COMPARISON OF CLASSICAL ANALYTIC HIERARCHY PROCESS (AHP) APPROACH AND FUZZY AHP APPROACH IN MULTIPLE-CRITERIA DECISION MAKING FOR COMMERCIAL VEHICLE INFORMATION SYSTEMS AND NETWORKS (CVISN) PROJECT

Radio Frequency Identification (RFID) has emerged as an important technology with many possible applications in a wide variety of fields. It is said that RFID can perform well in transportation system. Nebraska Department of Motor Vehicles (NEDMV) is using this technique to perform an analysis on utilizing RFID license plates to assist with Commercial Vehicle Information Systems and Networks (CVISN) program with the cooperation of many other stakeholders. Previous House of Quality (HOQ) analysis evaluates stakeholders’ needs and provides the pairwise comparison values of six important technical requirements for each stakeholder. Based on these, this research aims to seek for the comprehensive ranking of the six technical requirements. The weights of different technical requirements vary a lot according to different stakeholders. As a result, assumptions are made to make it possible that fuzzy analytic hierarchy process (AHP) approach could be used to give weight rankings of this multiple-criteria decision making problem. Problem comes out naturally that whether or not fuzzy AHP is appropriate to solve this problem. To verify the feasibility of application of fuzzy AHP to CVISN project problem, benchmarking comparison of classical AHP and fuzzy AHP approaches is performed. The comparison bases on a series of statistical models with 240 randomly generated statistical data. Results of comparison indicate that the pairwise weight values of AHP approach positively affect the difference between the two approaches, and fuzzy AHP could narrow the differences of weights among different criteria. Benchmarking models provide basic parameters, based on which prediction intervals are built to verify the outcomes of CVISN project given by fuzzy AHP. Results show that fuzzy AHP is an appropriate approach for CVISN project. Finally a comprehensive weight vector of six technical requirements is provided by fuzzy AHP, catering to the requirements of further research on choosing a best RFID system

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

The sustainable management of land and fisheries resources using multicriteria techniques: A meta-analysis

In recent years modern societies have attached a multifunctional requirement to the use of renewable resources, making their optimal sustainable management more complex. In the last decades, in many cases, this complexity is addressed by formulating management models with the help of the concepts and methods belonging to the well-known multicriteria decision-making (MCDM) paradigm. The purpose of this paper was to undertake a hermeneutic meta-analysis of the literature provided in primary journals on issues related to the management of these resources with the help of the MCDM paradigm. In this way, the paper aimed to obtain new, basic insights with considerations that might improve the efficiency of future research in the field studied. The meta-analysis was implemented by formulating and testing a battery of hypotheses of how the MCDM methods have been used in the past for the formulation of management models for the type of resource analyzed.The work of Carlos Romero, Carlos Iglesias-Merchan, and Luis Diaz-Balteiro was funded by the Ministry of Economy and Competitiveness of Spain under project AGL2015-68657-R. Additionally, this research was partially financed by the European Union’s H2020 Research and Innovation Programme under the Marie Sklodowska-Curie Actions, grant agreement No. 691149–SuFoRun

Practical Methods for Optimizing Equipment Maintenance Strategies Using an Analytic Hierarchy Process and Prognostic Algorithms

Many large organizations report limited success using Condition Based Maintenance (CbM). This work explains some of the causes for limited success, and recommends practical methods that enable the benefits of CbM. The backbone of CbM is a Prognostics and Health Management (PHM) system. Use of PHM alone does not ensure success; it needs to be integrated into enterprise level processes and culture, and aligned with customer expectations. To integrate PHM, this work recommends a novel life cycle framework, expanding the concept of maintenance into several levels beginning with an overarching maintenance strategy and subordinate policies, tactics, and PHM analytical methods. During the design and in-service phases of the equipment’s life, an organization must prove that a maintenance policy satisfies specific safety and technical requirements, business practices, and is supported by the logistic and resourcing plan to satisfy end-user needs and expectations. These factors often compete with each other because they are designed and considered separately, and serve disparate customers. This work recommends using the Analytic Hierarchy Process (AHP) as a practical method for consolidating input from stakeholders and quantifying the most preferred maintenance policy. AHP forces simultaneous consideration of all factors, resolving conflicts in the trade-space of the decision process. When used within the recommended life cycle framework, it is a vehicle for justifying the decision to transition from generalized high-level concepts down to specific lower-level actions. This work demonstrates AHP using degradation data, prognostic algorithms, cost data, and stakeholder input to select the most preferred maintenance policy for a paint coating system. It concludes the following for this particular system: A proactive maintenance policy is most preferred, and a predictive (CbM) policy is more preferred than predeterminative (time-directed) and corrective policies. A General Path prognostic Model with Bayesian updating (GPM) provides the most accurate prediction of the Remaining Useful Life (RUL). Long periods between inspections and use of categorical variables in inspection reports severely limit the accuracy in predicting the RUL. In summary, this work recommends using the proposed life cycle model, AHP, PHM, a GPM model, and embedded sensors to improve the success of a CbM policy

Dynamics under Uncertainty: Modeling Simulation and Complexity

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc

DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems

Context-Related Scaling of Human Judgement in the Multiplicative AHP, SMART, and ELECTRE

Since decisions are invariably made within a given context, we model relative preferences as ratios of increments or decrements in an interval on the axis of desirability. Next, we sort the ratio magnitudes into a small number of categories, represented by numerical values on a geometric scale. We explain why the Analytic Hierarchy Process (AHP) and the French collection of ELECTRE systems, typically based on pairwise -- comparison methods, are concerned with category judgement of ratio magnitudes, whereas the Simple Multi-Attribute Rating Technique (SMART) essentially uses the orders of magnitude of these ratios. This phenomenon, well-known in psycho-physics, provides a common basis for the analysis of the methods in question and for a cross-validation of their results. Throughout the paper, we illustrate the approach via a well-known case study, the choice of a location for a nuclear power plant

Unified Bayesian Frameworks for Multi-criteria Decision-making Problems

This paper presents Bayesian frameworks for different tasks within multi-criteria decision-making (MCDM) based on a probabilistic interpretation of the MCDM methods and problems. Owing to the flexibility of Bayesian models, the proposed frameworks can address several long-standing and fundamental challenges in MCDM, including group decision-making problems and criteria correlation, in a statistically elegant manner. Also, the models can accommodate different forms of uncertainty in the preferences of the decision makers (DMs), such as normal and triangular distributions as well as interval preferences. Further, a probabilistic mixture model is developed that can group the DMs into several exhaustive classes. A probabilistic ranking scheme is also designed for both criteria and alternatives, where it identifies the extent to which one criterion/alternative is more important than another based on the DM(s) preferences. The experiments validate the outcome of the proposed frameworks on several numerical examples and highlight its salient features compared to other methods