A heuristic search approach for solving multiobjectivenon-order-preserving path selection problems

We consider the problem of routing a vehicle making multiple intermediate stops, assuming a non-order-preserving, multiattribute reward structure. Sub-paths of optimal paths may not be optimal for such a reward structure, which may result from routing a pick-up and delivery vehicle carrying hazardous materials that is routed on the basis of minimizing cost and risk. We assume that a priori bounds exist on the rewards from the vehicle's current position to each of the intermediate destinations and to the depot through all the intermediate destinations that have yet to be visited. Precise calculations of these rewards would require additional computational effort. Two heuristic search algorithms, BU* and DU*, are developed and analyzed. Both algorithms satisfy termination, completeness, and admissibility properties. Results indicate that BU* is guaranteed to perform no worse given better heuristic information, a guarantee that cannot be made for DU*. Computational requirements are illustrated through examples based on a real network in northeast Ohio     

A heuristic method for large-scale multi-facility location problems

A heuristic method for solving large-scale multi-facility location problems is presented. The method is analogous to Cooper's method (SIAM Rev. 6 (1964) 37), using the authors’ single facility location method (Comput. Optim. Appl. 21 (2002) 213) as a parallel subroutine, and reassigning customers to facilities using the heuristic of nearest center reclassification. Numerical results are reported. Scope and purpose We study the multiple facility location problem (MFLP). The objective in MFLP is to locate facilities to serve optimally a given set of customers. MFLPs have many applications in Operations Research, and a rich literature, see Drezner (Location Sci. 3(4) (1995) 275) for a recent survey.MFLPs involve, in addition to the location decision, also the assignment of customers to facilities. The MFLP is therefore a special clustering problem, the clusters here are the sets of customers assigned to the same facility.We propose a parallel heuristic method for solving MFLPs, using ideas from cluster analysis (nearest mean reclassification (Cluster Analysis, 3rd Edition, Edward Arnold, London, 1993)), and the authors’ Newton bracketing method for convex minimization (Comput. Optim. Appl. 21 (2002) 213) as a subroutine. The method is suitable for large-scale problems, as illustrated by numerical examples.     

A numerical method for solving shortest path problems

Chebyshev pseudo-spectral method is one of the most efficient methods for solving continuous-time optimization problems. In this paper, we utilize this method to solve the general form of shortest path problem. Here, the main problem is converted into a nonlinear programming problem and by solving of which, we obtain an approximate shortest path. The feasibility of the nonlinear programming problem and the convergence of the method are given. Finally, some numerical examples are considered to show the efficiency of the presented method over the other methods.     

A recursive method for solving unconstrained tangentialinterpolation problems

An efficient recursive solution is presented for the one-sided unconstrained tangential interpolation problem. The method relies on the triangular factorization of a certain structured matrix that is implicitly defined by the interpolation data. The recursive procedure admits a physical interpretation in terms of discretized transmission lines. In this framework the generating system is constructed as a cascade of first-order sections. Singular steps occur only when the input data is contradictory, i.e., only when the interpolation problem does not have a solution. Various pivoting schemes can be used to improve numerical accuracy or to impose additional constraints on the interpolants. The algorithm also provides coprime factorizations for all rational interpolants and can be used to solve polynomial interpolation problems such as the general Hermite matrix interpolation problem. A recursive method is proposed to compute a column-reduced generating system that can be used to solve the minimal tangential interpolation problem     

A randomized method for solving discrete ill-posed problems

An approach is proposed to the stable solution of discrete ill-posed problems on the basis of a combination of random projection of the initial ill-conditioned matrix with an ill-defined numerical rank and the pseudo-inversion of the resultant matrix. To select the dimension of the projection matrix, we propose to use criteria for the selection of a model and a regularization parameter. The results of experimental studies based on the well-known examples of discrete ill-posed problems are presented. Their solution errors are close to the Tikhonov regularization error, but a matrix dimension reduction owing to projection reduces the expenditures for computations, especially at high noise levels.     

A heuristic approach to solving a class of bilinear matrix inequality problems

Many canonical and modern control problems can be recast into the problem of a group of matrix inequalities. Some of them are in the form of linear matrix inequalities (LMIs), which can be solved very efficiently by the powerful LMI toolbox in Matlab, but some others are in the form of bilinear matrix inequalities. The characteristic of this latter class of problems is that when the so called “communicating variables” are fixed, the overall problem will be reduced to the problem in LMIs. Thus, how to find the communicating variables is the key to solve the whole problem. In this paper, an optimal estimate for the communicating variables is presented. We will illustrate our method by completely solving the problems of overshoot bound control and reachable set analysis for uncertain systems. Numerical examples are provided to show the effectiveness of the proposed method.     

A new and efficient ant-based heuristic method for solving the traveling salesman problem

Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems.     

Variable neighborhood descent heuristic for solving reverse logistics multi-item dynamic lot-sizing problems

The multi-product dynamic lot sizing problem with product returns and recovery is an important problem that appears in reverse logistics and is known to be NP-hard. In this paper we propose an efficient variable neighborhood descent heuristic algorithm for solving this problem. Furthermore, we present a new benchmark set with the largest instances in the literature. The computational results demonstrate that our approach outperforms the state-of-the-art Gurobi optimizer.     

A GP-AHP method for solving group decision-making fuzzy AHP problems

The analytic hierarchy process (AHP) elicits a corresponding priority vector interpreting the preferred information from the decision-maker(s), based on the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale, in practice the decision-maker(s) usually give some or all pair-to-pair comparison values with an uncertainty degree rather than precise ratings. By employing the property of goal programming (GP) to treat a fuzzy AHP problem, this paper incorporates an absolute term linearization technique and a fuzzy rating expression into a GP-AHP model for solving group decision-making fuzzy AHP problems. In contrast to current fuzzy AHP methods, the GP-AHP method developed herein can concurrently tackle the pairwise comparison involving triangular, general concave and concave–convex mixed fuzzy estimates under a group decision-making environment.

Scope and purpose

Many real world decision problems involve multiple criteria in qualitative domains. As expected, such problems will be increasingly modeled as multiple criteria decision-making problems, which involve scoring on subjective/qualitative domains. This results in a class of significant problems for which an evaluation framework, which handles occurrences of seeming intransitivity and inconsistency will be required. Another interesting issue of group decision-making analysis is how to deal with disagreements between two or more different rankings within an alternative set. These phenomena are likely to appear in qualitative/subjective domains where the decision-making environment is ambiguous and vague. Therefore, this study proposes a GP-AHP model that is sufficiently robust to permit conflict and imprecision. Numerical examples demonstrate the effectiveness and applicability of the proposed models in deriving the most promising priority vector from a fuzzy AHP problem within a group decision-making environment.     

A heuristic quasi-physical strategy for solving disks packing problem

By elaborately simulating the movement of the smooth elastic disks in the container in the physical world, we can find the solution for the disks packing problem. This problem is a classical one that arises in many scientific and engineering fields, and it is also one of the NP hard problems. Based on the simulated annealing, i.e., imitating the displacements of the objects under different temperature, the calculation speed is improved. The performance of the algorithm is demonstrated by actual calculations.     

A fault-tolerant parallel heuristic for assignment problems

This paper presents a new approach for parallel heuristic algorithms based on adaptive parallelism. Adaptive parallelism was used to dynamically adjust the parallelism degree of the application with respect to the system load. This approach demonstrates that high-performance computing using a hundred of heterogeneous workstations combined with massively parallel machines is feasible to solve large optimization problems with respect to the personal character of workstations. The fault-tolerant algorithm allows a minimal loss of computation in case of failures. The proposed algorithm exploits the properties of this class of applications in order to reduce the complexity of the algorithm in terms of the checkpoint files size and the control messages exchanged. The parallel heuristic algorithm combines different search strategies: simulated annealing and tabu search. Encouraging results have been obtained in solving the quadratic assignment problem. We have improved the best known solutions for some large real-world problems.     

A general heuristic for vehicle routing problems

We present a unified heuristic which is able to solve five different variants of the vehicle routing problem: the vehicle routing problem with time windows (VRPTW), the capacitated vehicle routing problem (CVRP), the multi-depot vehicle routing problem (MDVRP), the site-dependent vehicle routing problem (SDVRP) and the open vehicle routing problem (OVRP).     

A parallel iterative method for solving symmetric tridiagonal extreme eigenpair problems

In this paper, we propose a parallel iterative method for calculating the extreme eigenpair (the largest or smallest eigenvalue and corresponding eigenvector) of a large symmetric tridiagonal matrix. It is based upon a divide and repeated, rank-one modification technique. The rank-one modification with a parameter only changes one diagonal element of each submatrix. We present a basic theory for subdividing the extremal eigenpair problem and then prove several convergence theorems that show the convergence of the iteration scheme for any positive initial modification parameter and the asymptotical quadratic convergence rate. Some numerical experiments are given, which show the efficiency of the parallel algorithm.     

A new method for solving boundary value problems for partial differential equations

This paper proposes a symmetry–iteration hybrid algorithm for solving boundary value problems for partial differential equations. First, the multi-parameter symmetry is used to reduce the problem studied to a simpler initial value problem for ordinary differential equations. Then the variational iteration method is employed to obtain its solution. The results reveal that the proposed method is very effective and can be applied for other nonlinear problems.