Multimodal Fuzzy Downstream Petroleum Supply Chain:A Novel Pentagonal Fuzzy Optimization

The petroleum industry has a complex,inflexible and challenging supply chain(SC)that impacts both the national economy as well as people’s daily lives with a range of services,including transportation,heating,electricity,lubricants,as well as chemicals and petrochemicals.In the petroleum industry,supply chain management presents several challenges,especially in the logistics sector,that are not found in other industries.In addition,logistical challenges contribute significantly to the cost of oil.Uncertainty regarding customer demand and supply significantly affects SC networks.Hence,SC flexibility can be maintained by addressing uncertainty.On the other hand,in the real world,decision-making challenges are often ambiguous or vague.In some cases,measurements are incorrect owing to measurement errors,instrument faults,etc.,which lead to a pentagonal fuzzy number(PFN)which is the extension of a fuzzy number.Therefore,it is necessary to develop quantitative models to optimize logistics operations and supply chain networks.This study proposed a linear programming model under an uncertain environment.The model minimizes the cost along the refineries,depots,multimode transport and demand nodes.Further developed pentagonal fuzzy optimization,an alternative approach is developed to solve the downstream supply chain using themixed-integer linear programming(MILP)model to obtain a feasible solution to the fuzzy transportation cost problem.In this model,the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number.Furthermore,defuzzification is performed using an accuracy function.To validate the model and technique and feasibility solution,an illustrative example of the oil and gas SC is considered,providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.

Fuzzy optimization design of multicomponent refinery hydrogen network

Hydrogen and light hydrocarbon components are essential resources of the refinery.The optimization of the refinery hydrogen system and recovery of the light hydrocarbon components contained in the gas streams are key strategies to reduce the operating costs for sustainable development.Many research efforts have been focused on the optimization of single impurity hydrogen network,and the flowrates of the hydrogen sources and sinks are assumed to be constant.However,their flowrates vary along with the quality of crude oil and refinery processing plans.A general superstructure of multicomponent refinery hydrogen network is proposed,which considers four components,namely H_(2),H_(2)S,CH_(4) and C_(2+),as well as the flowrate variations of hydrogen source and hydrogen sink.The mathematical model based on the superstructure is developed with objective functions,including the minimization of total annualized cost and the maximization of overall satisfaction of the hydrogen network.Moreover,the model considers the removal of hydrogen sulfide and the recovery of light hydrocarbon components(i.e.,C_(2+))in the optimization.To verify the applicability of the proposed mathematical model,a simplified industrial case study with four scenarios is solved.The optimization results show that the economic benefit can be maximized by considering both the direct reuse of gas streams from high-pressure separator(HP gas stream)and from low-pressure separator(LP gas stream)and the recovery of the light hydrocarbon streams.The fuzzy optimization method can be used to guide the optimal design of the refinery hydrogen system with multi-period variable flowrates.

Fuzzy optimization neural network model based on LM algorithm

A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In this new model,the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training,which changes the weights adjusting equations of the network and increases the training speed. Moreover,to avoid the results yielding to local minimum,the transfer function is also revised to sigmoid function. A case study is utilized to validate this new model,and the results reveal that the new model fast training speed and better forecasting capability.

Fuzzy optimization of space frame

Fuzzy concepts are introduced into structural optimization to solve fuzzyoptimization problems with a crisp objective function and fuzzy constraints, also a non-membershipfunction is used to convert fuzzy constrains into crisp constrains. Two models are discussed wherethe objective function considered is the volume of space frame and the fuzzy constrains are designlimits by the axial strength, slenderness, deflection, thickness and diameter of space frame member.

Fuzzy Optimization of an Elevator Mechanism Applying the Genetic Algorithm and Neural Networks

Considering the indefinite character of the value of design parameters and being satisfied with load-bearing capacity and stiffness, the fuzzy optimization mathematical model is set up to minimize the volume of tooth corona of a worm gear in an elevator mechanism. The method of second-class comprehensive evaluation was used based on the optimal level cut set, thus the optimal level value of every fuzzy constraint can be attained; the fuzzy optimization is transformed into the usual optimization. The Fast Back Propagation of the neural networks algorithm are adopted to train feed-forward networks so as to fit a relative coefficient. Then the fitness function with penalty terms is built by a penalty strategy, a neural networks program is recalled, and solver functions of the Genetic Algorithm Toolbox of Matlab software are adopted to solve the optimization model.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor

In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.

Fuzzy Optimization of Storage Space Allocation in a Container Terminal

A fuzzy optimization model of storage space allocation is proposed,and a rolling-planning method is derived. The model takes the uncertainty of departure time of import containers and arrival time of export containers into account. For each planning horizon,the problem is decomposed into two levels: the first level minimizes the unbalanced workloads among blocks using hybrid intelligence algorithm;based on block workloads allocated in the above level,the second level minimizes the number of blocks to which the same group of import containers are split. Numerical results show that the model reduces workload imbalance,and speeds up the vessel loading and discharging process.

Time-Variant Reliability-Based Multi-Objective Fuzzy Design Optimization for Anti-Roll Torsion Bar of EMU

Although various types of anti-roll torsion bars have been developed to inhibit excessive roll angle of the electric multiple unit(EMU)car body,it is critical to ensure the reliability of structural design due to the complexity of the problems involving time and uncertainties.To address this issue,amulti-objective fuzzy design optimization model is constructed considering time-variant stiffness and strength reliability constraints for the anti-roll torsion bar.A hybrid optimization strategy combining the design of experiment(DoE)sampling and non-linear programming by quadratic lagrangian(NLPQL)is presented to deal with the design optimization model.To characterize the effect of time on the structural performance of the torsion bar,the continuous-time model combined with Ito lemma is proposed to establish the time-variant stiffness and strength reliability constraints.Fuzzy mathematics is employed to conduct uncertainty quantification for the design parameters of the torsion bar.A physical programming approach is used to improve the designer’s preference and to make the optimization results more consistent with engineering practices.Moreover,the effectiveness of the proposed method has been validated by comparing with current methods in a practical engineering case.

Fuzzy linear model for production optimization of mining systems with multiple entities

Planning and production optimization within multiple mines or several work sites （entities） mining systems by using fuzzy linear programming （LP） was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.

Product design on the basis of fuzzy quality function deployment

In the implementation of quality function deployment （QFD）, the determination of the target values of engineering characteristics is a complex decision process with multiple variables and multiple objectives that should trade off, and optimize all kinds of conflicts and constraints. A fuzzy linear programming model （FLP） is proposed. On the basis of the inherent fuzziness of QFD system, triangular fuzzy numbers are used to represent all the relationships and correlations, and then, the functional relationships between the customer needs and engineering characteristics and the functional correlations among the engineering characteristics are determined with the information in the house of quality （HoQ） fully used. The fuzzy linear programming （FLP） model aims to find the optimal target values of the engineering characteristics to maximize the customer satisfaction. Finally, the proposed method is illustrated by a numerical example.

Reliability Growth Modeling and Optimal Release Policy Under Fuzzy Environment of an N-version Programming System Incorporating the Effect of Fault Removal Efficiency

Failure of a safety critical system can lead to big losses. Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems. Fault-tolerant softwares are used to increase the overall reliability of software systems. Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery （recovery block scheme）, fault masking （N-version programming （NVP）） or a combination of both （Hybrid scheme）. These softwares incorporate the ability of system survival even on a failure. Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems. Most of them consider the stable system reliability. Few attempts have been made in reliability modeling to study the reliability growth for an NVP system. Recently, a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency. In this model, a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed. In this paper, we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation. Using this model, a software reliability growth model （SRGM） is developed to model the reliability growth of an NVP system. The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required. It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost. In this paper, we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.

Bipolar Interval-Valued Neutrosophic Optimization Model of Integrated Healthcare System

Bipolar Interval-valued neutrosophic set is another generalization of fuzzy set,neutrosophic set,bipolar fuzzy set and bipolar neutrosophic set and thus when applied to the optimization problem handles uncertain data more efficiently and flexibly.Current work is an effort to design a flexible optimization model in the backdrop of interval-valued bipolar neutrosophic sets.Bipolar interval-valued neutrosophic membership grades are picked so that they indicate the restriction of the plausible infringement of the inequalities given in the problem.To prove the adequacy and effectiveness of the method a unified system of sustainable medical healthcare supply chain model with an uncertain figure of product complaints is used.Time,quality and cost are considered as satisfaction level to choose best supplier for medicine procurement.The proposed model ensures 99%satisfaction for cost reduction,63%satisfaction for the quality of product and 64%satisfaction for total time taken in medicine supply chain.

New judging model of fuzzy cluster optimal dividing based on rough sets theory

To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory： information system discrete algorithm （algorithm 1） and samples representatives judging algorithm （algorithm 2）. On the principle of the farthest distance, algorithm 1 transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.

ODFF: Optimized Dual Fuzzy Flow Controller Based Voltage Sag Compensation for SMES-Based DVR in Power Quality Applications

The booming electronics itself carries an impact on power quality. Superconducting Magnetic Energy Storage (SMES) is proposed to enhance power quality in three-phase systems under various loads. This paper aimed to compensate the voltage sags under various faults in the grid systems. The SMES is selected as an energy storage unit to improve the capability of voltage sag compensation. Optimized Dual Fuzzy Flow (ODFF) logic controller is designed to prevent the voltage sag time during excessive phase voltage variation. Hence the proposed controller strategy reduces the total harmonic distortion during various fault conditions. To regulate the contribution of active power, the least possible value is improved using ODFF. The depth of voltage sags compensation is achieved by the over modulation and an iterative loop is designed in the control block. While protecting sensitive loads from voltage disturbances, and sags initiated by the power system, the proposed configuration is advantageous for an industrial implementation. It is found that the proposed method can result in more than 50% additional sag support time when compared with the previous methods such as PI and PSO. Utilizing MATLAB Simulink, compensation of sag and minimization of THD is established, and the simulation tests are performed to evaluate the performance of the proposed control method.

The order structure of fuzzy numbers based on the level characteristics and its application to optimization problems

Ranking and comparing fuzzy numbers is an important part in many fuzzy optimization problems such as intelligent control and manufacturing system production line scheduling with uncertainty environments. In this paper, based on the level characteristic function and α-average of level cut sets of fuzzy number, we establish the IMα-metric method for measuring fuzzy number as a whole, and introduce the concept of IDα-difFerence that describes the reliability of IMα-metric value. Further, the basic properties and the separability of IMα-metric and IDα-difference are discussed. Finally, we give a mathematical model to solve fuzzy optimization problems by means of IMα-metric.

Simplified graphical correlation for determining flow rate in tight gas wells in the Sulige gas field

The Sulige tight gas reservoir is characterized by low-pressure, low-permeability and lowabundance. During production, gas flow rate and reservoir pressure decrease sharply; and in the shut- in period, reservoir pressure builds up slowly. Many conventional methods, such as the indicative curve method, systematic analysis method and numerical simulation, are not applicable to determining an appropriate gas flow rate. Static data and dynamic performance show permeability capacity, kh is the most sensitive factor influencing well productivity, so criteria based on kh were proposed to classify vertical wells. All gas wells were classified into 4 groups. A multi-objective fuzzy optimization method, in which dimensionless gas flow rate, period of stable production, and recovery at the end of the stable production period were selected as optimizing objectives, was established to determine the reasonable range of gas flow rate. In this method, membership functions of above-mentioned optimizing factors and their weights were given. Moreover, to simplify calculation and facilitate field use, a simplified graphical illustration （or correlation） was given for the four classes of wells. Case study illustrates the applicability of the proposed method and graphical correlation, and an increase in cumulative gas production up to 37% is achieved and the well can produce at a constant flow rate for a long time.

Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization

This paper presents a novel modified inter- active honey bee mating optimization （IHBMO） base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources （DERs）. The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting, A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduc- tion in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power orDERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.

模糊数值函数的强广义凸性及其在模糊优化中的应用

Based on the ordering of fuzzy numbers proposed by Goetschel and Voxman,the representations and some properties of strongly preinvex fuzzy-valued function are defined and obtained, several new concepts of strongly monotonicities fuzzy functions are introduced, the relationship among the strongly preinvex, strongly invex and monotonicities under some suitable and appropriate conditions is established and a necessary condition for strongly pseudoinvex functions is given. As an application, the conditions of local optimal solution and global optimal solution in the mathematical programming problem are discussed.

OPTIMAL TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLE WITH AUTOMATIC MECHANICAL TRANSMISSION

In parallel hybrid electrical vehicle （PHEV） equipped with automatic mechanical transmission （AMT）, the driving smoothness and the clutch abrasion are the primary considerations for powertrain control during gearshift and clutch operation. To improve these performance indexes of PHEV, a coordinated control system is proposed through the analyzing of HEV powertrain dynamic characteristics. Using the method of minimum principle, the input torque of transmission is optimized to improve the driving smoothness of vehicle. Using the methods of fuzzy logic and fuzzy-PID, the engaging speed of clutch and the throttle opening of engine are manipulated to ensure the smoothness of clutch engagement and reduce the abrasion of clutch friction plates. The motor provides the difference between the required input torque of transmission and the torque transmitted through clutch plates. Results of simulation and experiments show that the proposed control strategy performs better than the contrastive control system, the smoothness of driving and the abrasion of clutch can be improved simultaneously.