Optimal generator maintenance scheduling using a modified discrete PSO

A modified discrete particle swarm optimisation (MDPSO) algorithm to generate optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system, while satisfying system load demand and crew constraints, is presented. Discrete particle swarm optimisation (DPSO) is known to effectively solve large-scale multi-objective optimisation problems and has been widely applied in power system. The MDPSO proposed for the generator maintenance scheduling optimisation problem generates optimal and feasible solutions and overcomes the limitations of the conventional methods, such as extensive computational effort, which increases exponentially as the size of the problem increases. The efficacy of the proposed algorithm is illustrated and compared with the genetic algorithm (GA) and DPSO in two case studies ? a 21-unit test system and a 49-unit system feeding the Nigerian national grid. The MDPSO algorithm is found to generate schedules with comparatively higher system reliability indices than those obtained with GA and DPSO.     

Multiprocessor task scheduling problem using hybrid discrete particle swarm optimization

Task Scheduling is a complex combinatorial optimization problem and known to be an NP hard. It is an important challenging issue in multiprocessor computing systems. Discrete Particle Swarm Optimization (DPSO) is a newly developed swarm intelligence technique for solving discrete optimization problems efficiently. In DPSO, each particle should limit its communication with the previous best solution and the best solutions of its neighbors. This learning restriction may reduce the diversity of the algorithm and also the possibility of occurring premature convergence problem. In order to address these issues, the proposed work presents a hybrid version of DPSO which is a combination of DPSO and Cyber Swarm Algorithm (CSA). The efficiency of the proposed algorithm is evaluated based on a set of benchmark instances and the performance criteria such as makespan, mean flow time and reliability cost.     

A hybrid particle swarm based method for process planning optimisation

A process planning (PP) problem is defined as to determine a set of operation-methods (machine, tool, and set-up configuration) that can convert the given stock to the designed part. Essentially, the PP problem involves the simultaneous decision making of two tasks: operation-method selection and sequencing. This is a combinatorial optimisation problem and it is difficult to find the best solution in a reasonable amount of time. In this article, an optimisation approach based on particle swarm optimisation (PSO) is proposed to solve the PP problem. Due to the characteristic of discrete process planning solution space and the continuous nature of the original PSO, a novel solution representation scheme is introduced for the application of PSO in solving the PP problem. Moreover, two kinds of local search algorithms are incorporated and interweaved with PSO evolution to improve the best solution in each generation. The numerical experiments and analysis have demonstrated that the proposed algorithm is capable of gaining a good quality solution in an efficient way.     

A simulated annealing-based optimization algorithm for process planning

Computer-aided process planning (CAPP) in the past typically employed knowledge-based approaches, which are only capable of generating a feasible plan for a given part based on invariable machining resources. In the field of concurrent engineering, there is a great need for process planning optimization. This paper describes an approach that models the constraints of process planning problems in a concurrent manner. It is able to generate the entire solution space by considering multiple planning tasks, i.e. operations (machine, tool and tool approach direction), selection and operations sequencing simultaneously. Precedence relationships among all the operations required for a given part are used as the constraints for the solution space. The relationship between an actual sequence and the feasibility of applying an operation is also considered. An algorithm based on simulated annealing (SA) has been developed to search for the optimal solution. Several cost factors including machine cost, tool cost, machine change cost, tool change cost and set-up change cost can be used flexibly as the objective function. The case study shows that the algorithm can generate highly satisfying results.     

Particle swarm-based structural optimization of laminated composite hydrokinetic turbine blades

Composite blade manufacturing for hydrokinetic turbine application is quite complex and requires extensive optimization studies in terms of material selection, number of layers, stacking sequence, ply thickness and orientation. To avoid a repetitive trial-and-error method process, hydrokinetic turbine blade structural optimization using particle swarm optimization was proposed to perform detailed composite lay-up optimization. Layer numbers, ply thickness and ply orientations were optimized using standard particle swarm optimization to minimize the weight of the composite blade while satisfying failure evaluation. To address the discrete combinatorial optimization problem of blade stacking sequence, a novel permutation discrete particle swarm optimization model was also developed to maximize the out-of-plane load-carrying capability of the composite blade. A composite blade design with significant material saving and satisfactory performance was presented. The proposed methodology offers an alternative and efficient design solution to composite structural optimization which involves complex loading and multiple discrete and combinatorial design parameters.     

Comparing four metaheuristics for solving a constraint satisfaction problem for ship outfitting scheduling

This study compares the performance of four different metaheuristics for solving a constraint satisfaction scheduling problem of the outfitting process of shipbuilding. The ship outfitting process is often unorganised and chaotic due to the complex interactions between the stakeholders and the overall lack of sufficiently detailed planning. The examined methods are genetic algorithms (GA), simulated annealing (SA), genetic simulated annealing (GSA) and discrete particle swarm optimisation (PSO). Each of these methods relies on a list scheduling heuristic to transform the solution space into feasible schedules. Although the SA had the best performance for a medium-sized superstructure section, the GSA created the best schedules for engine room double-bottom sections, the most complex sections in terms of outfitting. The GA provided the best scalability in terms of computational time while only marginally sacrificing solution quality. The solution quality of the PSO was very poor in comparison with the other methods. All methods generated schedules with sufficiently high resource utilisation, approximately 95%. The findings from this work will be incorporated into a larger project with the aim of creating a tool which can automatically generate an outfitting planning for a vessel.     

Robotic disassembly sequence planning using enhanced discrete bees algorithm in remanufacturing

Increasing attention is being paid to remanufacturing due to environmental protection and resource saving. Disassembly, as an essential step of remanufacturing, is always manually finished which is time-consuming while robotic disassembly can improve disassembly efficiency. Before the execution of disassembly, generating optimal disassembly sequence plays a vital role in improving disassembly efficiency. In this paper, to minimise the total disassembly time, an enhanced discrete Bees algorithm (EDBA) is proposed to solve robotic disassembly sequence planning (RDSP) problem. Firstly, the modified feasible solution generation (MFSG) method is used to build the disassembly model. After that, the evaluation criterions for RDSP are proposed to describe the total disassembly time of a disassembly sequence. Then, with the help of mutation operator, EDBA is proposed to determine the optimal disassembly sequence of RDSP. Finally, case studies based on two gear pumps are used to verify the effectiveness of the proposed method. The performance of EDBA is analysed under different parameters and compared with existing optimisation algorithms used in disassembly sequence planning (DSP). The result shows the proposed method is more suitable for robotic disassembly than the traditional method and EDBA generates better quality of solutions compared with the other optimisation algorithms.     

子母穿梭车式立体仓库复合作业路径优化

目的研究子母穿梭车式立体仓库中复合作业路径优化问题,有利于提高系统运行效率,降低成本。方法对于子母穿梭车式立体仓库在一次存取货作业中复合作业方式的实际调度路径,考虑到其运动机构的加(减)速度,以完成复合作业总时间最短为目标建立数学模型。针对该系统复合作业的运行特征,提出一种结合遗传和蚁群算法各自优点的混合粒子群算法进行优化求解。结果实例验证可知,与粒子群算法和蚁群算法相比,文中提出的混合粒子群算法具有性能稳定、优化效率更高等优点。结论文中所提复合作业路径优化方法能够有效地缩短子母穿梭车式立体仓库的复合作业时间,提高了进出库调度效率。     

Global optimization of a feature-based process sequence using GA and ANN techniques

Operation sequencing has been a key area of research and development for computer-aided process planning (CAPP). An optimal process sequence could largely increase the efficiency and decrease the cost of production. Genetic algorithms (GAs) are a technique for seeking to ‘breed’ good solutions to complex problems by survival of the fittest. Some attempts using GAs have been made on operation sequencing optimization, but few systems have intended to provide a globally optimized fitness function definition. In addition, most of the systems have a lack of adaptability or have an inability to learn. This paper presents an optimization strategy for process sequencing based on multi-objective fitness: minimum manufacturing cost, shortest manufacturing time and best satisfaction of manufacturing sequence rules. A hybrid approach is proposed to incorporate a genetic algorithm, neural network and analytical hierarchical process (AHP) for process sequencing. After a brief study of the current research, relevant issues of process planning are described. A globally optimized fitness function is then defined including the evaluation of manufacturing rules using AHP, calculation of cost and time and determination of relative weights using neural network techniques. The proposed GA-based process sequencing, the implementation and test results are discussed. Finally, conclusions and future work are summarized.     

Seeding the initial population with feasible solutions in metaheuristic optimization of steel trusses

In spite of considerable research work on the development of efficient algorithms for discrete sizing optimization of steel truss structures, only a few studies have addressed non-algorithmic issues affecting the general performance of algorithms. For instance, an important question is whether starting the design optimization from a feasible solution is fruitful or not. This study is an attempt to investigate the effect of seeding the initial population with feasible solutions on the general performance of metaheuristic techniques. To this end, the sensitivity of recently proposed metaheuristic algorithms to the feasibility of initial candidate designs is evaluated through practical discrete sizing of real-size steel truss structures. The numerical experiments indicate that seeding the initial population with feasible solutions can improve the computational efficiency of metaheuristic structural optimization algorithms, especially in the early stages of the optimization. This paves the way for efficient metaheuristic optimization of large-scale structural systems.     

Expert CAPP system for single spindle automats

We report the design and implementation of an expert system (EXTURN) for the process planning of rotationally symmetric components manufactured on single spindle automats. EXTURN essentially comprises an interactive graphical feature modeller (GFM) and process planning modules for operation extraction, sequencing, tool selection, and process plan generation. The feature modeller provides utilities for interactive component synthesis, manufacturability assessment, dimensioning and tolerancing, graphical display, etc. The CAPP module synthesizes a process plan from the part model created by GFM, using rules organized in the knowledge base. EXTURN has been implemented for both the cut-off as well as the turret type of automats and has been tested for a variety of industrial components. It was found to generate consistent process sequences acceptable to industrial shop practices.     

Automated process planning for parametric parts

The available automated CAPP solutions are mostly academic research or specific applications, which cannot be used in different environments. This paper presents a solution for automated process planning for parametric parts included in one CAPP environment with many other software components. It meets the planning requirements of any parts and products in a real industrial environment. This paper provides a brief overview of CAPP and a definition of the parametric parts used. The requirements of the CAPP environment are discussed and its concept and design characteristics are presented. A practical application of the CAPP environment is described in order to validate the proposed solution.     

A meta-heuristic approach for solving the no-wait flow-shop problem

No-wait flow-shop scheduling problems refer to the set of problems in which a number of jobs are available for processing on a number of machines in a flow-shop context with the added constraint that there should be no waiting time between consecutive operations of the jobs. The problem is strongly NP-hard. In this paper, the considered performance measure is the makespan. In order to explore the feasible region of the problem, a hybrid algorithm of Tabu Search and Particle Swarm Optimisation (PSO) is proposed. In the proposed approach, PSO algorithm is used in order to move from one solution to a neighbourhood solution. We first employ a new coding and decoding technique to efficiently map the discrete feasible space to the set of integer numbers. The proposed PSO will further use this coding technique to explore the solution space and move from one solution to a neighbourhood solution. Afterwards, the algorithm decodes the solutions to its respective feasible solution in the discrete feasible space and returns the new solutions to the TS. The algorithm is tested by solving a large number of problems available in the literature. Computational results show that the proposed algorithm is able to outperform competitive methods and improves some of the best-known solutions of the considered test problems.     

Optimisation for multi-part flow-line configuration of reconfigurable manufacturing system using GA

To facilitate the configuration selection of reconfigurable manufacturing systems (RMS) at the beginning of every demand period, it needs to generate K (predefined number) best configurations as candidates. This paper presents a GA-based approach for optimising multi-part flow-line (MPFL) configurations of RMS for a part family. The parameters of the MPFL configuration comprise the number of workstations, the number of paralleling machines and machine type as well as assigned operation setups (OSs) for each workstation. Input requirements include an operation precedence graph for each part, relationships between operations and OSs as well as machine options for each OS. The objective is to minimise the capital cost of MPFL configurations. A 0-1 nonlinear programming model is developed to handle sharing machine utilisation over consecutive OSs for each part which is ignored in the existing approach. Then a novel GA-based approach is proposed to identify K economical solutions within a refined solution space comprising the optimal configurations associated with all feasible OS assignments. A case study shows that the best solution found by GA is better than the optimum obtained by the existing approach. The solution comparisons between the proposed GA and a particle swarm optimisation algorithm further illustrate the effectiveness and efficiency of the proposed GA approach.     

工期不确定的模具车间柔性Flow-shop调度问题

由于模具制造属于非重复性单件订货生产,模具加工的任务工期具有较强的不确定性,导致生产调度混乱。为制定合理可行的生产调度方案,建立了任务工期离散概率模型,以最大完工时间的期望值最小为目标,建立不确定工期柔性Flow-shop调度模型;在遗传算法交叉、变异等操作中融入模拟退火操作,将遗传算法的全局搜索能力与模拟退火算法的良好局部搜索能力相结合,设计了不确定工期的柔性Flow-shop调度问题混合遗传模拟退火算法。利用混合遗传模拟退火算法对调度模型进行求解,通过仿真实验表明,该研究对于解决工期不确定的模具车间柔性Flow-shop调度问题是行之有效的。     

A Hybrid Learning‐Based Model for Simultaneous Monitoring of Process Mean and Variance

Unnatural patterns exhibited on process mean and variance control charts can be associated separately with different assignable causes. Quick and accurate knowledge of the type of control chart patterns (CCPs), either because of process mean or variance, can greatly facilitate identification of assignable causes. Over the past few decades, however, process mean and variance CCPs are seldom studied simultaneously in the statistical process control literature. This study proposes a hybrid learning‐based model for simultaneous monitoring of process mean and variance CCPs. In this model, a self‐organization map neural network‐based quantization error control chart is responsible for detecting the out‐of‐control signals, a discrete particle swarm optimization‐based selective ensemble of back‐propagation networks is responsible for classifying the detected out‐of‐control signals into categories of mean and/or variance abnormality, and two discrete particle swarm optimization‐based selective ensembles of learning vector quantization networks are responsible for further identifying the detected mean and variance out‐of‐control signals as one of the specific CCP types, respectively. Extensive simulations indicate that the proposed hybrid learning‐based model outperforms other existing approaches in detecting mean and variance changes, while also capable of CCP recognition. In addition, a case study is conducted to demonstrate how the proposed hybrid learning‐based model can function as an effective tool for monitoring mean and variance simultaneously. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimization of machining datum selection and machining tolerance allocation with genetic algorithms

In machining process planning, selection of machining datum and allocation of machining tolerances are crucial as they directly affect the part quality and machining efficiency. This study explores the feasibility to build a mathematical model for computer aided process planning (CAPP) to find the optimal machining datum set and machining tolerances simultaneously for rotational parts. Tolerance chart and an efficient dimension chain tracing method are utilized to establish the relationship between machining datums and tolerances. A mixed-discrete nonlinear optimization model is formulated with the manufacturing cost as the objective function and blueprint tolerances and machine tool capabilities as constraints. A directed random search method, genetic algorithm (GA), is used to find optimum solutions. The computational results indicate that the proposed methodology is capable and robust in finding the optimal machining datum set and tolerances. The proposed model and solution procedure can be used as a building block for computer automated process planning.     

保证公差要求的装夹和加工顺序自动规划

提出了一个保证公差要求的装夹方案和特征加顺序自动规划方法。该方法根据机床的加工精度确定关键公差特征,以关键公差特征和唯一加工方法特征为基础,将零件的特征模型分解为相同装夹特征集合。该方法具有较快的规划与排序的速度,与设计系统紧密集成,并可在规划的过程中实现零件的整体可制造性评价功能。