Multi-objective maintenance strategy for in-service corroding pipelines using genetic algorithms

Abstract

The multi-objective optimisation technique utilising genetic algorithms is employed to develop the optimal maintenance strategy for corroding oil and gas pipelines. The objective functions of the optimisation are the maximum annual conditional probabilities of small leak and burst, respectively, of all the pipe joints included in the pipeline segment over a predefined time horizon, and the total present-value cost of corrosion repair. The allowable annual probabilities of small leak and burst, and the annual repair budget are treated as constraints in the optimisation. The proposed optimal maintenance strategy is illustrated using a natural gas pipeline segment consisting of 90 corroding pipe joints. The analysis results indicate that a diverse set of solutions are included in the obtained Pareto front, which allow the decision-maker to select the maintenance plan achieving the desired tradeoff between the reliability and cost. The approach presented in this study can be incorporated in the practical optimal maintenance planning of corroding pipelines subjected to safety and resource constraints.     

Towards a better understanding of the epoxy-polymerization process using multi-objective evolutionary computation

The epoxy-polymerization process can be better understood by investigating the underlying optimization problem involving a number of conflicting objectives and more than 20 decision parameters. A combination of minimization or maximization of objectives, such as the number average molecular weight, polydispersity index and reaction time, are considered in this paper. The first two objectives are related to the properties of a polymer, whereas the third objective is related to productivity of the polymerization process. The decision variables are addition quantities of various reactants, e.g. the amount of addition for bisphenol-A (a monomer), sodium hydroxide and epichlorohydrin at different time steps (modeled in a semi-batch operation), whereas the satisfaction of all species balance equations is treated as constraints. A multi-objective evolutionary algorithm (the elitist non-dominated sorting genetic algorithm or NSGA-II) is used to obtain a set of non-dominated solutions in a single simulation run. The results show a substantial improvement (with about 300% more productivity) over the benchmark condition (reported by performing a one-time addition of reactants in the beginning in a batch process). Importantly, this study brings out a salient aspect of using an evolutionary approach to multi-objective problem solving. The availability of multiple optimal trade-off solutions allows a process engineer to have salient information about the polymerization process. Changes in the distribution of various polymer species in the course of polymerization process as observed among various Pareto-optimal solutions are identified and explained for this purpose. Such information provide important information about optimal operating conditions corresponding to different trade-offs among objectives, which are otherwise difficult to obtain. The systematic approach of starting from the two-objective problems to capture the essential features of interesting optimal operating conditions to finally solving the three-objective problem associated with the epoxy-polymerization problem in discovering the optimal trade-off interactions should motivate further such studies on other chemical process optimization problems. Overall, this paper demonstrates how fundamental optimization principles can be used systematically and reliably to find optimum operating conditions for complex chemical process operations.     

物流配送路径多目标优化的聚类-改进遗传算法

探讨运输车辆路线安排调度问题的解决方法，提出一种先用优先级综合聚类分析法将客户分类，再用带有控制开关系统的改进遗传算法求解多目标VRP的优化方法。构造了一种随机开关，以此控制遗传算法中的变异运算，增加了群体的多样性，避免了遗传算法中“局部最优现象”的发生。计算机仿真实验证明了该算法的有效性。     

一种实用的多运动目标标记与计数新方法

针对多运动目标的识别与计数.首先提出了一种改进的像素标记法,即对二值图像进行逐点扫描,将同一目标的像素赋于相同的标号,形成相应的标签图像,并初步统计出运动目标的数量;通过标记后,进一步按照运动目标的标号,提取每一个运动目标的面积、高宽比、质心特征量,并通过阈值判定,将特征量不在阈值范围内的伪目标清除,最终实现真实运动目标数量统计.实验证明该方法稳定性强、准确率较高,针对轮廓复杂的目标也有良好的效果.     

MobiWay应用中基于Hadoop的多目标多任务调度算法

针对Hadoop应用环境复杂、网络带宽等因素多变而影响调度算法性能的问题,提出适用于多任务多目标的Hadoop调度算法(MOSMT)。在分析已有调度算法工作原理的基础上,构建MOSMT算法的数学模型和调度策略;在负载模拟器中进行算法评估,并将MOSMT算法应用于MobiWay;对MobiWay应用中的MOSMT算法性能进行测试分析,以最少的资源和最低的时间成本完成任务的调度。通过与FIFO和Fair调度算法对比表明,该算法能够实现类似的功能,并且在处理多任务多目标时耗时更少,更为经济。     

Local arc consistency for non-invertible semirings, with an application to multi-objective optimization

We extend algorithms for local arc consistency proposed in the literature in order to deal with (absorptive) semirings that may not be invertible. As a consequence, these consistency algorithms can be used as a pre-processing procedure in soft Constraint Satisfaction Problems (CSPs) defined over a larger class of semirings, such as those obtained from the Cartesian product of two (or more) semirings. One important instance of this class of semirings is adopted for multi-objective CSPs. First, we show how a semiring can be transformed into a novel one where the + operator is instantiated with the least common divisor (LCD) between the elements of the original semiring. The LCD value corresponds to the amount we can “safely move” from the binary constraint to the unary one in the arc consistency algorithm. We then propose a local arc consistency algorithm which takes advantage of this LCD operator.     

The efficient and economic design of PEM fuel cell systems by multi-objective optimization

Since the efficiency of fuel cells is the ratio of the electrical power output and the fuel input, it is a function of power density, system pressure, and stoichiometric ratios of hydrogen and oxygen. Typically, the fuel cell efficiency decreases as its power output increases. In order for the fuel cell system to obtain highly efficient operation with the same power generation, more cells and other auxiliaries such as a high-capacity compressor system, etc. are required. In other words, fuel cell efficiency is closely related to fuel cell economics. Therefore, an optimum efficiency should exist and should result in the definition of a cost-effective fuel cell system. Using a multi-objective optimization technique, the sequential quadratic programming (SQP) method, the efficiency and cost of a fuel cell system have been optimized under various operating conditions. This paper has obtained some analytical results that provide a useful suggestion for the design of a cost-effective fuel cell system with high operation efficiency.     

A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs

In this paper the problem of permutation flow shop scheduling with the objectives of minimizing the makespan and total flow time of jobs is considered. A Pareto-ranking based multi-objective genetic algorithm, called a Pareto genetic algorithm (GA) with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed. The proposed algorithm makes use of the principle of non-dominated sorting, coupled with the use of a metric for crowding distance being used as a secondary criterion. This approach is intended to alleviate the problem of genetic drift in GA methodology. In addition, the proposed genetic algorithm maintains an archive of non-dominated solutions that are being updated and improved through the implementation of local search techniques at the end of every generation. A relative evaluation of the proposed genetic algorithm and the existing best multi-objective algorithms for flow shop scheduling is carried by considering the benchmark flow shop scheduling problems. The non-dominated sets obtained from each of the existing algorithms and the proposed PGA-ALS algorithm are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by the proposed PGA-ALS.     

A multi-criteria optimisation approach for the design of sustainable utility systems

Large amounts of gaseous emissions are generated by combustion processes associated with the utility systems. The emissions include SO_x, CO₂, CO, NO_x, CH₄, and N₂O. Such emissions can result in significant impact on the surrounding environment. As a result of serious concerns about environmental problems in recent years, the design criteria for a modern utility system should include both environmental and economic requirements. This work proposes a multi-objective optimisation (MOO) strategy to identify the sustainable design of utility systems that satisfies both economic and environmental goals. A MOO mixed integer linear programming (MILP) model is developed to combine the minimisation of costs with the minimisation of environmental impact that is assessed in terms of life cycle environmental burdens. Most of the gaseous emissions are addressed in the model. The resulting MOO problem is solved using lexicographic goal programming (LGP) techniques. The new strategy has been applied to a case study for the design of a utility system with specific utility demands.     

Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids

Evolutionary algorithms have been successfully applied to various multi-objective optimization problems. However, theoretical studies on multi-objective evolutionary algorithms, especially with self-adaption, are relatively scarce. This paper analyzes the convergence properties of a self-adaptive (μ+1)-algorithm. The convergence of the algorithm is defined, and general convergence conditions are studied. Under these conditions, it is proven that the proposed self-adaptive (μ+1)-algorithm converges in probability or almost surely to the Pareto-optimal front.