332.     

A new algorithm for the propositional satisfiability problem

A new enumeration algorithm is proposed for the propositional satisfiability problem. Such algorithm is based on a hypergraph formulation of the problem. Two different implementations of the algorithm are presented together with the results of an experimentation intended to compare their performance with the performance of other known methods. The computational results obtained are quite promising.     

A new greedy algorithm for the quadratic assignment problem

The classical greedy algorithm for discrete optimization problems where the optimal solution is a maximal independent subset of a finite ground set of weighted elements, can be defined in two ways which are dual to each other. The Greedy-In where a solution is constructed from the empty set by adding the next best element, one at a time, until we reach infeasibility, and the Greedy-Out where starting from the ground set we delete the next worst element, one at a time, until feasibility is reached. It is known that while the former provides an approximation ratio for maximization problems, its worst case performance is not bounded for minimization problems, and vice-versa for the later. However the Greedy-Out algorithm requires an oracle for checking the existence of a maximal independent subset which for most discrete optimization problems is a difficult task. In this work we present a Greedy-Out algorithm for the quadratic assignment problem by providing a combinatorial characterization of its solutions.     

A new branch-and-price algorithm for the traveling tournament problem

The traveling tournament problem (ttp) consists of finding a distance-minimal double round-robin tournament where the number of consecutive breaks is bounded. For solving the problem exactly, we propose a new branch-and-price approach. The starting point is a new compact formulation for the ttp. The corresponding extensive formulation resulting from a Dantzig-Wolfe decomposition is identical to one given by Easton, K., Nemhauser, G., Trick, M., 2003. Solving the traveling tournament problem: a combined interger programming and constraint programming approach. In: Burke, E., De Causmaecker, P. (Eds.), Practice and Theory of Automated Timetabling IV, Volume 2740 of Lecture Notes in Computer Science, Springer Verlag Berlin/Heidelberg, pp. 100–109, who suggest to solve the tour-generation subproblem by constraint programming. In contrast to their approach, our method explicitly utilizes the network structure of the compact formulation: First, the column-generation subproblem is a shortest-path problem with additional resource and task-elementarity constraints. We show that this problem can be reformulated as an ordinary shortest-path problem over an expanded network and, thus, be solved much faster. An exact variable elimination procedure then allows the reduction of the expanded networks while still guaranteeing optimality. Second, the compact formulation gives rise to supplemental branching rules, which are needed, since existing rules do not ensure integrality in all cases. Third, non-repeater constraints are added dynamically to the master problem only when violated. The result is a fast exact algorithm, which improves many lower bounds of knowingly hard ttp instances from the literature. For some instances, solutions are proven optimal for the first time.     

A new algorithm for solving the general quadratic programming problem

For the general quadratic programming problem (including an equivalent form of the linear complementarity problem) a new solution method of branch and bound type is proposed. The branching procedure uses a well-known simplicial subdivision and the bound estimation is performed by solving certain linear programs.     

A new algorithm for the small-field astrometric point-pattern matching problem

The small-field astrometric point-pattern matching problem is a fundamental problem in astronomy, that although considered a long time ago still lacks a formal definition. It can be textually stated as: given two lists of positions of stars, find the common stars between these lists, taking into account rotation, translation, reflection and scaling operations. It is expected that there might be missing stars between the lists. In this work, we give the astronomical context, review some heuristic methods adopted in the literature, present distance matrix formulations for the problem and propose a new algorithm to solve it.     

A new approximation algorithm for the multilevel facility location problem

In this paper we propose a new integer programming formulation for the multilevel facility location problem and a novel 3-approximation algorithm based on LP-rounding. The linear program that we use has a polynomial number of variables and constraints, thus being more efficient than the one commonly used in the approximation algorithms for these types of problems.     

A new extension principle algorithm for the traveling salesman problem

In this paper, an algorithm for solving the asymmetric traveling salesman problem is developed and tested by computation. This algorithm is based on the extension principle by Schoch and uses the assignment problem relaxation of the traveling salesman problem for computing lower bounds. Computational experience with randomly generated test problems indicate that the present algorithm yields good results in runtime which are comparable with the results of Smith/Srinivasan/Thompson. Computational experience are reported for up to 120-node problems with uniformly distributed and approximately normally distributed cost.     

A new branch-and-cut algorithm for the capacitated vehicle routing problem

We present a new branch-and-cut algorithm for the capacitated vehicle routing problem (CVRP). The algorithm uses a variety of cutting planes, including capacity, framed capacity, generalized capacity, strengthened comb, multistar, partial multistar, extended hypotour inequalities, and classical Gomory mixed-integer cuts. For each of these classes of inequalities we describe our separation algorithms in detail. Also we describe the other important ingredients of our branch-and-cut algorithm, such as the branching rules, the node selection strategy, and the cut pool management. Computational results, for a large number of instances, show that the new algorithm is competitive. In particular, we solve three instances (B-n50-k8, B-n66-k9 and B-n78-k10) of Augerat to optimality for the first time.     

A new variable dimension algorithm for the fixed point problem

We propose a new variable dimension algorithm for solving a system ofn equations inn variables. The algorithm solves a sequence of subproblems consisting of the first several equations, the dimension of subproblems does not, however, increase monotonically. A convergence condition is also presented.     

A new algorithm for the undesirable 1-center problem on networks

Recent papers have developed efficient algorithms for the location of an undesirable (obnoxious) 1-center on general networks with n nodes and m edges. Even though the theoretical complexity of these algorithms is fine, the computational time required to get the solution can be diminished using a different model formulation and slightly improving the upper bounds. Thus, we present a new O(mn) algorithm, which is more straightforward and computationally faster than the previous ones. Computing time results comparing the former approaches with the proposed algorithm are supplied.     

A new hybrid genetic algorithm for the capacitated vehicle routing problem

Recently proved successful for variants of the vehicle routing problem (VRP) involving time windows, genetic algorithms have not yet shown to compete or challenge current best search techniques in solving the classical capacitated VRP. A new hybrid genetic algorithm to address the capacitated VRP is proposed. The basic scheme consists in concurrently evolving two populations of solutions to minimize total travelled distance using genetic operators combining variations of key concepts inspired from routing techniques and search strategies used for a time variant of the problem to further provide search guidance while balancing intensification and diversification. Results from a computational experiment over common benchmark problems report the proposed approach to be very competitive with the best-known methods.     

A new algorithm for the solution of the linear minimax approximation problem

This paper introduces an efficient approach to the solution of the linear mini-max approximation problem. The classical nonlinear minimax problem is cast into a linear formulation. The proposed optimization procedure consists of specifying first a feasible point belonging to the feasible boundary surface. Next, feasible directions of decreasing values of the objective function are determined. The algorithm proceeds iteratively and terminates when the absolute minimum value of the objective function is reached. The initial point May be selected arbitrarily or it May be optimally determined through a linear method to speed up algorithmic convergence. The algorithm was applied to a number of approximation problems and results were compared to those derived using the revised simplex method. The new algorithm is shown to speed up the problem solution by at least on order of magnitude.     

A new heuristic algorithm solving the linear ordering problem

The linear ordering problem is an NP-hard combinatorial problem with a large number of applications. Contrary to another very popular problem from the same category, the traveling salesman problem, relatively little space in the literature has been devoted to the linear ordering problem so far. This is particularly true for the question of developing good heuristic algorithms solving this problem.In the paper we propose a new heuristic algorithm solving the linear ordering problem. In this algorithm we made use of the sorting through insertion pattern as well as of the operation of permutation reversal. The surprisingly positive effect of the reversal operation, justified in part theoretically and confirmed in computational examples, seems to be the result of a unique property of the problem, called in the paper the symmetry of the linear ordering problem. This property consists in the fact that if a given permutation is an optimal solution of the problem with the criterion function being maximized, then the reversed permutation is a solution of the problem with the same criterion function being minimized.     

A new branch and bound algorithm for cell formation problem

Cell formation (CF) is the first and the most important problem in designing cellular manufacturing systems. Due to its non-polynomial nature, various heuristic and metaheuristic algorithms have been proposed to solve CF problem. Despite the popularity of heuristic algorithms, few studies have attempted to develop exact algorithms, such as branch and bound (B&B) algorithms, for this problem. We develop three types of branch and bound algorithms to deal with the cell formation problem. The first algorithm uses a binary branching scheme based on the definitions provided for the decision variables. Unlike the first algorithm, which relies on the mathematical model, the second one is designed based on the structure of the cell formation problem. The last algorithm has a similar structure to the second one, except that it has the ability to eliminate duplicated nodes in branching trees. The proposed branch and bound algorithms and a hybrid genetic algorithm are compared through some numerical examples. The results demonstrate the effectiveness of the modified problem-oriented branch and bound algorithm in solving relatively large size cell formation problems.     

A new tabu search algorithm for the vehicle routing problem with backhauls

In the distribution of goods from a central depot to geographically dispersed customers happens quite frequently that some customers, called linehauls, receive goods from that depot while others, named backhauls, send goods to it. This situation is described and studied by the vehicle routing problem with backhauls. In this paper we present a new tabu search algorithm that starting from pseudo-lower bounds was able to match almost all the best published solutions and to find many new best solutions, for a large set of benchmark problems.     

A polynomial-time algorithm for the change-making problem

Optimally making change—representing a given value with the fewest coins from a set of denominations—is in general NP-hard. In most real money systems however, the greedy algorithm is optimal. We give a polynomial-time algorithm to determine, for a given coin system, whether the greedy algorithm is optimal.