Research and Application of Maintenance Decision Method of Complex Equipment in Nuclear Power Plant

The determination of maintenance mode of complex equipment in nuclear power plant is an essential work for reliability analysis and maintenance decision. Currently, the main decision method of maintenance mode is reliability centered maintenance( RCM) logic decision-making process, but the process is a qualitative analysis process. Based on a comprehensive analysis of factors affecting equipment reliability and maintenance work, it adopts a fuzzy synthesis decision method to establish a maintenance decision model,which uses the maximum subordination principle and expert assessment method to determine the maintenance mode of complex equipment. Combined with a concrete example of generators in nuclear power plant,a description of maintenance decision method was proposed in the application of complex equipment. The research shows that the method is feasible and reliable.

GENERALIZED TRANSFER FUNCTION OF CONTROL SYSTEM AND AN IMPROVEMENT ON THE DECISION METHOD OF MOVEMENT STABILITY

In this paper the definitions of generalized transfer functios of control system and itscontinuity are presented.Using generalized transfer function as a tool,a set of theorems fordeciding movement stability have been constructed.Thus basing understanding of thecharacteristics of a control dynamics system on its measured procedure will simplify thedecision method of movement stability problems.

Utilizing comprehensive decision analysis methods to determine an optimal planting pattern and nitrogen application for winter oilseed rape

Oilseed rape is one of the most important oil crops globally.Attaining the appropriate cultivation method(planting pattern and nitrogen level)is necessary to achieve high yield,quality and resource utilization efficiency.However,the optimal method for oilseed rape varies across countries and regions.The objective of the present study was to determine an appropriate cultivation method,including planting pattern and nitrogen application,for winter oilseed rape in northwestern China.Two planting patterns:ridge film mulching and furrow planting(RFMF)and flat planting(FP),and six nitrogen(N)amounts:0(N0),60(N60),120(N120),180(N180),240(N240),and 300(N300)kg N ha–1 were applied across three growing seasons(2014–2017).Three comprehensive decision analysis methods:principal component analysis,grey correlation degree analysis and the combined entropy weight and dynamic technique for order preference by similarity to ideal solution method were used to evaluate the growth and physiological indicators,nutrient uptake,yield,quality,evapotranspiration,and water use efficiency of winter oilseed rape.Planting pattern,nitrogen amount and their interaction significantly affected the indicators aforementioned.The RFMF pattern significantly increased all indicators over the FP pattern.Application of N also markedly increased all the indicators except for seed oil content,but the yield,oil production and water use efficiency were decreased when N fertilizer exceeded 180 kg N ha–1 under FP and 240 kg N ha–1 under RFFM.The evaluation results of the three comprehensive decision analysis methods indicated that RFMF planting pattern with 240 kg N ha–1 is an appropriate cultivation method for winter oilseed rape in northwestern China.These findings are of vital significance to maximize yield,optimize quality and improve resource use efficiencies of winter oilseed rape.

Light-Weighted Decision Support Framework for Selecting Cloud Service Providers

Multi-criteria decision making(MCDM)is a technique used to achieve better outcomes for some complex business-related problems,whereby the selection of the best alternative can be made in as many cases as possible.This paper proposes a model,the multi-criteria decision support method,that allows both service providers and consumers to maximize their profits while preserving the best matching process for resource allocation and task scheduling.The increasing number of service providers with different service provision capabilities creates an issue for consumers seeking to select the best service provider.Each consumer seeks a service provider based on various preferences,such as price,service quality,and time to complete the tasks.In the literature,the problem is viewed from different perspectives,such as investigating how to enhance task scheduling and the resource allocation process,improve consumers’trust,and deal with network problems.This paper offers a novel model that considers the preferences of both service providers and consumers to find the best available service provider for each consumer.First,the model adopts the best-worst method(BWM)to gather and prioritize tasks based on consumers’and service providers’preferences.Then,the model calculates and matches similarities between the sets of tasks from the consumer’s side with the sets of tasks from the provider’s side to select the best service provider for each consumer using the two proposed algorithms.The complexity of the two algorithms is found to be O(n3).

Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method

One of the most critical and complicated steps in mine design is a selection of suitable mining method based upon geological,geotechnical,geographical,safety and economical parameters.The aim of this study is developing a Monte Carlo simulation to selection the optimum mining method by using effective and major criteria and at the same time,taking subjective judgments of decision makers into consideration.Proposed approach is based on the combination of Monte Carlo simulation with conventional Analytic Hierarchy Process(AHP).Monte Carlo simulation is used to determine the confdence level of each alternative’s score,is calculated by AHP,with the respect to the variance of decision makers’opinion.The proposed method is applied for Jajarm Bauxite Mine in Iran and eventually the most appropriate mining methods for this mine are ranked.

Optimum mining method selection using fuzzy analytical hierarchy process–Qapiliq salt mine,Iran

Mining method selection is the first and the most critical problem in mine design and depends on some parameters such as geotechnical and geological features and economic and geographic factors. In this paper, the factors affecting mining method selection are determined. These factors include shape, thick- ness, depth, slope, RMR and RSS of the orebody, RMR and RSS of the hanging wall and footwall. Then, the priorities of these factors are calculated. In order to calculate the priorities of factors and select the best mining method for Qapiliq salt mine, Iran, based on these priorities, fuzzy analytical hierarchy process （AHP） technique is used. For this purpose, a questionnaire was prepared and was given to the associated experts. Finally, after a comparison carried out based on the effective factors, between the four mining methods including area mining, room and pillar, cut and fill and stope and pillar methods, the stope and nillar mining method was selected as the most suitable method to this mine.

Evaluation of Lean Product Development Stages of Autonomous Vehicle Technologies with AHP Method

The potential innovation and emerging workforce created by autonomous vehicle technologies, which have just entered the lean product development disciplines, play an important role in the development or change of the automotive manufacturing industry. Therefore, the intensity of work and the innovation practices brought by the technologies in question at each step of very different and interdisciplinary studies deeply affect the new and lean product development steps. Comparatively measuring the operating weight of new autonomous vehicle technologies in different company structures in these lean product development steps has important consequences for the development and change of the automotive industry under heavy global competition. On the other hand, it is difficult to measure the innovation input or the use of new autonomous technology under the AHP mathematical model of each part that constitutes the whole of the lean product development process, but it also creates the future predictions of the sector. The Analytical Hierarchy Process (AHP), which is one of the multi-purpose decision-making methods, was used to determine the most intense value creation, the design and development phase where there is innovation input, or the lean product development discipline throughout the whole process. The AHP method was preferred for the comparative analysis and synthesis of different applications or similar approaches in the automotive manufacturing industry companies (global and local) and lean product development processes in the field study of the research, under qualitative data. Under the AHP mathematical model created in the research, it was aimed to measure interdisciplinary clusters with a focus on new technology and to identify similarities or differences under alternative applications created by different company structures and to compare them systematically and evaluate them mathematically. In the study, the AHP mathematical model was used to compare lean product development processes and the use of new autonomous vehicle technologies, and the Expert Choice program was preferred in the application of the method.

Alunite processing method selection using the AHP and TOPSIS approaches under fuzzy environment

Alunite is the most important non bauxite resource for alumina. Various methods have been proposed and patented for processing alunite, but none has been performed at industrial scale and no technical,operational and economic data is available to evaluate methods. In addition, selecting the right approach for alunite beneficiation, requires introducing a wide range of criteria and careful analysis of alternatives.In this research, after studying the existing processes, 13 methods were considered and evaluated by 14 technical, economic and environmental analyzing criteria. Due to multiplicity of processing methods and attributes, in this paper, Multi Attribute Decision Making methods were employed to examine the appropriateness of choices. The Delphi Analytical Hierarchy Process(DAHP) was used for weighting selection criteria and Fuzzy TOPSIS approach was used to determine the most profitable candidates. Among 13 studied methods, Spanish, Svoronos and Hazan methods were respectively recognized to be the best choices.

A Study on the Factors Influencing Value-Added in the Cruise Ship Value Chain Based on the DEMATEL-ISM Model

Cruise value chain is to take the exchange of cruise products and services as the core in a certain spatial scope,and enterprises with core advantages within or between different industries establish associations in accordance with certain technical and economic conditions,so as to realise the multi-dimensional extension and value appreciation of the cruise value chain in the vertical and horizontal links,and ultimately establish a chain-network type of enterprise strategic alliance.This paper tries to analyse the value-added factors of the cruise industry chain by constructing a multi-level hierarchical structural model with reference to the influencing factor analysis methods of relevant literature-DEMATEL(Decision Making Experiment and Evaluation Experiment)and ISM(Interpretative Structural Model).The study shows that the innovation and scale value-added module in the upstream of the cruise industry chain is the core module of value-added of the whole cruise industry chain,and the value-added mainly originates from the design and manufacturing of cruise ships.The middle reaches of the cruise industry chain are mainly cruise operation enterprises,and the specificity of cruise operation determines that its brand value-added is mainly accomplished through the global layout of multinational corporations,and the cruise brand is able to drive the consumption demand and has value-added ability.The downstream value-added of the cruise industry chain is mainly realised through the increase in profits of cruise tourism service products.

Fuzzy based decision support method for selection of sustainable wastewater treatment technologies

Inadequate decision support tools have led to selection of inappropriate wastewater treatment technologies.The objectives of this research were to investigate performance data for wastewater treatment technologies,develop a Decision Support Method(DSM)for evaluating performance of technologies,and to validate the developed method.The method was developed through evaluation of performance of wastewater treatment technologies against environmental and economic indicators.Fuzzy logic techniques in form of linguistic variables were applied in order to support decision making under uncertainty.The DSM relied on performance evaluation in order to rate effectiveness of wastewater treatment technologies.DSM was validated through a training tool in ED-WAVE,a model developed by a consortium of European and Asian countries.The reliance of the DSM on performance evaluation was an improvement on the existing decision support tools such as ED-WAVE that relied on retrieval of past performance data.As DSM integrated environmental and economic factors in evaluating wastewater treatment technologies,it was thus able to select a process that was not only environmentally sustainable but also economically affordable.

Genecological zones and selection criteria for natural forest populations for conservation:the case of Boswellia papyrifera in Ethiopia

Rapid changes in land-use in the Combretum–Terminalia woodlands of northwestern Ethiopia are mainly due to the increases in commercial farming and immigration.We used integrated ecological and social data collection techniques,including subdivision of the vegetation zone,vegetation survey,focus group discussions and key informant interviews,to identify genecological zones and set criteria for selection of viable populations of Boswellia papyrifera(Del.)Hochst in Ethiopia for conservation.Interviews of senior experts were supported with a rating method and involved 43 respondents and focused on identifying and weighting criteria and indicators of selection in a participatory way to prioritize populations for conservation.Using mean annual rainfall data,we reclassified the Combretum–Terminalia woodland vegetation region into three moisture zones(wet,moist and dry),and designated them as genecological zones for B.papyrifera conservation.A total of 35 woody species were identified at Lemlem Terara site in Metema district,and the Shannon diversity index and evenness were 2.01 and of 0.62,respectively.There were 405 adult trees,and 10 saplings and3314 seedlings per ha.The trees were medium-sized with overall mean diameter at breast height(dbh) of 16.9(±9.5)cm.Seedling recruitment was poor due to grazing,crop production and fire incidences.Through a multi-criteria decision analysis,five criteria and 20 quantitative indicators were identified and weighted to prioritize populations for conservation.These criteria in their descending order of importance are(1) forest ecosystem health and vitality,(2)forest cover and population structure of B.papyrifera,(3)productive function of the forest,(4) biological diversity in the forest,and(5) socioeconomic benefits of the forest to communities.Multivariate tests in the general linear model revealed significant differences among researchers and nonresearchers in rating the criteria and indicators,but not among foresters and nonforesters.Hence,participatory multi-criteria decision analysis should involve people from various institutions to rectify decisions on conservation of the species.Careful evaluation of the investment policy environment and engaging those government bodies that are responsible to allocate the dry forests for commercial farming is recommended before the proposed criteria are applied to select populations for conservation,thus ensuring subsequent use of the outcomes of such exercises and better reconciling conservation and agricultural production increment goals.

New Scenarios for a Shift towards Agroecology in Viticulture

In the light of its negative impacts on the environment and human health, conventional agriculture is currently facing new challenges;for example, reducing pesticide reliance, improving biodiversity, adapting to climate change and reconciling winegrowers with consumers, which require changes to be made to vineyard management. A shift towards more sustainable agriculture via the development of agroecological systems may be key to meeting these environmental, economic and social challenges. This study aimed to evaluate the performance of existing viticultural systems, as well as that of three new scenarios that we built to change conventional vine production systems and their related practices. The end aim is to adopt the principles of agroecology, and more virtuously, to ensure that vine production remains in line with societal expectations. First, thirty-eight different viticultural systems were chosen. Three realistic scenarios for changing these production systems were then built by working with stakeholders and incorporating the best practices that had been identified in the vineyard. Conventional practices were optimised in the first scenario and an agroecological approach was adopted for the other two scenarios: an Agroecological scenario (using synthetic chemicals) and an Agroecological-Bio scenario (organic system). All three scenarios were based on a combination of good practices which contribute to enhancing vineyard biodiversity, and which thus restore biological regulation and in turn reduce pesticides. The viticultural systems performances have been evaluated with a methodology involving multicriteria decision aid using ELECTRE Tri-C and ELECTRE III methods. Seven evaluation criteria were selected which covered socio-economic performance (economic profitability, workload and system complexity) and environmental performance (pesticide pressure, pesticide ecotoxicity, agroecological practices and pesticide drift). The best performances were achieved by the two agroecological scenarios, and this methodology can be adaptable to different production systems everywhere in different viticultural regions.

Edge Device Fault Probability Based Intelligent Calculations for Fault Probability of Smart Systems

In a smart system, the faults of edge devices directly impact the system’s overall fault. Further, complexity arises when different edge devices provide varying fault data. To study the Smart System Fault Evolution Process (SSFEP) under different fault data conditions, an intelligent method for determining the Smart System Fault Probability (SSFP) is proposed. The data types provided by edge devices include the following: (1) only known edge device fault probability;(2) known Edge Device Fault Probability Distribution (EDFPD);(3) known edge device fault number and EDFPD;(4) known factor state of the edge device fault and EDFPD. Moreover, decision methods are proposed for each data case. Transfer Probability (TP) is divided into Continuity Transfer Probability (CTP) and Filterability Transfer Probability (FTP). CTP asserts that a Cause Event (CE) must lead to a Result Event (RE), while FTP requires CF probability to exceed a threshold before RF occurs. These probabilities are used to calculate SSFP. This paper introduces a decision method using the information diffusion principle for low-data SSFP determination, along with an improved method. The method is based on space fault network theory, abstracting SSFEP into a System Fault Evolution Process (SFEP) for research purposes.

A Decision Support Model and Procedure for Selecting Interrelated R&D Projects

A decision support model with stochastic multiobjective functions and constraints and its solution procedure are presented for selecting R&D projects, in which the uncertainty of project evaluation and selection, the interactions of technique, resource and benefit among projects, and the experience, knowledge and preference of R&D managers are considered. The statistical results of stochastic factors representing the benefit contributions and the three kinds of interactions corresponding to each objective among projects are obtained by the Delphi method and QS/NI decision process for developing the model. In the decision analysis solution procedure, experiences and preferences of R&D managers to make decisions are considered by means of aspiration goal levels(AGL), probabilities to reach AGL called satisfied degrees, and probabilities to satisfy the corresponding constraints called feasible degrees, under which a project selection plan can be proposed by the decision analyst or computer. The utility of this model and solution procedure are demonstrated by a real world case example in R&D program of strength and vibration of aircraft turbine engines. which involves 23 project candidates. The evalutions of benefit contributions and interactions of projects are made by 41 experts in the field.

Fuzzy-Based Dynamic Distributed Queue Scheduling for Packet Switched Networks

Addressing the problem of queue scheduling for the packet-switched system is a vital aspect of congestion control. In this paper, the fuzzy logic based decision method is adopted for queue scheduling in order to enforce some level of control for traffic of different quality of service requirements using predetermined values. The fuzzy scheduler proposed in this paper takes into account the dynamic nature of the Internet traffic with respect to its time-varying packet arrival process that affects the network states and performance. Three queues are defined, viz low, medium and high priority queues. The choice of prioritizing packets influences how queues are served. The fuzzy scheduler not only utilizes queue priority in the queue scheduling scheme, but also considers packet drop susceptibility and queue limit. Through simulation it is shown that the fuzzy scheduler is more appropriate for the dynamic nature of Internet traffic in a packet-switched system as compared with some existing queue scheduling methods. Results show that the scheduling strategy of the proposed fuzzy scheduler reduces packet drop, provides good link utilization and minimizes queue delay as compared with the priority queuing （PQ）, first-in-first-out （FIFO）, and weighted fair queuing （WFQ）.

Shapley value cooperative game theory-based locational marginal price computation for loss and emission reduction

An iterative method based on Shapley Value Cooperative Game Theory is proposed for the calculation of local marginal price (LMP) for each Distributed Generator (DG) bus on a network. The LMP value is determined for each DG on the basis of its contribution to reduce loss and emission reduction, which is assessed using the Shapley Value approach. The proposed approach enables the Distribution Company (DISCO) decision-maker to operate the network optimally in terms of loss and emission. The proposed method is implemented in the Taiwan Power Company distribution network 7 warnings consisting of 84 buses and 11 feeders in the MATLAB environment. The results show that the proposed approach allows DISCO to operate the network on the basis of its priority between the reduction of active power loss and emission in the network.