Optimizing fuzzy p-hub center problem with generalized value-at-risk criterion

In this study, we reduce the uncertainty embedded in secondary possibility distribution of a type-2 fuzzy variable by fuzzy integral, and apply the proposed reduction method to p-hub center problem, which is a nonlinear optimization problem due to the existence of integer decision variables. In order to optimize p-hub center problem, this paper develops a robust optimization method to describe travel times by employing parametric possibility distributions. We first derive the parametric possibility distributions of reduced fuzzy variables. After that, we apply the reduction methods to p-hub center problem and develop a new generalized value-at-risk (VaR) p-hub center problem, in which the travel times are characterized by parametric possibility distributions. Under mild assumptions, we turn the original fuzzy p-hub center problem into its equivalent parametric mixed-integer programming problems. So, we can solve the equivalent parametric mixed-integer programming problems by general-purpose optimization software. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the efficiency of the proposed solution methods.     

Indirect target detection method in FLIR image sequences

Due to the complexity of the scene, target detection in forward-looking infrared (FLIR) imagery is a challenging problem, especially for occluded target. The main contribution of this paper is to propose an indirect detection method for improving the recognition probability and effectiveness of target detection method in FLIR image sequences under complex conditions. The proposed method mainly includes four steps: preparation of forward-looking reference image of landmark, extraction of the real-time scene image, template matching and target location, in which some key technologies are proposed, such as perspective transformation used to solve projective problems, position prediction for improving real-time performance, and target location used for identifying the target’s position. Experimental results are shown to demonstrate the robustness and efficiency of proposed method in FLIR image sequences.     

A probabilistic quality of service constraint for a location model of switches in ATM communications networks

We formulate a model for the optimal location of switches in ATM communications networks. The networks are designed in such a way that, at their arrival to the switches, ATM cells find free space in a buffer of length b, with a probability α. The model avoids the impairment to the communication caused by both cell loss (because of shorter buffers) and cell-delay variations (because of longer buffers). It is also shown how to transform a non-linear, probabilistic, constraint into a linear form. Computational experience is provided for the model. This revised version was published online in June 2006 with corrections to the Cover Date.     

Electron spin resonance and optical absorption studies on copper-doped lithium hydrazinium sulphate single crystals

ESR and optical absorption studies have been carried out on Cu²⁺-doped lithium hydrazinium sulphate single crystals at 303 K. The spin-Hamiltonian parameters evaluated indicate a N₂O₂ square planar environment for Cu²⁺ ion in this lattice. The correlation ofESR and crystal structure data leads us to conclude that Cu²⁺ ion enters the lattice interstitially. Charge compensation is achieved by the release of protons. Using the optical absorption andESR data, bonding parameters and orbital reduction factors are also evaluated.     

SITE GAINS AND ITS ESTIMATION METHOD OF A LIGHTNING LOCATION SYSTEM

We discuss a new concept in lightning location system (LLS). Specitically,it is anoptimum method of lightning signal strength and a parameterization method of the site gaincurve estimation in LLS. Site gain correction curves for all DFs for 1989 were found byusing the data observed in an LLS of three direction finders (DFs) in Beijing region. Theresult shows that the curves, as a function of measured bearing, only include the one- andtwo-cycle sinusoidal components. It also indicates that the larger the site errors which DFhas, the larger the amplitude of the site gain curve. In this LLS, the ranges of site gainare 0.9--1.2, 0.85--1.05, 0.9--1.1 for DF1, DF2 and DF3 respectively. For discussion, we suggested a model for site gains due to dipole radiation. This modelcan be used to reasonably explain the characteristics of structure of site gain curves.     

Applying simulated annealing to location-planning models

Simulated annealing is a computational approach that simulates an annealing schedule used in producing glass and metals. Originally developed by Metropolis et al. in 1953, it has since been applied to a number of integer programming problems, including the p-median location-allocation problem. However, previously reported results by Golden and Skiscim in 1986 were less than encouraging. This article addresses the design of a simulated-annealing approach for the p-median and maximal covering location problems. This design has produced very good solutions in modest amounts of computer time. Comparisons with an interchange heuristic demonstrate that simulated annealing has potential as a solution technique for solving location-planning problems and further research should be encouraged.     

Multicriteria tour planning for mobile healthcare facilities in a developing country

A multiobjective combinatorial optimization (MOCO) formulation for the following location-routing problem in healthcare management is given: For a mobile healthcare facility, a closed tour with stops selected from a given set of population nodes has to be found. Tours are evaluated according to three criteria: (i) An economic efficiency criterion related to the tour length, (ii) the criterion of average distances to the nearest tour stops corresponding to p-median location problem formulations, and (iii) a coverage criterion measuring the percentage of the population unable to reach a tour stop within a predefined maximum distance. Three algorithms to compute approximations to the set of Pareto-efficient solutions of the described MOCO problem are developed. The first uses the P-ACO technique, and the second and the third use the VEGA and the MOGA variant of multiobjective genetic algorithms, respectively. Computational experiments for the Thiès region in Senegal were carried out to evaluate the three approaches on real-world problem instances.     

An exact solution procedure for a cluster hub location problem

This paper proposes a new (MIP) model formulation and a new solution procedure for the hub network design problem under a non-restrictive policy introduced by Sung and Jin [Sung, C.S., Jin, H.W., 2001. Dual-based approach for a hub network design problem under non-restrictive policy. European Journal of Operational Research 132 (1), 88–105]. The model formulation contains significantly fewer variables so that optimal solutions for the LP-relaxation of the model can be determined for large instances using standard procedures for LP-models. Furthermore, the LP-relaxation provides very tight lower bounds. Computational results are given, which demonstrate that the new model formulation allows for solving much larger instances. It turned out that the new (exact) solution procedure, which utilises the new model formulation, is faster than the heuristic proposed by Sung and Jin (2001). It is also shown that the problem is np-hard.     

Competitive facility location model with concave demand

We consider a spatial interaction model for locating a set of new facilities that compete for customer demand with each other, as well as with some pre-existing facilities to capture the “market expansion” and the “market cannibalization” effects. Customer demand is assumed to be a concave non-decreasing function of the total utility derived by each customer from the service offered by the facilities. The problem is formulated as a non-linear Knapsack problem, for which we develop a novel solution approach based on constructing an efficient piecewise linear approximation scheme for the objective function. This allows us to develop exact and α-optimal solution approaches capable of dealing with relatively large-scale instances of the model. We also develop a fast Heuristic Algorithm for which a tight worst-case error bound is established.