Fuzzy goal-programming model with an artificial immune system (AIS) approach for a machine tool selection and operation allocation problem in a flexible manufacturing system

Some of the important planning problems that need realistic modelling and a quicker solution, especially in automated manufacturing systems, have recently assumed greater significance. In real-life industrial applications, the existing models considering deterministic situations fail as the true language adopted by foremen and technicians are fuzzy in nature. Thus, to map the situation on the shop floor to arrive at a real-time solution of this kind of tactical planning problem, it is essential to adopt fuzzy-based multi-objective goals so as to express the target desired by the management of business enterprises. This paper presents a fuzzy goal-programming approach to model the machine tool selection and operation allocation problem of flexible manufacturing systems. The model is optimized using an approach based on artificial immune systems and the results of the computational experiments are reported.     

改进的人工免疫算法在图像配准参数优化中的应用

借鉴禁忌搜索的思想改进了人工免疫网络算法(aiNet),提出一种禁忌人工免疫网络算法(TS-aiNet).在算法中引入禁忌表,禁忌在网络迭代中亲和力不再增加的细胞,通过特赦准则赦免一些被禁忌的优良状态;增加记忆表,保存成熟的记忆细胞;重新定义高斯变异方式,保证多样化的搜索.利用Markov链分析了该算法的全局收敛性,通过对典型系统的仿真实验分析了该算法的性能,并与克隆选择算法和opt-aiNet算法进行了比较,最终将改进的算法运用到红外与可见光图像配准中,像素级配准精度可以达到0.5像素.实验结果表明,该算法在多模态搜索空间中具有更好的全局收敛性、稳定性和发现极值点能力,能够克服早熟现象,提高图像配准的速度和精度,是一种有效的全局优化方法.     

An artificial immune network for multiobjective optimization problems

Multiobjective optimization is an important problem of great complexity and evolutionary algorithms have been established as a dominant approach in the field. This article suggests a method for approximating the Pareto front of a given function based on artificial immune networks. The proposed method uses cloning and mutation on a population of antibodies to create local subsets of the Pareto front. Elements of these local fronts are combined, in a way that maximizes diversity, to form the complete Pareto front of the function. The method is tested on a number of well-known benchmark problems, as well as an engineering problem. Its performance is compared against state-of-the-art algorithms, yielding promising results.     

面向并行工程的CAD系统研究

本文在ＣＩＭＳ环境下，以非回转体零件特征造型的基础上，其非回转体零件－－箱体对对象，应用并行工程技术，开发基于特征的三产体制造型系统。在生成工程图纸的同时更有效地与后续工序ＣＡＰＰ、ＣＡＭ进行信息集成。提供了一个更高层次的二次开发系统。     

A complete immunoglobulin-based artificial immune system algorithm for two-stage assembly flowshop scheduling problem with part splitting and distinct due windows

This paper considers a two-stage assembly flowshop scheduling problem with distinct due windows to minimise the sum of weighted earliness and tardiness. There are several identical parallel machines which produce parts in the first stage. When the required parts are available, a single assembly machine can group these parts into products in the second stage. It is assumed that a part can be split into sub-parts which can be processed independently on the parallel machines in the first stage. Setup is also considered. A mathematical model is established to describe and define the proposed problem. A new decoding method is developed by extending an existing decoding method. Two novel operators, named part splitting (PS) and optimal idle time insertion (ITI), are incorporated into the decoding procedure for improving the quality of the solution. A rule named Priority of Earliness and Tardiness (PET) and a Complete Immunoglobulin-based Artificial Immune System (C-IAIS) algorithm are proposed for solving the problem. To evaluate PET and C-IAIS algorithm, several existing algorithms are used in the experiments. Computational results show that C-IAIS algorithm performs better than other algorithms for solving the proposed problem.     

A green productivity based process planning system for a machining process

In the metal cutting industry, manufacturers have strived to increase energy efficiency and to reduce environmental burdens through the use of dust collectors and waste disposers. It is more beneficial and efficient to apply the front-of-pipe technology that prevents the sources of pollutants and minimises energy use through the redesign of products and the change of process planning and machining operations. In particular, process planning for the environment, called eco-process planning, is central to increasing energy efficiency and reducing environmental burdens because process planning decisions greatly influence machining performance. At present, greenability, a term used to indicate environmental friendliness, has been little considered as a major concern in the process planning stage because process planning decisions have focused on improving productivity aspects that include speed, cost and quality. Thus, it is essential to develop an eco-process planning approach that enables the harmonisation and enhancement of greenability performance while improving productivity performance, termed green productivity (GP). This paper presents the development of a GP-based process planning algorithm that enables the derivation of process parameters for improving GP in machining operations. The core mechanism of the algorithm is the realisation of the process improvement cycle that measures GP performance by the collection of machining data, quantifies this performance by categorical representation and predicts the performance through prediction models. To show the feasibility and applicability of the proposed algorithm, we have conducted an experiment and implemented a prototype system for a turning machining process.     

More MILP models for integrated process planning and scheduling

The integration of process planning and scheduling is important for an efficient utilisation of manufacturing resources. In general, there are two types of models for this problem. Although some MILP models have been reported, most existing models belong to the first type and they cannot realise a true integration of process planning and scheduling. Especially, they are completely powerless to deal with the cases where jobs are expressed by network graphs because generating all the process plans from a network graph is difficult and inefficient. The network graph-specific models belong to the other type, and they have seldom been deliberated on. In this research, some novel MILP models for integrated process planning and scheduling in a job shop flexible manufacturing system are developed. By introducing some network graph-oriented constraints to accommodate different operation permutations, the proposed models are able to express and utilise flexibilities contained in network graphs, and hence have the power to solve network graph-based instances. The established models have been tested on typical test bed instances to verify their correctness. Computational results show that this research achieves the anticipant purpose: the proposed models are capable of solving network graph-based instances.     

VIS: An artificial immune network for multi-objective optimization

The aim of this work is to propose and validate a novel multi-objective optimization algorithm based on the emulation of the behaviour of the immune system. The rationale of this work is that the artificial immune system has, in its elementary structure, the main features required by other multi-objective evolutionary algorithms described in the literature, such as diversity preservation, memory, adaptivity, and elitism. The proposed approach is compared with three multi-objective evolutionary algorithms that are representative of the state of the art in multi-objective optimization. Algorithms are tested on six standard problems (both unconstrained and constrained) and comparisons are carried out using three different metrics. Results show that the proposed approach has very good performances and can become a valid alternative to standard algorithms for solving multi-objective optimization problems.     

被动免疫算法在模拟电路故障诊断中的应用研究

基于生物免疫系统的信息处理机理,介绍了被动免疫算法的实现过程,并将其应用到某装备电路板的故障诊断之中。该算法具有边检测边学习的动态调整功能,仿真和实验实例表明,该算法适合于电路板的故障诊断,有较高的故障诊断率。     

Minimising makespan in the two-stage assembly hybrid flow shop scheduling problem using artificial immune systems

Two-stage hybrid flow shop (HFS) scheduling problem followed by single assembly machine is addressed in this paper. To produce the final product, parts need to be processed on the HFS stages and thereafter, several parts are joined under the assembly operations based on the predefined Bill of Materials of the product. The aim of this research is to find the schedule which minimises completion time of the last product, i.e. makespan. For the considered problem, lower bound, heuristic algorithms and two metaheuristic techniques based on artificial immune system are developed. Computational results demonstrate that the proposed lower bound and heuristic algorithms outperform the existent lower bounds and heuristic algorithms.     

Hybridizing a genetic algorithm with an artificial immune system for global optimization

This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using several test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.     

A generative process planning system for parts produced by rapid prototyping

This paper presents a generative process planning system for parts produced by the rapid prototyping process (i.e. fused deposition modelling–FDM). The proposed process planning involves optimal selection of orientating the model with a proper support structure and then provides an intelligent slicing methodology, such as direct or adaptive, to minimise the built up time, keeping the geometry and cusp height errors in control. Pre- and post-slicing processes have been used to minimise the sliced data error. The Computer Aided Process Planning (CAPP) model has been arranged into five modules: orientation, support structure generation, slicing, path planning and Numerical Control (NC) program generation, and model build up. The CAPP model has been implemented in C language having a unique methodology consisting of 42 simplified steps. The CAPP model has been tested for several examples and shows satisfactory results.     

Enterprise resource planning (ERP) operations support system for maintaining process integration

A successfully adopted and implemented enterprise resource planning (ERP) system does not automatically guarantee full benefits. It is important that ERP is operated as planned and thus provides the real-time information with a desired level of process integration. Any ERP system pushes a company toward full process integration and solves the fragmentation of information. However, the tight process integration involves operational issues that must be carefully managed. Thus, a conjecture can be made that a centrally coordinated support system is required to assist ERP users and administrators find problems, perform tedious validation and verification, and maintain the process integration of ERP with great consistency. This paper proposes an ERP operations support system (EOSS) that aims to achieve and maintain the process integration of ERP. EOSS monitors the operational status of process integration, prevents anomalies as early as possible and conducts repairs as soon as possible.     

A CBFSA approach to resolve the distributed manufacturing process planning problem in a supply chain environment

In this competitive world cost and lead time reduction are of prime concern for manufacturing firms. To achieve this objective manufacturing entities are adopting several management philosophies such as Total Quality Management (TQM), just-in-time (JIT), and theory of constraints (TOC). The present paper addresses the advanced computer-aided process planning (ACAPP) problem in a distributed manufacturing supply chain environment and aims at cost and lead times reduction under several technological constraints. To deal with the complexity of the problem the constraint based fast simulated annealing (CBFSA) algorithm has been explored in this article. Extensive computations have been performed over the well-known benchmarks of advanced planning problems and the results obtained prove the superiority of the proposed algorithm over the prior approaches.     

Integrated model for the batch sequencing problem in a multi-stage supply chain: an artificial immune system based approach

In this paper a mathematical model for the batch sequencing problem in a multistage supply chain is developed by taking into account three practically important objectives, viz. minimization of lead time, blocking time and due date violation. Attribute dependent operation time, sequence dependent setup time, different due dates, different lot sizes for batches and variable time losses due to interaction among several stages like waiting, idling, and blocking are also considered in the model. The problem is combinatorial in nature and complete enumeration of all its possibilities is computationally prohibitive. Therefore, a metaheuristic, artificial immune system (AIS) is employed to find an optimal/near optimal solution. In order to test the efficacy of AIS in solving the problem, its implementation on four different problems has been studied. Further, the comparative analysis of the results obtained by implementing AIS, genetic algorithm (GA) and simulated annealing (SA) on the proposed model reveals that AIS outperforms GA and SA in solving the underlying problem.     

A new method for guiding process flexibility investment: flexibility fit index

Flexibility enables manufacturing firms to respond efficiently to changes in the environment. Many firms make great efforts to increase their manufacturing flexibility to remain competitive in today's turbulent market. However, it is not true to say that the more flexible the better, because the cost of flexibility investment is high, and the capital for flexibility investment is limited. In this paper, we present a new method to guide process flexibility investment by developing a flexibility fit index. Taking demand changes into account, our method first defines a measure to quantify the requirement level of process flexibility. Then, a flexibility fit index is defined, which specifically identifies where flexibility is insufficient and where flexibility is surplus for a manufacturing system operating in a changing environment. The proposed fit index is objective and dimensionless, and so can be used more universally than previous subjective or non-dimensionless measures proposed in the literature. A set of simulation experiments shows that the proposed method can better guide flexibility investment by indicating the system structure which fits best with a given business situation, and other measures that ignore demand information may lead to over-investment or adding links that bring little benefit.     

Allocation of manufacturers through internet-based collaboration for distributed process planning

The pursuit of lower cost, shorter time-to-market, and better quality has led to a shift toward global production in today's competitive business environment. This shift however, forces manufacturing enterprises to have separate design houses and manufacturing facilities. In general, design houses are located in the same regions as customers to enable them to respond to the rapidly changing demands of customers. By contrast, manufacturing facilities can be placed in regions in which production costs are lower. However, this physical and logical separation between designers and manufacturers (or between upstream manufacturers and downstream manufacturers) raises various integration issues. The present paper addresses two of these issues: the framework for representing the data necessary to communicate requirements and objectives of the designer, and the methodology for utilizing such data to optimize the business objectives related to production cost and quality. The proposed representation, collaboration framework, and methodology will enable design houses and manufacturing facilities to realize the benefits of global production and to accommodate the management of loosely integrated supply chains.     

An intelligent process planning system for micro turn-mill parts

An attempt has been made to develop a generalised system for the generation of process plan for the manufacture of micro parts in this work. It has two components; first one deals with automatic part feature extraction from feature-based model and second one performs the execution of process planning activities in accordance with feature using knowledge-based system approach. The proposed system maps extensible markup language (XML) data for the feature based-model and produces the corresponding manufacturing activities needed for the manufacture of micro parts. The process plan modules considered in this work includes process sequence, tools and fixtures, process parameters selection and set-up plan generation. An attempt has been made to develop process parameters selection module based on experimental investigation and optimisation apart from manufacturing catalogues and user manuals. Feature extraction through XML files avoids complex feature extraction process. The application of the developed system has been verified with a case study. The present system is limited to micro turn-mill features. Incorporation of more micro features and consideration of other activities of process plan ensures a complete process planning system for micro-machining processes.     

Biogeography-based optimisation for flexible manufacturing system scheduling problem

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time.     

Manufacturing strategies for the ecosystem-based manufacturing system in the context of 3D printing

This paper aims to investigate the manufacturing strategies for the manufacturing systems in the context of 3D printing, referring to ecosystem-based manufacturing systems, rather than firm-based and network-based ones. A case study approach was adopted for this research, as the data was mainly collected via semi-structured interviews with staff members of companies in China. Besides the elements of strategic choices and manufacturing capabilities identified in the extant literature, this research verified three additional strategic choice elements (functional role, platform and solution) and identified two factors (platform openness and solution diversity) to classify an ecosystem-based manufacturing system. Meanwhile, four manufacturing capabilities of the ecosystem-based manufacturing system have been identified: collaborative manufacturing flexibility, rapid thriftiness ability, self-customisation and co-evolved design capability. The research results contribute to the area of manufacturing strategy via expanding its view from the firm and network levels to the ecosystem level. Meanwhile, the research results present operations managers with an understanding of the strategic choices and manufacturing capabilities of an ecosystem-based manufacturing system in the context of 3D printing.