A model for preventive maintenance planning by genetic algorithms based in cost and reliability

This work has two important goals. The first one is to present a novel methodology for preventive maintenance policy evaluation based upon a cost-reliability model, which allows the use of flexible intervals between maintenance interventions. Such innovative features represents an advantage over the traditional methodologies as it allows a continuous fitting of the schedules in order to better deal with the components failure rates. The second goal is to automatically optimize the preventive maintenance policies, considering the proposed methodology for systems evaluation.Due to the great amount of parameters to be analyzed and their strong and non-linear interdependencies, the search for the optimum combination of these parameters is a very hard task when dealing with optimizations schedules. For these reasons, genetic algorithms (GA) may be an appropriate optimization technique to be used. The GA will search for the optimum maintenance policy considering several relevant features such as: (i) the probability of needing a repair (corrective maintenance), (ii) the cost of such repair, (iii) typical outage times, (iv) preventive maintenance costs, (v) the impact of the maintenance in the systems reliability as a whole, (vi) probability of imperfect maintenance, etc. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR was used as a case study. The results obtained by this methodology outline its good performance, allowing specific analysis on the weighting factors of the objective function.     

Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results

This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power.     

Optimizing the Hot Isostatic Pressing Process

A genetic algorithm (GA) is used to optimize the hot isostatic pressing (HIPing) process for beryllium powder. The GA evaluates a HIPing model with different processing schedules in an effort to minimize temperature, pressure, processing time, ramp rates, grain growth, and distance to target relative density. It is shown that this is a constrained, multiobjective, noisy, optimization problem to which the GA is able to evolve a large number of viable solutions. However, for the GA to work in such a large multidimensional search space, it is suggested that the constraints be treated as objectives and then penalize the Pareto ranking for each constraint violated. This approach is necessary because a large-dimensional objective space naturally results in most members being Pareto rank 1.     

Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation

This paper describes a methodology based on genetic algorithms (GA) and experiments plan to optimize the availability and the cost of reparable parallel-series systems. It is a NP-hard problem of multi-objective combinatorial optimization, modeled with continuous and discrete variables. By using the weighting technique, the problem is transformed into a single-objective optimization problem whose constraints are then relaxed by the exterior penalty technique. We then propose a search of solution through GA, whose parameters are adjusted using experiments plan technique. A numerical example is used to assess the method.     

Analysis of surveillance test interval by Markov process for SDS1 in CANDU nuclear power plants

The Canadian Nuclear Safety Commission (CNSC) requires that each shutdown system (SDS) of CANDU plant should be available more than 99.9% of the reactor operating time and be tested periodically. The compliance with the availability requirement should be demonstrated using the component failure rate data and the benefits of the tests. There are many factors that should be considered in determining the surveillance test interval (STI) for the SDSs. These includes: the desired target availability, the actual unavailability, the probability of spurious trips, the test duration, and the side effects such as wear-out, human errors, and economic burdens. A Markov process model is developed to study the effect of test interval in the shutdown system number one (SDS1) in this paper. The model can provide the quantitative data required for selecting the STI. Representing the state transitions in the SDS1 by a time-homogeneous Markov process, the model can be used to quantify the effect of surveillance test durations and interval on the unavailability and the spurious trip probability. The model can also be used to analyze the variation of the core damage probability with respect to changes in the test interval once combined with the conditional core damage model derived from the event trees and the fault trees of probabilistic safety assessment (PSA) of the nuclear power plant (NPP).     

Basics of genetic algorithms optimization for RAMS applications

This paper discusses the use of genetic algorithms (GA) within the area of reliability, availability, maintainability and safety (RAMS) optimization. First, the multi-objective optimization problem is formulated in general terms and two alternative approaches to its solution are illustrated. Then, the theory behind the operation of GA is presented. The steps of the algorithm are sketched to some details for both the traditional breeding procedure as well as for more sophisticated breeding procedures. The necessity of affine transforming the fitness function, object of the optimization, is discussed in detail, together with the transformation itself. In addition, how to handle constraints by the penalization approach is illustrated. Finally, specific metrics for measuring the performance of a genetic algorithm are introduced.     

Modelling and optimization of proof testing policies for safety instrumented systems

This paper introduces a new development for modelling the time-dependent probability of failure on demand of parallel architectures, and illustrates its application to multi-objective optimization of proof testing policies for safety instrumented systems. The model is based on the mean test cycle, which includes the different evaluation intervals that a module goes periodically through its time in service: test, repair and time between tests. The model is aimed at evaluating explicitly the effects of different test frequencies and strategies (i.e. simultaneous, sequential and staggered). It includes quantification of both detected and undetected failures, and puts special emphasis on the quantification of the contribution of the common cause failure to the system probability of failure on demand as an additional component. Subsequently, the paper presents the multi-objective optimization of proof testing policies with genetic algorithms, using this model for quantification of average probability of failure on demand as one of the objectives. The other two objectives are the system spurious trip rate and lifecycle cost. This permits balancing of the most important aspects of safety system implementation. The approach addresses the requirements of the standard IEC 61508. The overall methodology is illustrated through a practical application case of a protective system against high temperature and pressure of a chemical reactor.     

A combined approach to solve the redundancy optimization problem for multi-state systems under repair policies

This paper combines universal moment generating function technique with stochastic Petri nets to solve the redundancy optimization problem for multi-state systems under repair policies. Redundant elements are included in order to achieve a desirable level of production availability. The elements of the system are characterized by their cost, performance and availability. These elements are chosen from a list of products available on the market. The number of repair teams is less than the number of reparable elements, and a repair policy specifies the maintenance priorities between the system elements. A heuristic is proposed to determine the minimal cost system configuration under availability constraints. This heuristic, first applies universal moment generating function technique to evaluate the system availability, assuming unlimited maintenance resources. Once a preliminary solution is found by the optimization algorithm, stochastic Petri nets are used to model different repair policies, and to find the best system configuration (architecture and number of repairmen) in terms of global performance (availability and cost). This combined procedure is applied to a reference example.     

Joint optimization of preventive maintenance and flexible flowshop sequence-dependent group scheduling considering multiple setups

This study focuses on a joint optimization problem regarding preventive maintenance (PM) and non-permutation group scheduling for a flexible flowshop manufacturing cell in order to minimize makespan. A mixed-integer linear programming model for the investigated problem is developed, which features the consideration of multiple setups, the relaxation of group technology assumptions, and the integration of group scheduling and PM. Based on the model, a lower bounding technique is presented to evaluate the quality of solutions. Furthermore, a genetic algorithm (GA) is proposed to improve computational efficiency. In the GA, a threshold-oriented PM policy, a hybrid crossover and a group swap mutation operator are applied. Numerical experiments are conducted on 45 test problems with various scales. The results show that the proposed model can remarkably reduce makespan. Comparative experiments reveal that the GA outperforms CPLEX, particle swarm optimization and cuckoo search with respect to effectiveness and efficiency.     

基于鱼骨型仓库布局的多车拣选路径问题优化

为了推动鱼骨型仓库在实际场景下的应用,针对鱼骨型仓库布局下的拣货路径优化问题,构建待拣货点距离计算模型和以有载重、容积限制的多车拣货距离最短为总目标的拣选路径优化模型。考虑遗传算法(GA)全局搜索能力强、粒子群算法(GAPSO)收敛速度快以及蚁群算法(ACO)较强的局部寻优能力,提出一种解决拣选路径优化模型的混合算法(GA-PSO-ACO)。通过不同订单规模的仿真实验,得出该混合算法在适应度值、迭代次数、收敛速度等方面均优于GA算法和GAPSO算法,且在订单规模较大时,平均适应度值约降低8%,有效缩短了总拣选距离,验证了混合算法在解决鱼骨型仓库布局下的拣货路径问题的先进性和有效性,为解决此类仓库内部的拣货路径问题提供新的解决方法和思路。     

Reliability based design optimization using response surfaces in application to multidisciplinary systems

Traditionally, reliability based design optimization (RBDO) is formulated as a nested optimization problem. For these problems the objective is to minimize a cost function while satisfying the reliability constraints. The reliability constraints are usually formulated as constraints on the probability of failure corresponding to each of the failure modes or a single constraint on the system probability of failure. The probability of failure is usually estimated by performing a reliability analysis. The difficulty in evaluating reliability constraints comes from the fact that modern reliability analysis methods are themselves formulated as an optimization problem. Solving such nested optimization problems is extremely expensive for large scale multidisciplinary systems which are likewise computationally intensive. In this research, a framework for performing reliability based multidisciplinary design optimization using approximations is developed. Response surface approximations (RSA) of the limit state functions are used to estimate the probability of failure. An outer loop is incorporated to ensure that the approximate RBDO converges to the actual most probable point of failure. The framework is compared with the exact RBDO procedure. In the proposed methodology, RSAs are employed to significantly reduce the computational expense associated with traditional RBDO. The proposed approach is implemented in application to multidisciplinary test problems, and the computational savings and benefits are discussed.     

A guided search genetic algorithm using mined rules for optimal affective product design

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.     

Tabu search for the redundancy allocation problem of homogenous series–parallel multi-state systems

This paper develops an efficient tabu search (TS) heuristic to solve the redundancy allocation problem for multi-state series–parallel systems. The system has a range of performance levels from perfect functioning to complete failure. Identical redundant elements are included in order to achieve a desirable level of availability. The elements of the system are characterized by their cost, performance and availability. These elements are chosen from a list of products available in the market. System availability is defined as the ability to satisfy consumer demand, which is represented as a piecewise cumulative load curve. A universal generating function technique is applied to evaluate system availability. The proposed TS heuristic determines the minimal cost system configuration under availability constraints. An originality of our approach is that it proceeds by dividing the search space into a set of disjoint subsets, and then by applying TS to each subset. The design problem, solved in this study, has been previously analyzed using genetic algorithms (GAs). Numerical results for the test problems from previous research are reported, and larger test problems are randomly generated. Comparisons show that the proposed TS out-performs GA solutions, in terms of both the solution quality and the execution time.     

基于GA优化的7055铝合金Johnson–Cook流变本构建模及其FEA应用

目的 研究7055铝合金高温流变行为,建立高精度流变本构模型和有限元分析（Finite Element Analysis,FEA）仿真模型。方法 基于Gholamzadeh温度修正模型和Evans摩擦修正模型,计算修正7055铝合金热压缩流动应力,以排除试验过程中变形温升和摩擦对流动应力的影响;针对修正后的流动应力构建Johnson–Cook本构模型,依托MATLAB编程采用遍历法优化模型参考条件,并引入遗传算法（Genetic Algorithm,GA）对模型参数进行优化,通过流动应力子程序二次开发实现优化后的模型在商用软件DEFORM中的应用,以预测变形工件的应力应变分布与成形载荷。结果 经温度和摩擦修正的流动应力与试验值相近;采用遍历法优选参考条件后的Johnson–Cook本构模型流动应力预测值与试验值之间的平均相对误差绝对值（Average Absolute Relative Error,AARE）为4.57%,经GA优化后降至3.50%,实现了精度的提升。基于该模型二次开发的DEFORM模拟平台能准确预测成形载荷,预测值与试验值之间的AARE为2.42%。结论构建了具有较高...     

Deciphering early-warning signals of SARS-CoV-2 elimination and resurgence from limited data at multiple scales

Inferring the transmission potential of an infectious disease during low-incidence periods following epidemic waves is crucial for preparedness. In such periods, scarce data may hinder existing inference methods, blurring early-warning signals essential for discriminating between the likelihoods of resurgence versus elimination. Advanced insight into whether elevating caseloads (requiring swift community-wide interventions) or local elimination (allowing controls to be relaxed or refocussed on case-importation) might occur can separate decisive from ineffective policy. By generalizing and fusing recent approaches, we propose a novel early-warning framework that maximizes the information extracted from low-incidence data to robustly infer the chances of sustained local transmission or elimination in real time, at any scale of investigation (assuming sufficiently good surveillance). Applying this framework, we decipher hidden disease-transmission signals in prolonged low-incidence COVID-19 data from New Zealand, Hong Kong and Victoria, Australia. We uncover how timely interventions associate with averting resurgent waves, support official elimination declarations and evidence the effectiveness of the rapid, adaptive COVID-19 responses employed in these regions.     

Mixed Self-adapting GA Optimal Scheduling Algorithm for a Multiple Resource Job-shop

With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.     

Constraint handling in genetic algorithm integrated structural optimization

Summary This paper is concerned with constraint handling techniques in GA integrated structural optimization. A modified version of Joines and Houck's [18] penalty function method is introduced and studied with respect to discrete optimization of truss structures. Three test cases are designed and numerically examined to reflect three distinct situations of loading and constraints. Certain recommendations are reached for penalty parameter values. Furthermore, the considered penalty function is reformulated to eliminate its shortcomings. The reformulated penalty function is also applied to the three test cases and the results are compared and fully discussed.     

具有几何约束的非等面积界面布局优化方法

目的 由于计算的难度与精度问题,在求解布局的过程中通常不考虑待布局元素面积的大小和形状的几何约束问题,导致求解的布局结果是理论上的,无法达到真正实用的目的。为提高界面布局优化方法生成布局方案的可用性,提出了一种集成启发式算法、多属性决策(MAMD)和整数线性规划(ILP)的方法。方法 首先,在获取到待布局元素间相关性、待布局元素的使用频率和面积等数据情况下,使用遗传算法(GA)通过改变待布局元素几何约束的参数生成一组备选布局方案;其次,将交互成本、GA寻优所花费的时间、有效性、效率和满意度作为评价界面布局的五个指标,根据TOPSIS、AHP和数据包络分析(DEA)等多属性决策方法对所有备选布局方案进行排序;最后,使用ILP方法获取一致性排序。结果 获得一个有效、具有高可用性的布局方案。结论 根据实验结果可知,该方法寻到的最优布局方案比原始方案有效地降低了交互成本和算法寻优的时间,提高了布局方案的可用性。     

Minimising the makespan in the two-machine job shop problem under availability constraints

Classical scheduling problem assumes that machines are available during the scheduling horizon. This assumption may be justified in some situations but it does not apply if maintenance requirements, machine breakdowns or other availability constraints have to be considered. In this paper, we treat a two-machine job shop scheduling problem with one availability constraint on each machine to minimise the maximum completion time (makespan). The unavailability periods are known in advance and the processing of an operation cannot be interrupted by an unavailability period (non-preemptive case). We present in our approach properties dealing with permutation dominance and the optimality of Jackson's rule under availability constraints. In order to evaluate the effectiveness of the proposed approach, we develop two mixed integer linear programming models and two schemes for a branch and bound method to solve the tackled problem. Computational results validate the proposed approach and prove the efficiency of the developed methods.