Application of genetic algorithm for reliability allocation in nuclear power plants

Reliability allocation is an optimization process of minimizing the total plant costs subject to the overall plant safety goal constraints. Reliability allocation was applied to determine the reliability characteristics of reactor systems, subsystems, major components and plant procedures that are consistent with a set of top-level performance goals; the core melt frequency, acute fatalities and latent fatalities. Reliability allocation can be performed to improve the design, operation and safety of new and/or existing nuclear power plants. Reliability allocation is a kind of a difficult multi-objective optimization problem as well as a global optimization problem. The genetic algorithm, known as one of the most powerful tools for most optimization problems, is applied to the reliability allocation problem of a typical pressurized water reactor in this article. One of the main problems of reliability allocation is defining realistic objective functions. Hence, in order to optimize the reliability of the system, the cost for improving and/or degrading the reliability of the system should be included in the reliability allocation process. We used techniques derived from the value impact analysis to define the realistic objective function in this article.     

A comprehensive reliability allocation method for design of CNC lathes

In the design and development of computerized numerical control lathes, an effective reliability allocation method is needed to allocate system level reliability requirements into subsystem and component levels. During the allocation process, many factors have to be considered. Some of these factors can be measured quantitatively while others have to be assessed qualitatively. In this paper, we consider seven criteria for conducting reliability allocation. A comprehensive failure rate allocation method is proposed for conducting the task of reliability allocation. Example data from field studies are used to illustrate the proposed method.     

A factory-level dynamic operator allocation policy: the bubble allocation

In this paper, we propose a factory-level dynamic operator allocation policy called the bubble allocation policy. This policy is commonly implemented in labour-intensive industries in the presence of different operator speeds, high labour turnover, and learning effects. We prove the optimality of bubble allocation in several typical scenarios under deterministic and exponential processing time and different operator speed assumptions. When labour turnover and learning effects were considered, the effects of the bubble allocation were verified through simulation. Bubble allocation had a more significant positive effect on system throughput than the passive operator allocation policy. The positive effects of bubble allocation are enhanced with a larger production system scale (more parallel lines and more stations), higher turnover rate and slower learning process. Compared with active allocation policies such as work-sharing policy, bubble allocation policy has no requirements for additional cross-training and is not sensitive to the switching time of tasks. The bubble allocation policy stands out when the system is large and the flow line is designed in a balanced way.     

Concurrent optimization of assembly tolerances for quality with position control using scatter search approach

Tolerance allocation to individual parts in any assembly should be a vital design function with which both the design and manufacturing engineers are concerned. Generally design engineers prefer to have tighter tolerances to ensure the quality of their design, whereas manufacturing engineers prefer loose tolerances for ease of production and the need to be economical. This paper introduces a concurrent tolerance approach, which determines optimal product tolerances and minimizes combined manufacturing and quality related costs in the early stages of design. A non-linear multivariable optimization model is formulated here for assembly. A combinatorial optimization problem by treating cost minimization as the objective function and stack-up conditions as the constraints are solved using scatter search algorithm. In order to further explore the influence of geometric tolerances in quality as well as in the manufacturing cost, position control is included in the model. The results show how position control enhances quality and reduces cost.     

A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy consumption.     

A System's Mean Time To Repair Allocation Method Based on the Time Factors

The aim of the maintenance allocation is to allocate the maintenance time to the subsystems reasonably and accurately. Traditional mean time to repair (MTTR) allocation methods are widely used in the industry. However, there are many deficiencies in practical application, which is inconsistent with the purpose of the maintenance allocation. To allocate the MTTR more precisely and reasonably, a better model is needed. The aim of this paper is to develop a maintenance allocation model, which can improve the applicability and operability, and solve the residue problem that exists in maintenance allocation. A case study is used to demonstrate how the model can be applied in practice. Copyright © 2013 John Wiley & Sons, Ltd.     

Reliability Allocation for Series-Parallel Systems subject to Potential Propagated Failures

To analyze the dependent failures in the early stage of system development, this paper considers the potential propagated failures in the reliability allocation process. Factors which can be used to not only measure the component importance but also to reflect the influence brought by propagated failures are proposed. Specifically, cooperative game theory is introduced to explore how the propagated failures affect the failure severity level. Failure rates are obtained by using the Alpha Factor Model with the consideration of dependence among components. Reliability improvement rate is also developed to proportionally assign the target improvement of system reliability to the corresponding components. Furthermore, reliability allocation frameworks for series, parallel and series-parallel systems are designed respectively to make the proposed model meet a wide range of applications. An illustrative example of a hydraulic cooling system is presented to show how the proposed approach is applied. The allocation results demonstrate that the proposed method can achieve a valid reliability improvement with the minimum error.     

A Multi‐attribute Reliability Allocation Method Considering Uncertain Preferences

In reliability allocation, certain reliability values are assigned to subsystems and components to achieve the required system reliability. One big challenge in solving such reliability‐based design problems is how to handle the uncertain preferences of a decision maker on multiple attributes of interest. In this paper, we propose a new ordered weighted averaging (OWA) method based on an analytic hierarchy process to address the decision maker's uncertain preferences in reliability allocation. In the proposed OWA operator, a bi‐objective mathematical programming model considering both maximal entropy and minimal variance is transformed into a single‐objective mathematical programming model using an ideal‐point method. The maximum entropy minimal variance OWA operator takes full advantage of available information and avoids overestimating the decision maker's preferences. A detailed computational procedure is presented to facilitate the implementation of the proposed method in practice. An illustrative example about the powertrain of fuel cell vehicles is provided to demonstrate the effectiveness of this method in handling multiple attributes with uncertain preferences in reliability allocation. Copyright © 2015 John Wiley & Sons, Ltd.     

基于质变与量变的供应链利润分配机制设计

设计的供应链合作利润分配机制定量模型由两部分构成,一是合作利润结构性分配,其是机制中的主体分配框架,具有分配中"质变"的性质,结构性分配是通过合作利润分配博弈模型来解得;二是合作利润运行性分配,其是按照利润分配机制设置的三个特征(理性、效用转移性、协商性)、根据合作利润增量转移量模型来设计的,其是基于结构性分配来完善的,具有分配中"量变"的性质,从而使分配机制具有质变与量变的互动功能;最后,通过一个算例来验证所建模型的科学性、可行性.     

Concurrent optimal allocation of design and process tolerances for mechanical assemblies with interrelated dimension chains

Concurrent tolerance allocation has been the focus of extensive research, yet very few researchers have considered how to concurrently allocate design and process tolerances for mechanical assemblies with interrelated dimension chains. To address this question, this paper presents a new tolerance allocation method that applies the concept of concurrent engineering. The proposed method allocates the required functional assembly tolerances to the design and process tolerances by formulating the tolerance allocation problem into a comprehensive model and solving the model using a non-linear programming software package. A multivariate quality loss function of interrelated critical dimensions is first derived, each component design tolerance is formulated as the function of its related process tolerances according to the given process planning, both manufacturing cost and quality loss are further expressed as functions of process tolerances. And then, the objective function of the model, which is to minimize the sum of manufacturing cost and expected quality loss, is established and the constraints are formulated based on the assembly requirements and process constraints. The purpose of the model is to balance manufacturing cost and quality loss so that concurrent optimal allocation of design and process tolerances is realized and quality improvement and product cost reduction is achieved. The proposed method is tested on a practical example.     

A Method of Reliability Allocation of a Complicated Large System

Aiming at the problem of reliability allocation for a complicated large system, a new thought is brought up. Reliability allocation should be a kind of decision-making behavior; therefore the more information is used when apportioning a reliability index, the more reasonable an allocation is obtained. Reliability allocation for a complicated large system consists of two processes, the first one is a reliability information reporting process from bottom to top, and the other one is a reliability index apportioning process from top to bottom. By a typical example, we illustrate the concrete process of reliability allocation algorithms.     

A study on capacity allocation scheme with seasonal demand

This paper studies a game-theoretical capacity allocation problem in a two-echelon supply chain comprised of one supplier and N retailers. With demand fluctuating seasonally and significantly, supply is sufficient in low-demand periods but is insufficient in high-demand periods, especially when the supplier’s capacity decreases in high-demand periods. Retailers compete for the supplier’s capacity in high-demand periods, but do not want to absorb the supplier’s redundant capacity in low-demand periods. A turn-and-earn allocation scheme is proposed to encourage retailers to increase their order quantity in low-demand periods. Under the turn-and-earn allocation scheme, in high-demand periods, the supplier is willing to offer a guaranteed portion of supply capacity for the primary retailer. The remaining capacity in high-demand periods is allocated based on orders retailers placed in low-demand periods. In response, the retailers will decide how much they should order in low-demand periods. Then, a competitive game based on Nash equilibrium among the supplier and her retailers is analysed. In order to solve the problem of unreasonable distribution of interest caused by competition, a contract is designed to make it possible for subsidy to be transferred from the supplier to the retailers. Usually, the supplier and her primary retailer can both be better off under turn-and-earn allocation compared with fixed allocation, and the system efficiency in Nash solution is close to it in optimal solution. A numerical study is also conducted to discuss the parties’ sensitivity to different demand level and guaranteed allocation portion of capacity.     

Using cognitive work analysis to describe the role of UAVs in military operations

Advances in uninhabited vehicle design have resulted in increased levels of autonomy – allowing command to be communicated at high levels of abstraction, rather than detailed control. The resultant change in interaction requirements has obvious implications for the redesign of current operator interfaces. Furthermore, it allows the organisation design to be reconsidered. A reduction in the requirement for human involvement could allow control to be passed from dedicated remote controllers to actors involved directly with local tasks. This offers a number of potential benefits. Those involved with the task directly may have a greater situational understanding, allowing them to make faster decisions that are more informed. Furthermore, dynamic function allocation would allow assets to be shared, with command passed to the actor best placed to make decisions. This article uses a systemic approach, cognitive work analysis (CWA), to explore the constraints within military air operations, and derive systemic information requirements. The understanding developed from the CWA of the military air operations domain constraints is used to postulate the implications of introducing unmanned air vehicles. The example illustrates how the constraint-based approach can form the basis for considering system change.     

A multi-criteria decision-making approach for capacity allocation problem in semiconductor fabrication

In today's highly competitive environment, good customer relationship management is essential for a company to survive and to acquire reasonable profit. A semiconductor enterprise has multi-site fabs, and follows basically a make-to-order production type. The allocation of capacity for manufacturing different types of products is important for the competitiveness and future development of the enterprise. To cope with this requirement, a multi-criteria decision-making approach is proposed in this article for more efficient evaluation and selection of capacity allocation plans in semiconductor fabs. Under this approach, fuzzy analytic network process (FANP) is incorporated with fuzzy Delphi method (FDM), constraint programming (CP) and benefits, opportunities, costs and risks (BOCR). FDM is used to achieve a consensus among the opinions of experts. CP is used to screen out a set of capacity allocation plans that should be further evaluated. FANP with BOCR is used to evaluate the various factors and the interrelationship among the factors under the BOCR merits and to determine the expected performance of the capacity allocation plans. The proposed approach can provide a ranking and also priorities of different capacity allocation plans, and the fab can select the most appropriate capacity allocation for production in the case that insufficient capacity existed.     

Assembly tolerance allocation using a coalitional game method

Assembly tolerance allocation in modern manufacturing industries is important because it directly affects product quality and manufacturing cost. Loose tolerances may cause quality deficiency while tight tolerances can increase the cost. It is significant to develop a reasonable tolerance allocation strategy for every assembly component combining the cost and quality demands. Traditionally, designers often adopt the single objective optimization with some kind of constraint or establish a comprehensive evaluation function combining several optimization objectives with different weights to solve the tolerance allocation problem. These approaches may not be desirable as it is difficult to adequately consider the interaction and conflict between the cost and quality demands. In this article, an assembly tolerance allocation method using coalitional game theory is proposed in an attempt to find a trade-off between the assembly cost and the assembly quality. First, the assembly tolerance allocation problem is formulated as a multi-objective optimization problem and the concept of the Pareto-optimal solution is introduced. Then, how the assembly tolerance allocation model is transformed into a coalitional game model is discussed, and a key technique of transforming the tolerance design variables into the game strategies is presented. Further, the Shapley value method of coalitional game based on each player's contribution evaluation to the profit of the whole coalition is given. Finally, the feasibility of the procedure is demonstrated through an example of vehicle front structure assembly.     

Performance of serial assembly line designs under unequal operator speeds and learning

The traditional balanced assembly line designs can perform quite inefficiently under the presence of high labor turnover, low operator learning rates, and stochastic processing times. In these situations assembly line designs based on dynamic work allocation and work sharing principles have been shown to render a higher and less variable throughput. However, for situations where low labor turnover conditions and high operator learning rates exist, the traditional balanced lines still tend to be most productive. This paper has two objectives: to introduce a method based on work sharing principles, which we denominate Modified Work Sharing (MWS), and to develop some simple analytical tools that will allow us to compare the performance of this method with the traditional and other dynamic work allocation line designs. These results suggest that the traditional method is the most affected by the introduction of new operators in the production lines and thus the most affected by variability in general. On the other hand, dynamic work allocation methods appear to better absorb the variability introduced by new operators by sharing the workload of new operators with the more experienced members of the line.     

Microstress estimate of stochastically heterogeneous structures by the functional perturbation method: A one dimensional example

A new functional perturbation method (FPM) for calculating the probabilistic response of stochastically heterogeneous, linear elastic structures is developed. The method is based on treating the governing differential operator as well as the unknown displacement function as a functional of material modulus field. By executing a functional perturbation around the homogeneous case, a set of successive differential equations is obtained and solved, from which the average and variance of any local parameter (displacements, stresses, strains) can be found. For a linear problem, the equations to be solved in each approximation order differ from the one for the homogeneous case by a pseudo external loading (right hand side) part only. Thus, only the Green function for the homogeneous case is needed for an analytical solution of the corresponding heterogeneous problem. A one dimensional stochastically heterogeneous rod embedded in a uniform shear resistant elastic medium is solved as an example. The statistical variance of displacements and stresses are found analytically, including the edge regions. Morphological (grain size) and material (modulus) effects on the stochastic response are demonstrated. The above results are essential for estimating the stochastic features of local stress concentrations, which are the source for many strength-related macro properties of materials. Extensive usage of generalized functions (Dirac operator and its derivatives) is needed for the analysis.     

Reasoning with multilevel flow models

Complex heterogeneous systems, such as power plants or petro-chemical process plants, nowadays contain complex automation for start-up and shut-down control and support systems for the operators. Often, however, the operator support and automation suffers from a lack of flexibility, and only functions for a number of well defined operating modes and pre-defined paths for the transition between these modes. This paper proposes an alternative and more flexible method for developing and describing intentional mode transitions, and for developing diagnostic systems, using Multilevel Flow Modeling (MFM). MFM models a system by expressing it in terms of its goals and in terms of elementary functions that describe the mass, energy and information flows in the system. This paper describes the use of MFM models as a basis for reasoning about the actions that are necessary to achieve the goals of a system or to obtain an intentional change in the system's mode. For this, data measured from the system must be used to update the state of the MFM model so that the state of the model reflects the state of the system. The outcome of the reasoning can be used as support for an operator or for automated control of complex systems. This paper defines the relevant states for goals and flow functions and presents a set of rules for determining these states on the basis of measurements from a process. The relations between goals and functions, and among functions themselves, are discussed. A mechanism is introduced to produce a change in the desired mode of a process, and expressed in rules to implement this change. The approach is explained at the hand of a simple example system. An MFM model of this example system is presented, and used to illustrate how measured variables can be used to calculate the states of the elements in the MFM model. At the hand of the same model the rules for inferring the states of goals and functions, and for determining the required actions will be illustrated.     

Determination of design alternatives and performance criteria for safety systems in a nuclear power plant via simulated annealing

This study presents an efficient methodology that derives design alternatives and performance criteria for safety functions/systems in commercial nuclear power plants. Determination of the design alternatives and intermediate-level performance criteria is posed as a reliability allocation problem. The reliability allocation is performed in a single step by means of the concept of two-tier noninferior solutions in the objective and risk spaces within the top-level probabilistic safety criteria (PSC). Two kinds of two-tier noninferior solutions are obtained: desirable design alternatives and intolerable intermediate-level PSC of safety functions/systems.The weighted Chebyshev norm (WCN) approach with an improved Metropolis algorithm in simulated annealing is used to find the two-tier noninferior solutions. This is very efficient in searching for the global minimum of the difficult multiobjective optimization problem (MOP) which results from strong nonlinearity of a probabilistic safety assessment (PSA) model and nonconvexity of the problem. The methodology developed in this study can be used as an efficient design tool for desirable safety function/system alternatives and for the determination of intermediate-level performance criteria.The methodology is applied to a realistic streamlined PSA model that is developed based on the PSA results of the Surry Unit 1 nuclear power plant. The methodology developed in this study is very efficient in providing the intolerable intermediate-level PSC and desirable design alternatives of safety functions/systems.     

A human factors methodology for safety assessment based on the DYLAM approach

This paper discusses how the problem of human factors can appropriately be included in a probabilistic safety study. During incidents, the effect of the plant dynamic evolution and the failures of components can lead to non-intuitive configuration of the system and, when the interaction of the operator and the control and protection system is accounted for, the overall scenario of the safety study becomes particularly complex. These problems demand the use of a dynamic reliability methodology technique and the development of an appropriate model of human behavior to be coupled to a taxonomy of erroneous actions. At JRC these two endeavors have been carried out by the DYLAM approach and by a number of modeling architectures of operator simulation, which have been coupled for the development of a dynamic methodology for human factors analysis. In the paper, a simple application case is shown by the study of the control of an Auxiliary Feedwater System during incidental conditions.