Fuzzy Production Research Articles

Adaptive fuzzy spiking neural P systems for fuzzy inference and learning

Fuzzy spiking neural P systems (in short, FSN P systems) are a novel class of distributed parallel computing models, which can model fuzzy production rules and apply their dynamic firing mechanism to achieve fuzzy reasoning. However, these systems lack adaptive/learning ability. Addressing this problem, a class of FSN P systems are proposed by introducing some new features, called adaptive fuzzy spiking neural P systems (in short, AFSN P systems). AFSN P systems not only can model weighted fuzzy production rules in fuzzy knowledge base but also can perform dynamically fuzzy reasoning. It is important to note that AFSN P systems have learning ability like neural networks. Based on neuron's firing mechanisms, a fuzzy reasoning algorithm and a learning algorithm are developed. Moreover, an example is included to illustrate the learning ability of AFSN P systems.

Adapting Multi-Robot Behavior to Communication Atmosphere in Humans-Robots Interaction Using Fuzzy Production Rule Based Friend-Q Learning

A behavior adaptation mechanism in humans-robots interaction is proposed to adjust robots’ behavior to communication atmosphere, where fuzzy production rule based friend-Q learning (FPRFQ) is introduced. It aims to shorten the response time of robots and decrease the social distance between humans and robots to realize the smooth communication of robots and humans. Experiments on robots/humans interaction are performed in a virtual communication atmosphere environment. Results show that robots adapt well by saving 44 and 482 learning steps compared to that by friend-Q learning (FQ) and independent learning (IL), respectively; additionally, the distance between human-generated atmosphere and robot-generated atmosphere is 3 times and 10 times shorter than the FQ and the IL, respectively. The proposed behavior adaptation mechanism is also applied to robots’ eye movement in the developing humans-robots interaction system, calledmascot robot system, and basic experimental results are shown in home party scenario with five eye robots and four humans.

Fault Diagnosis of Wind Turbine Hydraulic Variable Pitch Systems Based on FPN

The knowledge representation based on fuzzy Petri nets included library, transition, input, output, input confidence, output confidence and so on. Since the fuzzy knowledge had been preferably expressed and processed by model of fuzzy Petri nets based on fuzzy production rules, a fuzzy Petri nets model of hydraulic variable pitch system for wind turbine generator was established. In this paper, the fuzzy Petri net was represented in the form of matrix and a parallel reasoning algorithm was applied in fault diagnosis. The algorithm had analyzed many factors, such as weights, thresholds of transitions, reliability of rules. Beside this, according to the algorithm, the trigger condition of transitions became more strictly. It avoided unnecessary repetitions of transitions trigger effectively, and reduced the complexity of the algorithm. At last, the algorithm was compiled on the platform of Matlab with its powerful capabilities of matrix operations and data analysis. The result had shown that it had the ability of parallel reasoning for complex system and that it is highefficiency and is easy to implement.

The Design and Application of the Guide Rod of PROTOS Cigarette Machine

When the PROTOS cigarette machine for paper printing system is expanding the cigarette paper, it produce a lot of dust which easy to adhere to the ink roller and stamp fonts gap, leading to the cigarette stamp dirty, fuzzy cigarette product quality defects. To solve this problem, a negative pressure vacuum system is installed inside the guide rod of the cigarette paper, which help to discharge the cigarette dust to the dust removal system, and improve guide ring of the guide rod to reducing the friction. The application results show that the negative pressure vacuum is an effective solution to solve the dust floated problem. The unqualified rate reduce from 161.3PPM to 26.1PPM.It improve the effective operation of the device and ensure product quality.

Three-stage supply chain coordination under fuzzy random demand and production rate with imperfect production process

This paper considers three-stage supply chain (SC) coordination and focuses on the fuzziness as well as randomness aspect of demand and production rate. It is assumed that the production process is not perfect and deterioration takes place when it is physically present in stock. To obtain the optimal solution, graded mean integration method is employed to defuzzify the estimate of total cost of SC. Through numerical example, it is shown that the integrated policy results in an impressive cost reduction when it is compared with the independent decisions made by the producer, the distributor and the retailer. At the end, sensitivity analysis is performed with respect to different parameters.

A Fuzzy Collaborative Forecasting Approach for Forecasting the Productivity of a Factory

Productivity is always considered as one of the most basic and important factors to the competitiveness of a factory. For this reason, all factories have sought to enhance productivity. To achieve this goal, we first need to estimate the productivity. However, there is considerable degree of uncertainty in productivity. For this reason, a fuzzy collaborative forecasting approach is proposed in this study to forecast the productivity of a factory. First, a learning model is established to estimate the future productivity. Subsequently, the learning model is converted into three equivalent nonlinear programming problems to be solved from various viewpoints. The fuzzy productivity forecasts by different experts may not be equal and should therefore be aggregated. To this end, the fuzzy intersection and back propagation network approach is applied. The practical example of a dynamic random access memory (DRAM) factory is used to evaluate the effectiveness of the proposed methodology.

Research on Method of Ecomaterials Life Cycle Assessment

It is generally considered, as the material basis of human survival and production, materials industry is one of the main reasons causing energy shortages, excessive consumption of resources and environmental pollution, the issues on ecomaterials life cycle assessment are also increasingly subject to attention. In this paper, Fuzzy decision trees is applied due to they are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. And Fuzzy ID3 algorithm is used, a popular and particularly efficient method, to construct fuzzy decision tree. We use weighted fuzzy production rules generated from FDT to establish assessment model. In addition, a new method is proposed, based on fuzzy rules and degree of confidence, to set the band of assessment. Classification accuracy was used to examine results of the system. The results indicated that the system is very valid for eco-materials life cycle assessment.

Fuzzy weighted association rule based solution approaches to facility layout problem in cellular manufacturing system

This study aims to propose new solution approaches based on fuzzy weighted association rules for facility layout problem in a cellular manufacturing system. The facility layout problem under concern has two important aspects, namely; fuzzy product route and fuzzy machine weight. Number of machine types as well as the number of machines in each type is determined in order to define a fuzzy product route. A fuzzy set is then assigned for each machine type to match them with appropriate products. Alternative routes for each product and machine weights are defined by fuzzy functions. Market demand is also used to assign weights to the machines. Three solution approaches, namely fuzzy weighted association rule mining(FWARM), Gyenesei’s weighted quantitative association rules (WQAR) and normalised weighted association rule mining(NWARM) based approaches, are proposed in this study. To the authors’ best knowledge, this is the first attempt which considers fuzzy weighted association rule-based data mining approaches to the facility layout problem. An illustrative example is presented to demonstrate the usefulness of the proposed approaches.

A production inventory model with fuzzy production and demand using fuzzy differential equation: An interval compared genetic algorithm approach

In this paper, a production inventory model, specially for a newly launched product, is developed incorporating fuzzy production rate in an imperfect production process. Produced defective units are repaired and are sold as fresh units. It is assumed that demand coefficients and lifetime of the product are also fuzzy in nature. To boost the demand, manufacturer offers a fixed price discount period at the beginning of each cycle. Demand also depends on unit selling price. As production rate and demand are fuzzy, the model is formulated using fuzzy differential equation and the corresponding inventory costs and components are calculated using fuzzy Riemann-integration. α-cut of total profit from the planning horizon is obtained. A modified Genetic Algorithm (GA) with varying population size is used to optimize the profit function. Fuzzy preference ordering (FPO) on intervals is used to compare the intervals in determining fitness of a solution. This algorithm is named as Interval Compared Genetic Algorithm (ICGA). The present model is also solved using real coded GA (RCGA) and Multi-objective GA (MOGA). Another approach of interval comparison–order relations of intervals (ORI) for maximization problems is also used with all the above heuristics to solve the model and results are compared with those are obtained using FPO on intervals. Numerical examples are used to illustrate the model as well as to compare the efficiency of different approaches for solving the model.

Spare Parts Production Planning Model Based on Fuzzy Programming

In the field of civil aviation, MRO market has a strong seasonal characteristic. Combined with research on continuous airworthiness of the aircraft, the spare parts order procedure and flight planning process, and analysis on fuzzy demand and fuzzy production capacity, the model of spare parts order with fuzzy reliability constraints is established. Under the background of actual civil aviation enterprises, spare parts quantitative order model and algorithm in spare parts planning decision system in non-deterministic environment are put forward. It applies the theories and methods of fuzzy mathematics and probability and statistics, building the target programming model for reconfigurable spare parts, to formulate optimal reconfigurable spare parts production plan. Overall steps to solve the model are proposed, which make decision process in fuzzy environment more flexible, and reduce the number of emergency orders.

A Knowledge Representation and Learning Model Based on FNPN in PAAIS

Through used Fuzzy Petri Net to set up the knowledge representation and reasoning rules, a Fuzzy Neural Petri Network (FNPN) method whose parameters can be trained by Neural Network was proposed in this paper. The experimental result indicated that, the Neural Network and the Petri net combination can make the fuzzy production rule systems more effective, and also can be applied to most practical Petri net models and fuzzy systems.

EFFICIENCY IN FUZZY PRODUCTION POSSIBILITY SET

The existing Data Envelopment Analysis models for evaluating the relative eciency of a set of decision making units by using various inputs to produce various outputs are limited to crisp data in crisp production possi- bility set. In this paper, rst of all the production possibility set is extended to the fuzzy production possibility set by extension principle in constant re- turn to scale, and then the fuzzy model of Charnes, Cooper and Rhodes in input oriented is proposed so that it satises the initial concepts with crisp data. Finally, the fuzzy model of Charnes, Cooper and Rhodes for evaluating decision making units is illustrated by solving two numerical examples.

Fuzzy production inventory model for deteriorating items with shortages under the effect of time dependent learning and forgetting: a possibility / necessity approach

This study investigates the effects of learning and forgetting on the production lot size problems allowing shortages for the infinite planning horizon. Items deteriorate while they are in storage, and both demand and deterioration rates are arbitrary function of time. This paper extends the work of Alamri and Balkhi (Int. J. Prod. Econ. 107:125–138, 2007) by assuming the shortages in production lot size model subject to the effects of learning and forgetting in fuzzy environment. The system is subject to learning in the production stage and to forgetting while production ceased so that the optimal manufactured quantity for any given cycle is dependent on the instantaneous production rate. All cost are taken constants or fuzzy in nature. Hence two models are introduced separately with constant and fuzzy cost. A closed form for the total relevant costs is derived, that minimizes total cost of the underlying inventory system. Model with fuzzy costs is formulated as to optimize the possibility/ necessity measure of the fuzzy goal of the objective function. When costs are imprecise, optimistic and pessimistic equivalent of fuzzy objective function is obtained by using credibility measure of fuzzy event by taking fuzzy expectation. The models are illustrated with three examples as well as their numerical verifications are also given.

Fuzzy reasoning spiking neural P system for fault diagnosis

Spiking neural P systems (SN P systems) have been well established as a novel class of distributed parallel computing models. Some features that SN P systems possess are attractive to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required for many fault diagnosis applications. The lack of ability is a major problem of existing SN P systems when applying them to the fault diagnosis domain. Thus, we extend SN P systems by introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems). The FRSN P systems are particularly suitable to model fuzzy production rules in a fuzzy diagnosis knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning algorithm based on FRSN P systems is developed according to neuron’s dynamic firing mechanism. Besides, a practical example of transformer fault diagnosis is used to demonstrate the feasibility and effectiveness of the proposed FRSN P systems in fault diagnosis problem.

Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models

The main objective of this study is to evaluate and compare the results of evidential belief functions and fuzzy logic models for spatial prediction of landslide hazards in the Hoa Binh province of Vietnam, using geographic information systems. First, a landslide inventory map showing the locations of 118 landslides that have occurred during the last ten years was constructed using data from various sources. Then, the landslide inventory was randomly partitioned into training and validation datasets (70% of the known landslide locations were used for training and building the landslide models and the remaining 30% for the model validation). Secondly, nine landslide conditioning factors were selected (i.e., slope, aspect, relief amplitude, lithology, landuse, soil type, distance to roads, distance to rivers and distance to faults). Using these factors, landslide susceptibility index values were calculated using evidential belief functions and fuzzy logic models. Finally, landslide susceptibility maps were validated and compared using the validation dataset that was not used in the model building. The prediction-rate curves and area under the curves were calculated to assess prediction capability. The results show that all the models have good prediction capabilities. The model derived using evidential belief functions has the highest prediction capability. The model derived using fuzzy SUM has the lowest prediction capability. The fuzzy PRODUCT and fuzzy GAMMA models have almost the same prediction capabilities. In general, all the models yield reasonable results that may be used for preliminary landuse planning purposes.

A learning Fuzzy Petri net model

AbstractFuzzy Petri net (FPN) is a powerful modeling tool for the construction of knowledge systems. In this paper, we propose a new learning model tool—learning fuzzy Petri net (LFPN). In contrast with the existing FPN, there are three extensions in the new model: (i) the place can possess different tokens which represent different propositions; (ii) these propositions have different degrees of truth toward different transitions; and (iii) the truth degree of the proposition can be learned by adjusting the arc's weight function. The LFPN model obtains the capability of learning fuzzy production rules through truth degree updating. The LFPN learning algorithm which introduces network learning method into Petri net update is proposed and the convergence of algorithm is analyzed. Finally, for the purpose of certification of the effectiveness of the proposed model, LFPN is used to model the Web service discovery. The result of the simulation shows that the proposed LFPN is useful and effective. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A fuzzy rule-based approach for screening international distribution centres

In practical situations, a decision maker often has to deal with numerous alternatives in evaluating and selecting the most suitable international distribution centre (IDC) location for enhancing the organizational competitiveness. To improve the effectiveness of the decision making process, it is critical to reduce the number of alternatives through screening so that the decision maker can focus on the most attractive alternatives for further consideration. This paper presents a fuzzy rule-based approach to effectively screen decision alternatives in adequately solving the problem of evaluating and selecting IDC locations. Case-based reasoning with the use of fuzzy production rules is adopted to screen out unattractive alternatives based on past experience in a specific decision making situation. A decision support system based on the proposed approach is presented to improve the effectiveness of the screening process. An example is given to demonstrate the applicability of the proposed approach in improving the effectiveness of the decision making process in multicriteria decision making.

Preemptive Fuzzy Goal Programming in Fuzzy Environments

There are a variety of efficient approaches to solve crisp multiple objective decision making problems. However in the real life the input data may not be precisely determined because of the incomplete information. This paper deals with a method which can be applied to solve fuzzy multi objective production marketing problems.

On the Theory of Intuitionistic Fuzzy N-Inner Product Spaces with Applications

Problem statement: The purpose of this study was to introduce the notion of Intuitionistic fuzzy n-inner product space and to prove a fixed point theorem in complete intuitionistic fuzzy ninner product space. Conclusion/Recommendations: Further this result is applied to obtain the existence and uniqueness of solution for linear volterra integral equation.

A volume flexible fuzzy production inventory model under interactive and simulation approach

This paper concerns Economic Production Quantity (EPQ) policies with volume flexiblity manufacturing system for deteriorating items. In this situation, the demand occurs in accordance with stock, after a certain level. In order to make more realistic, this paper proposes that the inventory costs, selling price, storage space, and available budget are defined in imprecise nature. The objective is to determine the optimal production rate and time that maximise the total profit of the system. The equivalent multi-objective problem with space and budget constraints is optimised using-(i) interactive fuzzy decision making approach, (ii) necessity approach jointly with fuzzy simulation and Contractive Mapping Genetic Algorithm (CMGA). The model is illustrated numerically and the results obtained from different approaches are compared.