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A modified immunoglobulin-based artificial immune system algorithm for solving the permutation flow shop scheduling problem

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2017年第7期34卷 542-550页

作者： Liu, Xufei Chung, Tsui-Ping Jilin Univ Sch Mech Sci & Engn Dept Ind Engn Changchun Jilin Peoples R China

To minimize makespan in the permutation flow shop scheduling problem, a modified immunoglobulin-based artificial immune system algorithm (M-IAIS) is developed to search for a job sequence. The basic structure of immunoglobulin-based artificial immune system algorithm consists of three parts, somatic recombination, hypermutation, and isotype switching. A special process, named B cell repertoire updating, is considered in M-IAIS algorithm to accelerate the convergence speed. Taillard's benchmark problems are chosen as test instances. Percentage deviation, hypothesis test, convergence performance, and robust analysis are considered in the paper to evaluate the performance of M-IAIS algorithm. Computational results show that M-IAIS algorithm is competitive for the permutation flow shop scheduling problem.

关键词： artificial immune system algorithm permutation flow shop scheduling makespan

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artificial immune system algorithm and Simulated Annealing algorithm for Scheduling Batches of Parts based on Job Availability Model in a Multi-Cell Flexible Manufacturing system 12

Artificial Immune System Algorithm and Simulated Annealing A...

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12th Global Congress on Manufacturing and Management (GCMM)

作者： Balaji, A. N. Porselvi, S. KLN Coll Engn Dept Mech Engn Pottapalayam 630611 Tamil Nadu India

The flow shop manufacturing cell scheduling problem with sequence dependent setup time is a topic of great concern for many industrial applications. This paper addresses batch scheduling problem in the Multi cell Flexible Manufacturing system (MCFMS) havingsequence dependent batch set up time with flow shop characteristics. The objective of this research problem is the minimization of makespan. A mathematical model for the research problem is developed in this paper. As the research problem is known to be NP-hard, artificial immune systems (AIS) algorithm and Simulated annealing algorithm (SA) algorithm are developed to solve the problem. Eighty test problems are solved in order to test the proposed algorithms. The quality of solution in terms of makespan obtained by AIS algorithm is found superior to that of the SA algorithm. But in computational point of view, SA algorithm takes shorter time than AIS algorithm to find the near optimal batch sequence. Since the operation parameters of meta-heuristic algorithms normally affect the quality of the solution, Sensitive Analysis is carried out for the test problems by varying the percentage of receptor editing in AIS algorithm. (C) 2014 The Authors. Published by Elsevier Ltd.

关键词： Flow shop scheduling Flexible Manufacturing system Sequence dependent setup time Makespan artificial immune system algorithm Simulated annealing algorithm

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Simultaneous scheduling of machines and tools in multimachine flexible manufacturing systems using artificial immune system algorithm

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2014年第5期27卷 401-414页

作者： Raj, J. Aldrin Ravindran, D. Saravanan, M. Prabaharan, T. Govt Coll Engn Dept Mech Engn Tirunelveli Tamil Nadu India Natl Engn Coll Dept Mech Engn Kovilpatti Tamil Nadu India SBM Coll Engn & Technol Dept Mech Engn Dindigul Tamil Nadu India Mepco Schlenk Engn Coll Dept Mech Engn Virudunagar Tamil Nadu India

This article deals with simultaneous scheduling of machines and tools in a multimachine flexible manufacturing system to generate best optimal sequences that minimise makespan. As flexible manufacturing system (FMS) is an integrated manufacturing facility, simultaneous scheduling of different components of FMS is essential. In this research work, attempts have been made to integrate machines and tools. The aim of this article is to address combined machine and tool scheduling in a FMS consisting of machines and a central tool magazine. Four heuristics, priority dispatching rules, modified nondelay schedule generation algorithm with six different priority dispatching rules, modified Giffler and Thompson algorithm and artificial immune system (AIS) algorithm, have been proposed for solving combined machine and tool problems with makespan as objective. The proposed heuristics are numerically tested on various problems and the results are compared. The result shows that AIS algorithm yields better results for simultaneous scheduling of machines and tools.

关键词： artificial immune system algorithm flexible manufacturing systems Giffler and Thompson algorithm nondelay schedule generation algorithm priority dispatching rules

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artificial immune system algorithm and Simulated Annealing algorithm for Scheduling Batches of Parts based on Job Availability Model in a Multi-cell Flexible Manufacturing system

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Procedia Engineering 2014年 97卷 1524-1533页

作者： A.N. Balaji S. Porselvi Professor Department of Mechanical Engineering K.L.N.College of Engineering Pottapalayam Tamilnadu 630 611 India Assistant Professor Department of Mechanical Engineering K.L.N.College of Engineering Pottapalayam Tamilnadu 630 611 India

关键词： Flow shop scheduling Flexible Manufacturing system Sequence dependent setup time Makespan artificial immune system algorithm Simulated annealing algorithm

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Optimization of renewable energy project portfolio selection using hybrid AIS-AFS algorithm in an international case study

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Goli, Alireza Univ Isfahan Fac Engn Dept Ind Engn & Future Studies Esfahan *** Iran

In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-objective mathematical model for identifying the optimal Renewable Energy Project Portfolio (REPP), aiming to maximize net present value while minimizing investment risk. The key innovation of this model is its incorporation of project lifetime and workforce employment considerations to discern the best REPP. To optimize the objective functions of this mathematical model, a hybrid meta-heuristic algorithm combining artificial immune system (AIS) and artificial Fish Swarm (AFS) algorithms is introduced. Genuine data from a varied spectrum of renewable energy projects spanning 20 countries has been meticulously collected. The proposed model is optimized using this dataset, considering portfolio sizes of 3, 5, 10, and 15. The numerical results indicate that, at a specific investment risk threshold, the proposed hybrid algorithm outperforms both AIS and AFS in terms of profitability. Furthermore, the assessment of the geographical distribution of selected projects reveals a deliberate effort to avoid concentration in any specific region, demonstrating a commitment to identifying optimal investment opportunities globally. This research advances the understanding of renewable energy project portfolio optimization, providing valuable insights for investors, policymakers, and sustainable development practitioners.

关键词： Renewable energy project portfolio Profitability Investment risk artificial immune system algorithm Fish Swarm algorithm

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Comparison of Firefly algorithm and artificial immune system algorithm for lot streaming in m-machine flow shop scheduling

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE systemS 2012年第6期5卷 1184-1199页

作者： Chakaravarthy, G. Vijay Marimuthu, S. Sait, A. Naveen Fatima Michael Coll Engn & Technol Dept Mech Engn Madurai 625020 Tamil Nadu India Latha Mathavan Engn Coll Dept Mech Engn Madurai 625301 Tamil Nadu India Chendhuran Coll Engn & Technol Dept Mech Engn Pudukkottai 622507 India

Lot streaming is a technique used to split the processing of lots into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production time (makespan). The objective of this paper is to minimize the makespan and total flow time of n-job, m-machine lot streaming problem in a flow shop with equal and variable size sublots and also to determine the optimal sublot size. In recent times researchers are concentrating and applying intelligent heuristics to solve flow shop problems with lot streaming. In this research, Firefly algorithm (FA) and artificial immune system (AIS) algorithms are used to solve the problem. The results obtained by the proposed algorithms are also compared with the performance of other worked out traditional heuristics. The computational results shows that the identified algorithms are more efficient, effective and better than the algorithms already tested for this problem.

关键词： Flow shop Lot streaming Scheduling Firefly algorithm artificial immune system algorithm

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Comprehensive approach for distribution system planning with uncertainties

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第24期13卷 5467-5477页

作者： Seta, Felipe da Silva de Oliveira, Leonardo W. de Oliveira, Edimar J. Fed Ctr Technol Educ Dept Elect Engn Angra Dos Reis RJ Brazil Fed Univ Juiz Fora UFJF Dept Elect Engn Juiz De Fora MG Brazil

This study presents a comprehensive approach for the distribution system expansion planning (DSEP) that considers investment, operation, carbon dioxide emission and reliability costs, as well as uncertainties over load demand and wind-based distributed generation. A restoration strategy is taken into account for obtaining the energy not supplied under the 'N - 1' criterion and the corresponding reliability cost over a planning horizon. The problem is modelled as mixed-integer non-linear programming by using the artificial immune system algorithm. Also, two methods to represent uncertainties are applied and compared: an interval technique through an interval power flow and a scenario-based approach. Network constraints are considered, as the limits of current, voltage and power from substations, as well as the obtaining of radial and connected topology. The novelty of the proposed interval DSEP consists of handling the uncertainties over operation in an efficient manner through a single step, instead of the several deterministic evaluations of the scenario-based approach. Numerical results are presented for well known test systems, which show the potentials of the proposed approach.

关键词： power distribution planning distributed power generation investment load flow artificial immune systems power distribution economics nonlinear programming power distribution faults integer programming power distribution reliability mixed-integer nonlinear programming artificial immune system algorithm interval technique interval power flow scenario-based approach interval DSEP known test systems distribution system expansion planning carbon dioxide emission reliability costs uncertainties overload demand wind-based distributed generation restoration strategy planning horizon

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artificial immune system based on adaptive clonal selection for feature selection and parameters optimisation of support vector machines

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CONNECTION SCIENCE 2016年第1期28卷 47-62页

作者： Hashemipour, Maryam Sadat Soleimani, Seyed Ali Univ Shahrood Dept Elect & Robot Engn Shahrood Iran

artificial immune system (AIS) algorithm based on clonal selection method can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure along with the feature selection significantly impacts on the classification accuracy rate. In this study, AIS based on Adaptive Clonal Selection (AISACS) algorithm has been used to optimise the SVM parameters and feature subset selection without degrading the SVM classification accuracy. Several public datasets of University of California Irvine machine learning (UCI) repository are employed to calculate the classification accuracy rate in order to evaluate the AISACS approach then it was compared with grid search algorithm and Genetic algorithm (GA) approach. The experimental results show that the feature reduction rate and running time of the AISACS approach are better than the GA approach.

关键词： support vector machine Adaptive clonal selection algorithm feature selection artificial immune system algorithm

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An artificial immune system-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

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JOURNAL OF DIGITAL IMAGING 2015年第5期28卷 576-585页

作者： Wu, Wen-Jie Lin, Shih-Wei Moon, Woo Kyung Chang Gung Univ Dept Informat Management Taoyuan 333 Taiwan Seoul Natl Univ Dept Diagnost Radiol Seoul South Korea

A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96.67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A(z) is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

关键词： Breast tumors Textural feature Morphological feature artificial immune system algorithm Support vector machine

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Tolerance modelling in selective assembly for minimizing linear assembly tolerance variation and assembly cost by using Taguchi and AIS algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第5-8期75卷 869-881页

作者： Babu, J. Rajesh Asha, A. KLN Coll Engn Dept Automobile Engn Pottapalayam 630611 Tamil Nadu India Kamaraj Coll Engn & Technol Dept Mech Engn Virudunagar 626001 Tamil Nadu India

The quality of any product depends upon its component tolerance and assembly variations. Most products are composed of more than one component. These products require some type of assembly during production. Assembly is the process of joining two or more parts together. The functional performance of an assembled product and its manufacturing cost are directly affected by the individual component tolerances. However, tolerance is inevitable because manufacturing exactly equal parts is impossible. When the components are assembled together interchangeably, the assembly tolerance is the sum of the component tolerances. If the required assembly tolerance variation is less than the sum of the components' tolerances, the rejection rate of assemblies will be more. In such cases, the selective assembly is the method to achieve tight assembly tolerance with the components manufactured at wider tolerances. This is accomplished by partitioning produced components into groups prior to random assembly. The mating components in the selective groups are then assembled at random. In this paper, artificial immune system (AIS) algorithm is developed to obtain the best combination of selective group with minimum variation in the assembly tolerance and least loss value within the specification range. The concept of Taguchi's loss function is applied into the selective assembly method to evaluate the deviation from the mean. The selection of number of groups for selective assembly is also analyzed in this paper.

关键词： Selective assembly Taguchi's loss function Assembly tolerance variation Average assembly loss per combination artificial immune system algorithm

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