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The analytic hierarchy process (AHP) is one of the most popular multi-criteria decision-making (MCDM) methods, and so is its extension fuzzy analytic hierarchy process (FAHP). However, the FAHP, unlike the AHP, easily handles the trusted weights by the consistency index (CI) or consistency ratio (CR). We need to first derive the consistent fuzzy pairwise comparison matrix (FPCM) by the transitivity axiom and then drive fuzzy weights. We also need a flexible mechanism for users to control the spread of fuzzy weights under tolerable consistency. In this paper, we propose a novel model based on mathematical programming to derive rational fuzzy weights of the FAHP and provide a parameter for decision-makers to control the spread of fuzzy weights. Three examples are used to demonstrate the proposed method and compared with others to validate and justify the proposed method. [ABSTRACT FROM AUTHOR]

Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a generalized fusion model and parallel computing techniques. The developed modules, including eight typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. The experiments demonstrate that if parallel strategies are adopted, in the best cases the fastest times required to finish the entire fusion operation (including disk input/output (I/O) and computation) are close to the time required to directly read and write the images without any computation. The parallel processing implemented on a workstation with two CPUs is able to perform these operations up to 13.9 times faster than serial execution. An algorithm in the framework is 32.6 times faster than the corresponding version in the ERDAS IMAGINE software. Additionally, no obvious differences in the fusion effects are observed between the fusion results of different implemented versions. [ABSTRACT FROM AUTHOR]

In this paper we present a stochastic route choice model for transit networks that explicitly addresses route correlation due to overlapping alternatives. The model is based on a multi-objective mathematical programming problem, the optimality conditions of which generate an extension to the Multinomial Logit models. The proposed model considers a fixed point problem for treating correlations between routes, which can be solved iteratively. We estimated the new model on the Santiago (Chile) Metro network and compared the results with other route choice models that can be found in the literature. The new model has better explanatory and predictive power that many other alternative models, correctly capturing the correlation factor. Our methodology can be extended to private transport networks. [ABSTRACT FROM AUTHOR]

Software trustworthiness is an important research field in software engineering. In order to appropriately evaluate it, some different measurement approaches have been proposed, which have important guiding significance for improving software trustworthiness. Recently, we have investigated attributes-based approaches. That is, how to maximize trustworthy degree of some software satisfying a given threshold by adjusting every attribute value such that the cost is minimal, i.e., the sum of all attribute values is as small as possible. The work is helpful to improve the software quality under the same cost. This paper continues this work and considers a reallocation approach to dealing with the problem that the threshold and the minimal constraints of every attribute values dynamically increase. In this process, the costs of trustworthiness improvement should be ensured to be minimal. For this purpose, we firstly define a reallocation model by mathematical programming. Then we introduce the notion of growth function. Based on this, a polynomial reallocation algorithm is designed to solve the above reallocation model. Finally, we verify our work on spacecraft softwares and the results show that this work is valid. [ABSTRACT FROM AUTHOR]

In this paper, we first use the information we have on the patients of an oncology day hospital to distribute the treatment schedules they have in each of the visits to this centre. To do this, we propose a deterministic mathematical programming model in such a way that we minimise the duration of the waiting room stays of the total set of patients and taking into account the restrictions of the circuit. Secondly, we will look for a solution to the same problem under a stochastic approach. This model will explicitly consider the existing uncertainty in terms of the different times involved in the circuit, and this model also allows the reorganisation of the schedules of medical appointments with oncologists. The models are complemented by a tool that solves the problem of assigning nurses to patients. The work is motivated by the particular characteristics of a real hospital and the models are used and compared with data from this case. [ABSTRACT FROM AUTHOR]

High penetration of small-scale distributed energy sources into the distribution network increase negative impacts related to power quality causing adverse conditions. This paper presents a mathematical model that maximizes distribution network hosting capacity through optimal distributed generation capacity allocation and control and grid reconfiguration. In addition to this, the model includes on-load tap changer control for stabilization of grid voltage conditions primarily in grid operating conditions related to voltage rise problems, which can limit grid hosting capacity. Moreover, the objective function allows the possibility of energy transfer between distribution and transmission grids. The proposed model considers alternative grid connection points for distributed generation and determines optimal connection points as well as install capacity while considering network operating limits. The model is cast as a multiperiod second-order cone linear program and involves aspects of active power management. The model is tested on a modified IEEE 33 bus test network. [ABSTRACT FROM AUTHOR]

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models. [ABSTRACT FROM AUTHOR]