Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP

Failure mode and effects analysis (FMEA) is a widely used engineering technique for designing, identifying and eliminating known and/or potential failures, problems, errors and so on from system, design, process, and/or service before they reach the customer (Stamatis, 1995). In a typical FMEA, for each failure modes, three risk factors; severity (S), occurrence (O), and detectability (D) are evaluated and a risk priority number (RPN) is obtained by multiplying these factors. There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. In this study a fuzzy approach, allowing experts to use linguistic variables for determining S, O, and D, is considered for FMEA by applying fuzzy ‘technique for order preference by similarity to ideal solution’ (TOPSIS) integrated with fuzzy ‘analytical hierarchy process’ (AHP). The hypothetical case study demonstrated the applicability of the model in FMEA under fuzzy environment.     

The impact of inadequate and dysfunctional training on Agile transformation process: A Grounded Theory study

ContextTraining is an essential facilitator in moving from traditional to Agile software development.ObjectiveThis paper addresses the importance of adequate and functional training in Agile transformation process, the causes of inadequate and dysfunctional training, and the heuristic strategies that can be used in software companies for dealing with this phenomenon.MethodA Grounded Theory study was conducted with participation of 35 Agile experts from 13 different countries.ResultsThis research discovered that inadequate and dysfunctional training was one of the critical issues that affected Agile transformation process. This study shows that comprehensive and functional training is not often provided to support Agile transformation. This paper shows the primary causes of inadequate and dysfunctional training, its adverse consequences on the transformation process, and the heuristic and ad-hoc treatments as the strategies used by Agile teams to cope with this challenge.ConclusionComprehensive training is important in Agile transformation process. Inadequate and dysfunctional training causes several challenges and problems for software companies and development teams when moving to Agile. Several ad-hoc strategies identified by this study can be employed to help software teams and companies facing similar problems.     

Prototypes construction from partial rankings to characterize the attractiveness of companies in Belgium

What are the most relevant factors to be considered by employees when searching for an employer? The answer to this question poses valuable knowledge from the Business Intelligence viewpoint since it allows companies to retain personnel and attract competent employees. It leads to an increase in sales of their products or services, therefore remaining competitive across similar companies in the market. In this paper we assess the attractiveness of companies in Belgium by using a new two-stage methodology based on Artificial Intelligence techniques. The proposed method allows constructing high-quality prototypes from partial rankings indicating experts’ preferences. Being more explicit, in the first step we propose a fuzzy clustering algorithm for partial rankings called fuzzy c-aggregation. This algorithm is based on the well-known fuzzy c-means procedure and uses the Hausdorff distance as dissimilarity functional and a counting strategy for updating the center of each cluster. However, we cannot ensure the optimality of such prototypes, and therefore more accurate prototypes must be derived. That is why the second step is focused on solving the extended Kemeny ranking problem for each discovered cluster taking into account the estimated membership matrix. To accomplish that, we adopt an optimization method based on Swarm Intelligence that exploits a colony of artificial ants. Several simulations show the effectiveness of the proposal for the real-world problem under investigation.     

Spatial Plateau Algebra for implementing fuzzy spatial objects in databases and GIS: Spatial plateau data types and operations

Many geographical applications have to deal with spatial objects that reveal an intrinsically vague or fuzzy nature. A spatial object is fuzzy if locations exist that cannot be assigned completely to the object or to its complement. Spatial database systems and Geographical Information Systems (GIS) are currently unable to cope with this kind of data. Based on an available abstract data model of fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions that leverages fuzzy set theory and fuzzy point set topology, this article proposes a Spatial Plateau Algebra that provides spatial plateau data types as an implementation of fuzzy spatial data types. Each spatial plateau object consists of a finite number of crisp counterparts that are all adjacent or disjoint to each other, are associated with different membership values, and hence form different plateaus. The formal framework and the implementation are based on well known, exact models and implementations of crisp spatial data types. Spatial plateau operations as geometric operations on spatial plateau objects are expressed as a combination of geometric operations on the underlying crisp spatial objects. This article offers a conceptually clean foundation for implementing a database extension for fuzzy spatial objects and their operations, and demonstrates the embedding of these new data types as attribute data types in a database schema as well as the incorporation of fuzzy spatial operations into a database query language.     

Computing with words and its relationships with fuzzistics

Words mean different things to different people, and so are uncertain. We, therefore, need a fuzzy set model for a word that has the potential to capture their uncertainties. In this paper I propose that an interval type-2 fuzzy set (IT2 FS) be used as a FS model of a word, because it is characterized by its footprint of uncertainty (FOU), and therefore has the potential to capture word uncertainties. Two approaches are presented for collecting data about a word from a group of subjects and then mapping that data into a FOU for that word. The person MF approach, in which each person provides their FOU for a word, is limited to fuzzy set experts because it requires the subject to be knowledgeable about fuzzy sets. The interval end-points approach, in which each person provides the end-points for an interval that they associate with a word on a prescribed scale is not limited to fuzzy set experts. Both approaches map data collected from subjects into a parsimonious parametric model of a FOU, and illustrate the combining of fuzzy sets and statistics—type-2 fuzzistics.     

Types of arcs in a fuzzy graph

The concept of connectivity plays an important role in both theory and applications of fuzzy graphs. Depending on the strength of an arc, this paper classifies arcs of a fuzzy graph into three types namely α-strong, β-strong and δ-arcs. The advantage of this type of classification is that it helps in understanding the basic structure of a fuzzy graph completely. We analyze the relation between strong paths and strongest paths in a fuzzy graph and obtain characterizations for fuzzy bridges, fuzzy trees and fuzzy cycles using the concept of α-strong, β-strong and δ-arcs. An arc of a fuzzy tree is α-strong if and only if it is an arc of its unique maximum spanning tree. Also we identify different types of arcs in complete fuzzy graphs.     

A new approach to fuzzy group decision making with trapezoidal fuzzy preference relations by using compatibility measure

This paper aims to develop a new approach to deal with fuzzy group decision making (GDM) with additive trapezoidal fuzzy preference relations (ATFPRs) by using compatibility measure. We firstly present some concepts of compatibility index and expected preference relation (PR) for ATFPR and then propose a compatibility improving algorithm to help each individual PR achieve acceptable compatibility . Moreover, a least deviation model is provided to obtain the priority vector. Besides, based on the criterion of minimizing the compatibility index, we put forward an optimal model to determine the weights of experts in GDM. Finally, the GDM process with compatibility of ATFPRs is presented, and an illustrative example is utilized to verify the developed approach . The main features of our approach are that: (1) It guarantees that each individual ATFPR is acceptably compatible by using compatibility improving algorithm. (2) It ensures that experts’ weights in group aggregation are determined objectively by optimal model.

     

Fuzzy multiple goal programming applied to TFT-LCD supplier selection by downstream manufacturers

In today’s highly competitive environment, a good supply chain relationship is essential for a company to survive and to acquire reasonable profit. While a few large companies may be able to vertically integrate from the design stage to the final distribution of the entire supply chain, most companies can only focus on their specialized functions and to cooperate with upstream or downstream companies. Supplier selection, as a result, is very important for maintaining strategic alliances. The objective of this paper is to develop a fuzzy multiple goal programming (FMGP) model to help downstream companies to select thin film transistor liquid crystal display (TFT-LCD) suppliers for cooperation. Fuzzy analytic hierarchy process (FAHP) is applied first to analyze the importance of multiple factors by incorporating experts’ opinion, and these factors include cost, yield and number of suppliers. Multi-choice goal programming is used next to consider the limits of various resources and to formulate the constraints. From the experimental design and examination, we can testify that the proposed model not only can consider multi-choice goals, decision-making behavior and limit of resources, it can also allocate the purchase among the selected supplier(s).     

An effective fixed priority co-scheduling algorithm for periodic update and application transactions

An important function of many cyber-physical systems (CPS) is to provide a close monitoring of the operation environment to be able to adapt to changing situations effectively. One of the commonly applied techniques for that is to invoke time-constrained periodic application transactions to check the status of the operation environment. The status of the environment is represented by the values of the physical entities in the operation environment which are maintained as real-time data objects in a real-time database. Unfortunately, meeting the deadlines of application transactions and maintaining the quality of real-time data objects are conflicting with each other, because they compete for the same computation resources. To address this problem of update and application transactions co-scheduling problem, in this paper, we propose a fixed priority co-scheduling algorithm called periodic co-scheduling (PCS). PCS uses periodic update transactions to maintain the temporal validity of real-time data objects. It judiciously decides the priority orders among all the update and application transactions so that the constructed schedule can satisfy the deadline constraints of all the application transactions and at the same time maximize the qualities of the real-time data objects to ensure the correct execution of application transactions. The effectiveness of the algorithm is validated through extensive simulation experiments.     

Competitive substitution and technological diffusion for semiconductor foundry firms

To survive in an intensively competitive environment, semiconductor companies need to be more agile, responsive, and flexible than before. Generally, semiconductor industry consists of three business models: integrated design manufacturer, fabless chip design, and foundry business. In general, semiconductor firms are affected by three drivers: new entrants and rivals (competition), main customers (demand), and process technologies (R&D). Inspired by Michael Porter’s five-force analysis, a novel framework is presented to accomplish the following goals: (1) The interactions between the top three foundry firms, TSMC, Samsung, and Global Foundries, are analyzed to reveal managerial insights, (2) The main customers of TSMC including Apple, Huawei, Qualcomm, Mediatek, AMD, and NVidia are incorporated to into sales forecasting, and (3) Technological diffusion across the mature process (above 70 nm), the medium process (between 20 nm and 70 nm), and the advanced process (below 20 nm) is captured to predict sales revenues. Key findings are shown as follows: (1) a large-scale foundry frim benefits from the existence of a small-scale firm, (2) the inclusion of main customers significantly improves the performance of sales forecasting, (3) the advanced process gradually benefits from the mature process while it rapidly replaces the medium process in a “predator-prey” way.     

An agent for selecting optimal order set in EC marketplace

For most small manufacturing companies, the selection and completion of orders placed by buyers are closely linked to the load status of their production lines. The decision to accept an order, or the selection of optimal order set, critically depends on the production schedule when orders exceed production capacity. However, production scheduling is mainly performed by human experts, and small companies lack resources to implement such a task. As a result, most small manufacturers suffer from the difficulty to meet due dates or to make proper decision in accepting new orders. To address this problem, this paper develops an automatic agent that selects an optimal set of orders using commonly available Internet technology. The main engine of the selection agent is based on a typical job shop scheduling model, formulating and implementing it as an Integer Program (IP) model. We also translate IP into Genetic Algorithm (GA) to address its NP-hard problem. We conclude with a suggestion for an agent architecture that tackles Web-based order selection problems.     

A dynamically weight adjustment in the consensus reaching process for group decision-making with hesitant fuzzy preference relations

The consensus reaching process is a dynamic and iterative process for improving group's consensus level before making a final decision in group decision-making (GDM). As the experts will express their opinions under their own intellectual level from different aspects, it is natural that the experts’ weights should reflect their judgment information. This paper proposes a dynamic way to adjust weights of decision-makers (DMs) automatically when they are asked to give original judgment information for GDM problems, in which the DMs express their judgment information by hesitant fuzzy preference relations (HFPRs). Two indices, an individual consensus index of hesitant fuzzy preference relation (ICIHFPR) and a group consensus index of hesitant fuzzy preference relation (GCIHFPR), are introduced. Normalisation of HFPRs with different numbers of possible values is taken into consideration for better computation and comparison. An iterative consensus reaching algorithm is presented with DMs’ weighting vector changing in each consensus reaching process and the process terminates until both the ICIHFPR and GCIHFPR are controlled within predefined thresholds. Finally, an example is illustrated and comparative analyses demonstrate the effectiveness of the proposed methods.     

A multi-agent system to construct production orders by employing an expert system and a neural network

The authors describe the implementation of a multi-agent system, whose goal is to enhance production planning i.e. to improve the construction of production orders. This task has been carried out traditionally by the module known as production activity control (PAC). However, classic PAC systems lack adaptive techniques and intelligent behaviour. As a result they are mostly unfit to handle the NP Hard combinatorial problem underlying the construction of right production orders. To overcome this situation, we illustrate how an intelligent and collaborative multi-agent system (MAS) obtains a correct production order by coordinating two different techniques to emulate intelligence. One technique is performed by a feed-forward neural network (FANN), which is embedded in a machine agent, the objective being to determine the appropriate machine in order to fulfil clients’ requirements. Also, an expert system is provided to a tool agent, which in turn is in charge of inferring the right tooling. The entire MAS consists of a coordinator, a spy, and a scheduler. The coordinator agent has the responsibility to control the flow of messages among the agents, whereas the spy agent is constantly reading the Enterprise Information System. The scheduler agent programs the production orders. We achieve a realistic MAS that fully automates the construction and dispatch of valid production orders in a factory dedicated to produce labels.     

Regenerative scheduling problem in engineer to order manufacturing: an economic assessment

The dynamic production scheduling is a very complex process that may arise from the occurrence of unpredictable situations such as the arrival of new orders besides the ones already accepted. As a consequence, companies may often encounter several difficulties to make decisions about the new orders acceptance and sequencing along with the production of the existing ones. With this recognition, a mathematical programming model for the regenerative scheduling problem with deterministic processing times is formulated in the present paper to evaluate the economic advantage of accepting a new order in an engineer to order (ETO) manufacturing organization. The real case of an Italian ETO company which produces hydraulic marine and offshore cranes is afterwards presented.

     

Predicting financial activity with evolutionary fuzzy case-based reasoning

Predicting financial activity through examining the short-term liquidity is crucial within today’s turbulent financial environment. Firms, governments, and individuals all need an effective methodology based on liquidity information that plays performance deterioration warning a priori bankruptcy prediction. In this paper, we propose a hybrid decision model using case-based reasoning augmented with genetic algorithms (GAs) and the fuzzy k nearest neighbor (fuzzy k-NN) methods for predicting the financial activity rate. GAs are used to determine the optimal or near-optimal weight vector of financial features expressed in linguistic values by the expert. A fuzzy k-NN-based CBR scheme is designed to compute memberships of financial activity rates and to provide a more flexible and practical mechanism for acquiring, creating, and reusing the expert’s decision knowledge. An empirical experimentation using 746 publicly traded Taiwanese firms shows that the average accuracy of the rating is about 92.36%, which is superior to other related models. The proposed approach not only can lend support to the decision of an expert, but also allow proper feedback for the expert to improve the quality of the decision.     

Dynamic and adaptive composition of e-services

The Web is rapidly becoming the platform through which many companies deliver services to businesses and individual customers. The number and type of on-line services increase day by day, and this trend is likely to continue at an even faster pace in the immediate future. Examples of e-services currently available include bill payment, delivery of customized news, or archiving and sharing of digital documents. E-Services are typically delivered individually. However, the e-service market creates the opportunity for providing value-added, integrated services, which are delivered by composing existing e-services. To support organizations in pursuing this business opportunity we have developed eFlow, a system that supports the specification, enactment, and management of composite e-services, modeled as processes that are enacted by a service process engine. Composite e-services have to cope with a highly dynamic business environment in terms of services and of service providers. In addition, the increased competition forces companies to provide customized services to better satisfy the needs of every individual customer. Ideally, service process should be able to transparently adapt to changes in the environment and to the need of different customers with minimal or no user intervention. In addition, it should be possible to dynamically modify service process definitions in a simple and effective way to manage cases where user intervention is indeed required. In this paper we show how eFlow achieves these goals.     

Confidence levels q-rung orthopair fuzzy aggregation operators and its applications to MCDM problems

The concept of q-rung orthopair fuzzy set (q-ROFS) is the extension of intuitionistic fuzzy set (IFS) in which the sum of the qth power of the support for and the qth power of the support against is bounded by one. Therefore, the q-ROFSs are an important way to express uncertain information in broader space, and they are superior to the IFSs and the Pythagorean fuzzy sets. In this paper, the familiarity degree of the experts with the evaluated objects is incorporated to the initial assessments under q-rung orthopair fuzzy environment. For this, some aggregation operators are proposed to combine these two types of information. Their some important properties are also well proved. Furthermore, these developed operators are utilized in a multicriteria decision-making approach and demonstrated with a real life problem of customers' choice. Then, the experimental results are compared with other existing methods to show its superiority over recent research works.     

Ontological fuzzy agent for electrocardiogram application

The electrocardiogram (ECG) signal is adopted extensively as a low-cost diagnostic procedure to provide information concerning the healthy status of the heart. However, the QRS complex must be calculated accurately before proceeding with the heart rate variability (HRV). In particular, the R peak needs to be detected reliably. This study presents an adaptive fuzzy detector to detect the R peak correctly. Additionally, an ontological fuzzy agent is presented to process the collection of ECG signals. The required knowledge is stored in the ontology, which comprises some personal ontologies and predefined by domain experts. The ontological fuzzy agent retrieves the ECG signals with R peaks marked for HRV analysis and ECG further applications. It contains a personal fuzzy filter, an HRV analysis mechanism, and a fuzzy normed inference engine. Moreover, the ECG fuzzy signal space and some important properties are presented to define the working environment of the agent. An experimental platform has been constructed to test the performance of the agent. The results indicate that the proposed method can work effectively.     

A comparison of managers' and safety delegates' perceptions of work environment priorities in the manufacturing industry

This study investigated the attention and priority accorded to factors of work environment within manufacturing companies, as perceived by managers and safety delegates at small‐ and medium‐sized enterprises. Representatives from 142 Swedish manufacturing companies answered a 43‐item questionnaire covering seven areas of the work environment and a priority‐ranking question of company interests. Respondents (n = 249) rated the present situation and the situation one year earlier, using a visual analogue scale. The findings showed that both managers and safety delegates ranked profitability as the main company interest. Respondents rated the priorities of the work environment currently as higher than one year earlier (p = <0.05). Managers rated the priorities of the work environment higher than did the safety delegates (p = <0.05). We conclude that the two professional roles, manager and safety delegate, differ in their perceptions regarding to what extent different work environment factors are being attended to. © 2011 Wiley Periodicals, Inc.     

A collaborative and artificial intelligence approach for semiconductor cost forecasting

Forecasting the unit cost of a semiconductor product is an important task to the manufacturer. However, it is not easy to deal with the uncertainty in the unit cost. In order to effectively forecast the semiconductor unit cost, a collaborative and artificial intelligence approach is proposed in this study. In the proposed methodology, a group of domain experts is formed. These domain experts are asked to configure their own fuzzy neural networks to forecast the semiconductor unit cost based on their viewpoints. A collaboration mechanism is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, a radial basis function (RBF) network is used. The effectiveness of the proposed methodology is shown with a case study.