A heuristics approach for computing the largest eigenvalue of a pairwise comparison matrix

Pairwise comparison matrices (PCMs) are widely used to capture subjective human judgements, especially in the context of the Analytic Hierarchy Process (AHP). Consistency of judgements is normally computed in AHP context in the form of consistency ratio (CR), which requires estimation of the largest eigenvalue (Lmax) of PCMs. Since many of these alternative methods do not require calculation of eigenvector, Lmax and hence the CR of a PCM cannot be easily estimated. We propose in this paper a simple heuristics for calculating Lmax without any need to use Eigenvector Method (EM). We illustrated the proposed procedure with larger size matrices. Simulation is used to compare the accuracy of the proposed heuristics procedure with actual Lmax for PCMs of various sizes. It has been found that the proposed heuristics is highly accurate, with errors less than 1%. The proposed procedure would avoid biases and help managers to make better decisions. The advantage of the proposed heuristics is that it can be easily calculated with simple calculations without any need for specialised mathematical procedures or software and is independent of the method used to derive priorities from PCMs

Identification of Public Objectives Related to Agricultural Sector Support

The Common Agricultural Policy (CAP) is a widely debated policy in terms of both its budget and its instruments. In order to serve the citizens of Europe properly, CAP requires optimal identification of the public objectives desired. This paper aims to analyse the relative weights that citizens assign to the various potential objectives of the CAP and to show how these can be used to improve the selection of policy instruments. As a means of identifying social preferences we used the Analytical Hierarchy Process (AHP) technique on a population sample in Castilla y León (Spain). Results show how the current policy decision process lacks mechanisms capable of identifying social preferences and thus leading to the choice of sub-optimal policies.Common Agricultural Policy, Objectives, Social preferences, AHP, Castilla y León.

Monitoring international migration flows in Europe. Towards a statistical data base combining data from different sources

The paper reviews techniques developed in demography, geography and statistics that are useful for bridging the gap between available data on international migration flows and the information required for policy making and research. The basic idea of the paper is as follows: to establish a coherent and consistent data base that contains sufficiently detailed, up-to-date and accurate information, data from several sources should be combined. That raises issues of definition and measurement, and of how to combine data from different origins properly. The issues may be tackled more easily if the statistics that are being compiled are viewed as different outcomes or manifestations of underlying stochastic processes governing migration. The link between the processes and their outcomes is described by models, the parameters of which must be estimated from the available data. That may be done within the context of socio-demographic accounting. The paper discusses the experience of the U.S. Bureau of the Census in combining migration data from several sources. It also summarizes the many efforts in Europe to establish a coherent and consistent data base on international migration. The paper was written at IIASA. It is part of the Migration Estimation Study, which is a collaborative IIASA-University of Groningen project, funded by the Netherlands Organization for Scientific Research (NWO). The project aims at developing techniques to obtain improved estimates of international migration flows by country of origin and country of destination

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Postprint (published version

SIMULATION-BASED DECISION MODEL TO CONTROL DYNAMIC MANUFACTURING REQUIREMENTS: APPLICATION OF GREY FORECASTING - DQFD

Manufacturing systems have to adapt to changing requirements of their internal and external customers. In fact, new requirements may appear unexpectedly and may change multiple times. Change is a straightforward reality of production, and the engineer has to deal with the dynamic work environment. In this perspective, this paper proposes a decision model in order to fit actual and future processes’ needs. The proposed model is based on the dynamic quality function deployment (DQFD), grey forecasting model GM (1,1) and the technique for order preference by similarity to ideal solution (TOPSIS). The cascading QFD-based model is used to show the applicability of the proposed methodology. The simulation results illustrate the effect of the manufacturing needs changes on the strategic, operational and technical improvements

Improved Methods for Network Screening and Countermeasure Selection for Highway Improvements

Network screening and countermeasure selection are two crucial steps in the highway improvement process. In network screening, potential improvement locations are ranked and prioritized based on a specific method with a set of criteria. The most common practice by transportation agencies has been to use a simple scoring method, which, in general, weighs and scores each criterion and then ranks the locations based on their relative overall scoring. The method does not deal well with criteria that are qualitative in nature, nor does it account for the impacts of correlation among the criteria. The introduction of Analytic Hierarchy Process (AHP) provides agencies with a method to include both quantitative and qualitative criteria. However, it does not address the issue on correlation. This dissertation explores the use of both Analytic Network Process (ANP) and Fuzzy Analytic Network Process (FANP) for their potential capabilities to address both issues. Using urban four-lane divided highways in Florida for bicycle safety improvements, both ANP and FANP were shown to provide more reasonable rankings than AHP, with FANP providing the best results among the methods. After the locations are ranked and prioritized for improvements, the next step is to evaluate the potential countermeasures for improvements at the selected top-ranked locations. In this step, the standard practice has been to use Crash Modification Factors (CMFs) to quantify the potential impacts from implementing specific countermeasures. In this research, CMFs for bicycle crashes on urban facilities in Florida were developed using the Generalized Linear Model approach with a Zero-Inflated Negative Binomial (ZINB) distribution. The CMFs were tested for their spatial and temporal transferability and the results show only limited transferability both spatially and temporally. The CMFs show that, in general, wider lanes, lower speed limits, and presence of vegetation in the median reduce bicycle crashes, while presence of sidewalk and sidewalk barrier increase bicycle crashes. The research further considered bicycle exposure using the bicycle activity data from the Strava smartphone application. It was found that increased bicycle activity reduces bicycle crash probabilities on segments but increases bicycle crash probabilities at signalized intersections. Also, presence of bus stops and use of permissive signal phasing at intersections were found to increase bicycle crash probabilities

Using webcrawling of publicly available websites to assess E-commerce relationships

We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation

Evaluation of Rope Shovel Operators in Surface Coal Mining Using a Multi-Attribute Decision-Making Model

Rope shovels are used to dig and load material in surface mines. One of the main factors that influence the productivity and energy consumption of rope shovels is the performance of the operator. Existing methods of evaluating operator performance do not consider the relationship between production rate and energy consumption. This thesis presents a method for evaluating rope shovel operators using the Multi-Attribute Decision-Making (MADM) model. Data used in this research were collected from an operating surface coal mine in the southern United States. The MADM model used in this research consists of attributes, their weights of importance, and alternatives. Shovel operators are considered the alternatives in the MADM model. The energy consumption model was developed with multiple regression analysis, and its variables are included in the MADM model as attributes. Formulation of the production rate model is already known, and thus determining the attributes that have a significant influence is straightforward. Preferences with respect to min/max of the defined attributes were obtained with multi-objective optimization. Multi-objective optimization was performed with the overall goal of minimizing energy consumption and maximizing production rate. Weights of importance of the attributes were determined by using the Analytical Hierarchy Process (AHP). The overall evaluation of operators was performed by using one of the MADM models, PROMETHEE II. The research presented here may be used by mining professionals to help evaluate the performance of rope shovel operators in surface mining

Competency Model: A Study on the Cultivation of College Students’ Innovation and Entrepreneurship Ability

Objectives: This study was designed to analyze entrepreneurial competency and enhance college students' abilities in innovation and entrepreneurship. Methods: Ten relevant factors were summarized based on the interview records. Relevant data were collected through questionnaires and tested for reliability and validity. The effectiveness of the ten factors on entrepreneurial competency was tested using the regression analysis method. Then, an analytic hierarchy process model of entrepreneurial competency was established to calculate the relevant weights. Findings: The data collected from the survey questionnaire had sufficient reliability and validity. The ten relevant factors were effective in developing entrepreneurial competence. The weight distribution in the analytic hierarchy model indicated that entrepreneurial knowledge was most important, followed by entrepreneurial ability, and intrinsic potential was least significant. Novelty:The novelty of this article lies in not only verifying the effectiveness of relevant factors through regression analysis but also further analyzing the weight of these factors through an analytic hierarchy process. Doi: 10.28991/HIJ-2023-04-04-011 Full Text: PD

Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning

With the progressive development of infrastructure construction, conventional analytical methods such as correlation index, quantifying factors, and peer review are no longer satisfactory in support for decision-making of implementing an infrastructure project in the age of big data. This study proposes using a mathematical model named Fuzzy-Neural Comprehensive Evaluation Model (FNCEM) to improve the reliability of the feasibility study of infrastructure projects by using data mining and machine learning. Specifically, the data collection on time-series data, including traffic videos (278 Gigabytes) and historical weather data, uses transportation cameras and online searching, respectively. Meanwhile, the researcher sent out a questionnaire for the collection of the public opinions upon the influencing factors that an infrastructure project may have. Then, this model implements the backpropagation Artificial Neural Network (BP-ANN) algorithm to simulate traffic flows and generate outputs as partial quantitative references for evaluation. The traffic simulation outputs used as partial inputs to the Analytic Hierarchy Process (AHP) based Fuzzy logic module of the system for the determination of the minimum traffic flows that a construction scheme in corresponding feasibility study should meet. This study bases on a real scenario of constructing a railway-crossing facility in a college town. The research results indicated that BP-ANN was well applied to simulate 15-minute small-scale pedestrian and vehicle flow with minimum overall logarithmic mean squared errors (Log-MSE) of 3.80 and 5.09, respectively. Also, AHP-based Fuzzy evaluation significantly decreased the evaluation subjectivity of selecting construction schemes by 62.5%. It concluded that the FNCEM model has strong potentials of enriching the methodology of conducting a feasibility study of the infrastructure project