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Production planning and control system on the factory floor using the cooperation of intelligent agents to achieve mass customization

Number of pages: 111 File Format: word File Code: 30906
Year: 2009 University Degree: Master's degree Category: Electronic Engineering
Tags/Keywords: Achieving mass customization - Customization - Intelligent agents - Mass customization - Planning system - Production control system
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  • Summary of Production planning and control system on the factory floor using the cooperation of intelligent agents to achieve mass customization

    Master's Thesis in Mechatronic Engineering

    Introduction

    Recent advances in flexible manufacturing and information technology have made it possible for manufacturing systems to provide a wider range of products or services at a lower cost. In addition, the increase in competition at the global level has led to the confrontation of industries with the approach of increasing customer value in providing products or services. Therefore, the need to consider the specific needs of each customer has led manufacturers to involve customers in the production process. In the meantime, customization in mass production [1] is one of the new production methods that is being noticed by more producers every day. Customization in mass production is the ability to produce a product or service specific to each customer based on their order or known needs through a fully flexible and integrated process while maintaining the benefits of mass production. Customization in mass production is one of the opportunities that have been provided to producers through the growth and coordination of production technologies and information technology. It is clear that the activities carried out in mass production customization systems require extensive cooperation, information exchange and interaction within and outside the organization. Part of this interaction is to plan the assignment of tasks to resources and schedule the execution of tasks on resources. The problem of planning the assignment of tasks to resources and scheduling the execution of tasks is considered one of the most complex optimization problems of combinations, which in this research is an attempt to achieve an expansion in the field of solving this category of problems.

    In customization systems in mass production, since each manufactured product will have its own conditions, based on the stated needs of the customer, the problem of coordination and interaction of components on the factory floor takes on a more complex form. In order to solve the planning problem in such conditions, the components of the planning system must have characteristics such as communication with other components, responsiveness and autonomy. According to these characteristics, the use of multi-agent evolutionary systems is proposed as one of the appropriate solutions. In this method, solutions can be obtained in a dynamic computing environment by using social factors, each of which has a set of characteristics and resources.

    1-2- Research innovations

    In this research, according to the research background that has been carried out in the field of solving the problem of flexible workshop work planning and scheduling, a new and effective model for modeling the solution space of the problem is presented. The presented model has special characteristics, among the most important of which we can point out the simultaneous solution of the sub-problems of planning the assignment of tasks to resources and the scheduling of the order of execution of tasks. In the continuation of the research, the optimization algorithm of the collective movement of particles is introduced and a new form of this algorithm is introduced to solve multi-objective optimization problems, in which the selection of guide particles is made based on the density of particles in the target space, then the presented algorithm is compared with one of the similar algorithms. After that, the two presented ideas will be used in solving the problem of flexible workshop work scheduling and its results will be examined. 1-3- Structure of the thesis In the continuation of the thesis, in the second chapter, the concepts of mass customization and its implementation levels will be presented, and the factors that lead to its successful implementation will be examined from the perspective of different authors and researchers, and the issue of planning and production scheduling will be presented. To achieve customization in mass production, it is reduced to the problem of planning and scheduling the flexible production workshop [2] with several objectives [3]. From here on, the research approach will be towards solving the multi-objective scheduling problem in flexible workshop work. In the third chapter, the concepts of workshop scheduling are presented and by using them, a suitable mathematical model is obtained for the problem of flexible workshop work scheduling. In the continuation of the thesis, in the fourth chapter, a short review of the concept of metaexploration [4] and its application in optimization problems is done. In this chapter, some important meta-heuristic methods that are widely used in optimization are introduced in the form of three categories.. In the fifth chapter, multi-objective optimization methods are studied based on the optimization algorithm of collective motion of particles [5] as an evolutionary [6] multi-factor [7] method, and a new algorithm based on the kernel density [8] of particles in the target space is presented and compared with one of the multi-objective optimization algorithms based on collective motion of particles. In the sixth chapter, first, a new representation of the search space for the flexible workshop work scheduling problem is introduced, which provides the ability to simultaneously solve the sub-problems of assigning operations to machines and scheduling the order of execution of operations simultaneously. Then, in the continuation of this chapter, the optimization algorithm that was introduced in the fifth chapter is used to solve the flexible workshop work scheduling problem and its results are compared with other methods. Finally, in the seventh chapter, the results of the research conducted in this thesis are discussed and some suggestions are presented for the expansion of research in this field. Chapter 2: Customization in mass production.

    2-1- Introduction

    Recent advances in flexible manufacturing and information technology, which enable production systems to offer a wider range of products at lower costs, shortening product life cycles, and increasing industrial competition that requires manufacturing strategies that pay attention to the needs of individual customers have led to the emergence of customization in mass production.

    One of the first people to talk about customization in mass production is Alvin It was Toffler. Alvin Toffler started his career as a journalist, but achieved international fame with the publication of his first book, The Future Strike [1] in 1970. "Third Wave" [2] was released ten years later, and "Transition of Power" [3] ten years after that. The third wave mentioned in the title of the book refers to the super-industrial society that emerged at the end of the 20th century and is still being formed. This society was created after the second wave, i.e. the industrial society, which itself was caused by the industrial revolution. The industrial society emerged after the agricultural stage, which is known as the first wave. Each new wave appeared by the development of new technology. Finally, electronic technology gave rise to the third wave. Toffler's main concern is the transition from the second wave to the third wave in advanced societies, although he also examines the possible area of ??friction between people who are in different stages of development (conditions of different waves) and coexist with each other. According to Toffler, the characteristic feature of the third wave is mass customization instead of mass production.

    When we look at the history or records of prominent automobile companies, we see that they have also used the same strategies. At one time, they were engaged in mass production, after that pure production became popular, and now we are in a period where mass production based on people's needs and tastes or customization has become important in mass production. Doing such work means production according to the customer's order, with all the advantages of mass production, is only possible when the tools are available.

    The concept of customization in mass production was officially introduced in the late 1980s, and it is a logical continuation of development and progress in various fields of production, such as flexible and optimized production based on quality and price. According to the definition provided by Davis [9] in 1989 [4], customization in mass production is the provision of specific products or services for a customer through high agility in the process, flexibility and integration in the production system. They believe that customization in mass production is the use of information technology, flexible processes and special organizational architecture to provide a wide range of products and services that respond to the specific needs of each customer (often through a set of choices) with a cost close to mass production [5]. It covers from customer selection to product delivery.

    Despite the many problems of implementing production systems based on customization in mass production, the following problems seem to be more fundamental: a) Keeping the price of customized product low compared to standard product mass production.

  • Contents & References of Production planning and control system on the factory floor using the cooperation of intelligent agents to achieve mass customization

    List:

    Chapter 1: Introduction. 5

    1-1- Introduction. 6

    1-2- Research innovations. 7

    1-3- Structure of thesis. 7

    Chapter 2: Customization in mass production. 9

    2-1- Introduction. 10

    2-2- Customization levels. 12

    2-3- Capabilities needed to implement customization in mass production 16

    2-5- Summary and conclusion. 18

    Chapter 3: Planning and scheduling. 19

    3-1- Introduction. 20

    3-2- Production system modeling in planning and scheduling. 21

    3-3- Works, machines and workshops 24

    3-4- Performance criteria in production scheduling. 28

    3-5- Mathematical modeling of flexible workshop scheduling problem. 32

    3-6- Research background in the field of flexible workshop scheduling. 35

    3-7- Summary and conclusion. 37

    Chapter 4: meta discoveries in optimization. Error! Bookmark not defined.

    4-1- Introduction. 80

    4-2- Basic definitions. 80

    4-3- Route-based methods. 85

    4-4- exploratory local search methods. 92

    4-5- Population-based methods. 97

    4-6- Summary and conclusion. 107

    Chapter 5: Multi-objective optimization with collective motion of particles. 40

    5-1- Introduction. 41

    5-5- Summary and conclusion. 61

    Chapter 6: Application of DbMOPSO in flexible workshop planning and scheduling. 63

    6-1- Introduction. 64

    6-2- Search space. 65

    6-3- Issues to be investigated. 66

    6-4- Simulation results. 71

    6-6- Summary and conclusion. 75

    Chapter 7: Summary and conclusion. 76

    7-1- Introduction. 77

    7-2- Research achievements. 77

    3-7- Axis of further study and expansion. 78

    References. 109

     

     

     

     

     

    Table 1-2: General customization levels

    15

    Table 6-1: Execution times of the operations related to sample problem 1

    95

    Table 2-6: Operation execution times related to problem example 2

    96

    Table 6-3: Operation execution times related to problem example 3

    97

    Table 6-4: Operation execution times related to problem example 4

    98

    Table 6-5: Objective function values ??of solutions obtained for example problem 1 by Kasem and Colleagues

    100

    Table 6-6: The values ??of the objective functions of the solutions obtained for the sample problem 1 using the DbMOPSO algorithm

    100

    Table 6-7: The values ??of the objective functions of the solutions obtained for the sample problem 2 by Kasem et al.

    101

    Table 6-8: The values ??of the objective functions of the solutions obtained for the sample problem 2 Using the DbMOPSO algorithm

    101

    Table 6-9: Values ??of the objective functions of the solutions obtained for sample problem 3 by Kasem et al.

    102

    Table 6-10: Values ??of the objective functions of the solutions obtained for the sample problem 3 using the DbMOPSO algorithm

    102

    Table 6-11: Values The objective functions of the solutions obtained for sample problem 4 by Kasem et al. 103 Table 6-12: Values ??of the objective functions of the solutions obtained for sample problem 4 using the DbMOPSO algorithm 103 Production

    23

    Figure 4-1: Iterative improvement algorithm

    46

    Figure 4-2: Simulated fusion algorithm

    46

    Figure 4-3: Guided local search idea

    54

    Figure 4-4: An arbitrary step of ILS

    56

    Figure 4-5: The general format of evolutionary computing algorithms

    58

    Figure 4-6: The general format of the collective movement algorithm of particles

    65

    Figure 5-1: The concept of Pareto optimal solution

    70

    Figure 5-2: Showing the concept of dominance in the goal space

    71

    Figure 5-3: Showing the Pareto Optimal Front in Objective space

    72

    Figure 5-4: DbMOPSO algorithm

    76

    Figure 5-5: Result of NSGA II algorithm in solving sample problem 1

    78

    Figure 5-6: Result of PAES algorithm in solving sample problem 1

    78

    Figure 5-7: Result of algorithm MOPSO in solving sample problem 1

    79

    Figure 5-8: The result of the algorithm109

     

     

     

     

     

    Table 1-2: General customization levels

    15

    Table 6-1: Execution times of the operations related to sample problem 1

    95

    Table 2-6: Operation execution times related to problem example 2

    96

    Table 6-3: Operation execution times related to problem example 3

    97

    Table 6-4: Operation execution times related to problem example 4

    98

    Table 6-5: Objective function values ??of solutions obtained for example problem 1 by Kasem and Colleagues

    100

    Table 6-6: The values ??of the objective functions of the solutions obtained for the sample problem 1 using the DbMOPSO algorithm

    100

    Table 6-7: The values ??of the objective functions of the solutions obtained for the sample problem 2 by Kasem et al.

    101

    Table 6-8: The values ??of the objective functions of the solutions obtained for the sample problem 2 Using the DbMOPSO algorithm

    101

    Table 6-9: Values ??of the objective functions of the solutions obtained for sample problem 3 by Kasem et al.

    102

    Table 6-10: Values ??of the objective functions of the solutions obtained for the sample problem 3 using the DbMOPSO algorithm

    102

    Table 6-11: Values The objective functions of the solutions obtained for sample problem 4 by Kasem et al. 103 Table 6-12: Values ??of the objective functions of the solutions obtained for sample problem 4 using the DbMOPSO algorithm 103 Production

    23

    Figure 4-1: Iterative improvement algorithm

    46

    Figure 4-2: Simulated fusion algorithm

    46

    Figure 4-3: Guided local search idea

    54

    Figure 4-4: An arbitrary step of ILS

    56

    Figure 4-5: The general format of evolutionary computing algorithms

    58

    Figure 4-6: The general format of the collective movement algorithm of particles

    65

    Figure 5-1: The concept of Pareto optimal solution

    70

    Figure 5-2: Showing the concept of dominance in the goal space

    71

    Figure 5-3: Showing the Pareto Optimal Front in Objective space

    72

    Figure 5-4: DbMOPSO algorithm

    76

    Figure 5-5: Result of NSGA II algorithm in solving sample problem 1

    78

    Figure 5-6: Result of PAES algorithm in solving sample problem 1

    78

    Figure 5-7: Result of algorithm MOPSO in solving sample problem 1

    79

    Figure 5-8: The result of DbMOPSO algorithm in solving sample problem 1

    79

    Figure 5-9: The result of NSGA II algorithm in solving sample problem 2

    81

    Figure 5-10: The result of PAES algorithm in solving sample problem 2

    81

    Figure 5-11: The result of MOPSO algorithm in solving sample problem 2

    82

    Figure 5-12: The result of DbMOPSO algorithm in solving sample problem 2

    82

    Figure 5-13: The result of NSGA II algorithm in solving sample problem 3

    84

    Figure 5-14: The result of PAES algorithm in solving sample problem 3

    84

    Figure 5-15: The result of MOPSO algorithm in solving sample problem 3

    85

    Figure 5-16: The result of DbMOPSO algorithm in solving sample problem 3

    85

    Figure 5-17: The result of NSGA II algorithm in solving problem Example 4

    87

    Figure 5-18: The result of the PAES algorithm in solving the problem of example 4

    87

    Figure 5-19: The result of the MOPSO algorithm in solving the problem of example 4

    88

    Figure 5-20: The result of the DbMOPSO algorithm in solving the problem of example 4

    88

    Source:

    References

     

    A. Toffler, "Future shock," Bantam Books, 1970

    A. Toffler, "The third wave," Bantam Books, 1980

    A. Toffler, "Powershift: knowledge, wealth and violence at the edge of the 21st century," Bantam Books, 1990

    S. Davis, "From future perfect: Mass customizing," Planning Review, vol. 17, no. 2, pp. 16-21, 1989

    G. Da Silveira et al., "Mass customization: Literature review and research directions," Int. J. Production Economics, vol. 72, pp. 1-13, 2001

    J. Gilmore, and J. Pine, "The four faces of mass customization," Harvard Business Review, vol. 75, no. 1, pp. 91-101, 1997

    J. Pine, "Mass customizing products and services," Planning Review, vol. 21, no. 4, pp. 6-13, 1993

    J. Spira, "Mass customization through training at Lutron Electronics," Computers in Industry, vol. 30, no. 3, pp. 171-174, 1996

    M. Pinedo, "Planning and scheduling in manufacturing services," Springer, 2005

    P. Bruker, and R. Schlie, "Job shop scheduling with multi-purpose machines," Computing, vol. 45, pp. 369–375, 1990

    E. Hurink, B. Jurisch, and M. Thole, "Tabu search for the job shop scheduling problem with multi-purpose machines," Operations Research Spektrum, vol. 15, pp. 205-215, 1994

    M. Mastrolloli, and L.M. Gambardella, "Effective neighborhood functions for the flexible job shop problem," Journal of Scheduling, vol. 3, no. 1, pp. 3-20, 2002

    I. Kacem, S. Hammadi, and P. Borne, "Pareto-optimality approach for flexible job-shop scheduling problems: Hybridization of evolutionary algorithms and fuzzy logic," Mathematics and Computers in Simulation, vol. 60, pp. 245–276, 2002

    W. Xia, and Z. Wu, "An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems," Computers & Industrial Engineering, vol. 48, pp. 409–425, 2005

    C. Blum, "Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison," ACM Computing Surveys, vol. 35, no. 3, pp. 268–308, 2003

    I.H. OSMAN, and G. LAPORTE, "Metaheuristics: A bibliography," Ann. Operations Research, vol. 63, pp. 513–623, 1996

    S. VO?, et. al., "Meta-Heuristics—Advances and Trends in Local Search Paradigms for Optimization," Kluwer Academic Publishers, 1999

    METAHEURISTICS NETWORK WEBSITE, http://www.metaheuristics.net/, Visited in 2009

    C. Coello, G. Pulido, and M. Lechuga, "Handling multiple objectives with particle swarm optimization," IEEE Trans. Evolutionary Computation, vol. 8, pp. 256-279, 2004

    E. Zitzler, "Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications," Ph.D. thesis, Swiss Federal Institute of Technology Zurich, 1999

    K. Deb, "Multi-objective genetic algorithms: problem difficulties and construction of test problems," Evolutionary Computation, vol. 7, no. 3, pp.

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