Exact computational approaches to a stochastic uncapacitated single allocation p-hub center problem

The stochastic uncapacitated single allocation p-hub center problem is an extension of the deterministic version which aims to minimize the longest origin-destination path in a hub and spoke network. Considering the stochastic nature of travel times on links is important when designing a network to guarantee the quality of service measured by a maximum delivery time for a proportion of all deliveries. We propose an efficient reformulation for a stochastic p-hub center problem and develop exact solution approaches based on variable reduction and a separation algorithm. We report numerical results to show effectiveness of our new reformulations and approaches by finding global solutions of small-medium sized problems. The combination of model reformulation and a separation algorithm is particularly noteworthy in terms of computational speed.     

Solving nested-constraint resource allocation problems with an interior point method

Efficient methods for convex resource allocation problems usually exploit algebraic properties of the objective function. For problems with nested constraints, we show that constraint sparsity structure alone allows rapid solution with a general interior point method. The key is a special-purpose linear system solver requiring only linear time in the problem dimensions. Computational tests show that this approach outperforms the previous best algebraically specialized methods.     

Solving large scale crew scheduling problems

The crew pairing problem is posed as a set partitioning zero-one integer program. Variables are generated as legal pairings meeting all work rules. Dual values obtained from solving successive large linear program relaxations are used to prune the search tree. In this paper we present a graph based branching heuristic applied to a restricted set partitioning problem representing a collection of ‘best’ pairings. The algorithm exploits the natural integer properties of the crew pairing problem. Computational results are presented to show realized crew cost savings.     

Tabu search for large location–allocation problems

Recently it has been demonstrated that the use of simulated annealing is a good alternative for solving the minisum location–allocation problem with rectilinear distances compared with other popular methods. In this study it is shown that the same solution quality and a great saving of computational time can be achieved by using tabu search. It is also possible to transfer this method to location–allocation problems with euclidean distances.     

Solving large set covering problems for crew scheduling

This paper presents a computationally effective heuristic method which produces good-quality solutions for large-scale set covering problems with thousands of constraints and about one million variables. The need to solve such large-scale problems arises from a crew scheduling problem of mass transit agencies where the number of work shifts required has to be minimized. This problem may be formulated as a large-scale non-unicost set covering problem whose rows are trips to be performed while columns stand for round trips. The proposed method is mainly based on lagragian relaxation and sub-gradient optimization. After the reduction of the number of rows and columns by the logical tests, “greedy” heuristic algorithms provide upper and lower bounds which are continuously improved to produce goodquality solutions. Computational results, regarding randomly generated problems and real life problems concerning crew scheduling at Italian Railways Company, show that good-quality solutions can be obtained at an acceptable computational cost. This work was supported by the project “Progetto Finalizzato Transporti 2” of National Research Council of Italy (C.N.R.) contract No. 94.01436PF74 and by “Ferrovie dello Stato S.p.A.”     

Solving large quadratic assignment problems on computational grids

The quadratic assignment problem (QAP) is among the hardest combinatorial optimization problems. Some instances of size n = 30 have remained unsolved for decades. The solution of these problems requires both improvements in mathematical programming algorithms and the utilization of powerful computational platforms. In this article we describe a novel approach to solve QAPs using a state-of-the-art branch-and-bound algorithm running on a federation of geographically distributed resources known as a computational grid. Solution of QAPs of unprecedented complexity, including the nug30, kra30b, and tho30 instances, is reported. Received: September 29, 2000 / Accepted: June 5, 2001?Published online October 2, 2001     

Solving (large scale) matching problems combinatorially

In this paper we describe computational results for a modification of the shortest augmenting path approach for solving large scale matching problems. Using a new assignment start procedure and the two-phase strategy, where first the problem is solved on a sparse subgraph and then reoptimization is used, matching problems on complete graphs with 1000 nodes are solved in about 10–15 seconds on an IBM 4361.This work was partially supported by Sonderforschungsbereich 303, University of Bonn, and a special grant from the Deutsche Forschungsgemeinschaft.     

A heuristic for solving large bin packing problems in two and three dimensions

The more-dimensional bin packing problem (BPP) considered here requires packing a set of rectangular-shaped items into a minimum number of identical rectangular-shaped bins. All items may be rotated and the guillotine cut constraint has to be respected. A straightforward heuristic is presented that is based on a method for the container loading problem following a wall-building approach and on a method for the one-dimensional BPP. 1,800 new benchmark instances are introduced for the two-dimensional and three-dimensional BPP. The instances include more than 1,500 items on average. Applied to these very large instances, the heuristic generates solutions of acceptable quality in short computation times. Moreover, the influence of different instance parameters on the solution quality is investigated by an extended computational study.     

Solving large scale Max Cut problems via tabu search

In recent years many algorithms have been proposed in the literature for solving the Max-Cut problem. In this paper we report on the application of a new Tabu Search algorithm to large scale Max-cut test problems. Our method provides best known solutions for many well-known test problems of size up to 10,000 variables, although it is designed for the general unconstrained quadratic binary program (UBQP), and is not specialized in any way for the Max-Cut problem.     

Solving general continuous single facility location problems by cutting planes

A unified cutting plane method is proposed to solve a large class of continuous single facility location problems. These include both minisum and minimax problems with mixed norms and convex transportation costs. The method provides an easy stopping rule and permits the post optimal determination of an outer approximation to near optimality regions. Two examples are given which show the power of the method. Extensive computational results in dimension two and higher are reported.     

Solving matching problems with linear programming

In this paper we describe an implementation of a cutting plane algorithm for the perfect matching problem which is based on the simplex method. The algorithm has the following features:

-It works on very sparse subgraphs ofK _n which are determined heuristically, global optimality is checked using the reduced cost criterion.

-Cutting plane recognition is usually accomplished by heuristics. Only if these fail, the Padberg-Rao procedure is invoked to guarantee finite convergence.

Our computational study shows that—on the average—very few variables and very few cutting planes suffice to find a globally optimal solution. We could solve this way matching problems on complete graphs with up to 1000 nodes. Moreover, it turned out that our cutting plane algorithm is competitive with the fast combinatorial matching algorithms known to date.     

A new attack on RSA with two or three decryption exponents

Let N=pq be an RSA modulus, i.e. the product of two large unknown primes of equal bit-size. In this paper, we describe an attack on RSA in the presence of two or three exponents e _i with the same modulus N and satisfying equations e _i x _i ??(N)y _i =z _i , where ?(N)=(p?1)(q?1) and x _i , y _i , z _i are unknown parameters. The new attack is an extension of Guo’s continued fraction attack as well as the Blömer and May lattice-reduction basis attack.     

Solving network manpower problems with side constraints

A number of authors have used goal-programming to solve manpower problems. Some of these authors have also formulated goal-programming manpower models in a network so as to take advantage of the rapid codes now available for the solution of capacitated transportation problems. In this paper, a number of methods of dealing with side constraints (those not included in the network formulation) are explored. Of particular interest are constraints for dealing with attrition and for expressing budgetary restrictions.     

Solving arc routing problems with turn penalties

In several arc routing problems, it is necessary to take turn penalties into account when designing a solution. Traditionally, this is done through a transformation of the arc routing problem into an equivalent vertex routing problem. In this paper it is shown that a more direct approach, not resorting to such a transformation, may be more efficient.     

Solving routing problems with pairwise synchronization constraints

Pairwise route synchronization constraints are commonly encountered in the field of service technician routing and scheduling and in the area of mobile care. Pairwise route synchronization refers to constraints that require that two technicians or home care workers visit the same location at exactly the same time. We consider constraints of this type in the context of the well-known vehicle routing problem with time windows and a generic service technician routing and scheduling problem. Different approaches for dealing with the problem of pairwise route synchronization are compared and several ways of integrating a synchronization component into a metaheuristic algorithm tailored to the original problems are analyzed. When applied to benchmark instances from the literature, our algorithm matches almost all available optimal values and it produces several new best results for the remaining instances.

     

Solving sparse linear least-squares problems on some supercomputers by using large dense blocks

Efficient subroutines for dense matrix computations have recently been developed and are available on many high-speed computers. On some computers the speed of many dense matrix operations is near to the peak-performance. For sparse matrices storage and operations can be saved by operating only and storing only nonzero elements. However, the price is a great degradation of the speed of computations on supercomputers (due to the use of indirect addresses, to the need to insert new nonzeros in the sparse storage scheme, to the lack of data locality, etc.). On many high-speed computers a dense matrix technique is preferable to sparse matrix technique when the matrices are not large, because the high computational speed compensates fully the disadvantages of using more arithmetic operations and more storage. For very large matrices the computations must be organized as a sequence of tasks in each of which a dense block is treated. The blocks must be large enough to achieve a high computational speed, but not too large, because this will lead to a large increase in both the computing time and the storage. A special “locally optimized reordering algorithm” (LORA) is described, which reorders the matrix so that dense blocks can be constructed and treated with some standard software, say LAPACK or NAG. These ideas are implemented for linear least-squares problems. The rectangular matrices (that appear in such problems) are decomposed by an orthogonal method. Results obtained on a CRAY C92A computer demonstrate the efficiency of using large dense blocks.     

Solving nonlinear programming problems via a homotopy continuation method under three unbounded conditions

In this paper, we give three unbounded conditions under which we are able to solve the nonlinear programming problems in unbounded sets by a homotopy continuation method. In addition, we also discuss their relations.