智能制造中的计算方法：工业4.0应用

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"Computational Methods for Application in Industry 4.0" 是一本专注于将计算方法应用于工业4.0领域的 Springer Briefs in Applied Sciences and Technology 系列书籍，由 Nikolaos E. Karkalos, Angelos P. Markopoulos 和 J. Paulo Davim 联合撰写。这本书探讨了如何利用信息技术的最新进展，尤其是物联网、云计算和大数据分析，来提升制造业自动化和数字化水平，从而改进流程、服务和产品。在工业4.0的背景下，智能化制造是关键的发展方向。通过引入软计算或计算方法，可以实现对工业实践中的优化，这些方法能快速建立仿真模型，有效解决复杂的工程问题。特别是在实时接收工业环境中物理过程信息的决策系统中，采用快速收敛的优化方法能够提供可靠的结果。计算优化技术在这一过程中扮演了至关重要的角色，许多研究已经证实了其价值，并提出了多种方法和技术来解决制造业的问题。书中的内容可能涵盖以下知识点： 1. 工业4.0概念：解释工业4.0的核心理念，即通过信息技术与制造技术的深度融合，实现高度自动化和数字化的生产环境。 2. 物联网（IoT）：讨论物联网如何连接生产设备，收集和交换数据，以优化生产流程并实现远程监控。 3. 云计算：阐述云计算如何提供可扩展的计算资源，支持大规模数据处理和分析，助力工业决策制定。 4. 大数据分析：说明大数据分析如何帮助识别模式，预测趋势，为制造过程的持续改进提供依据。 5. 软计算和计算方法：介绍如遗传算法、模糊逻辑、神经网络等软计算方法，以及它们在解决复杂优化问题中的应用。 6. 智能决策系统：讨论实时数据驱动的决策系统设计，以及如何集成快速收敛的优化算法以提升效率和可靠性。 7. 案例研究：可能包含实际的工业案例，展示计算方法如何成功应用于特定制造问题的解决。 8. 未来展望：探讨计算方法和技术在工业4.0未来发展中可能的角色，以及面临的挑战和机遇。本书不仅适合研究人员、工程师和学者，也适用于希望了解如何利用先进计算技术推动制造业创新的业界人士。通过深入理解并应用这些计算方法，读者可以更好地适应和引领工业4.0时代的变革。

4 1 General Aspects of t he Application of Computational …

system, which will receive information in real time from the physical processes in

the industrial environment and provide reliable results. Thus, it is possible to enable a

higher degree of reasoning for the CPS. During the last decades, nontraditional opti-

mization techniques, with generic capabilities, such as Genetic Algorithms (GA),

Swarm Optimization Algorithms, or others, were introduced to the ﬁeld of industrial

engineering in order to facilitate the solution of industry-related problems. These

methods are intended to be presented in this book, with a view to inform the inter-

ested reader of the optimization techniques available and be assistive to the selection

of appropriate optimization techniques for each problem. Furthermore, the details

regarding the computational efﬁciency of each technique, which are provided in each

section of this book, are important to be considered in the case of actual integration

of these algorithms in the decision-making processes of CPS.

As it will be shown in this book, the ﬁeld of optimization methods in indus-

trial practice is always active and of great importance and thus, it is necessary to

acquire a general knowledge of the most useful methods and their applications in

order to be able to apply them efﬁciently in real-life situations. Thus, computational

methods, which can be used by intelligent systems within the concept of Indus-

try 4.0, were gathered and presented. These methods include Evolutionary-Based

and Swarm Intelligence-Based methods; however, some more methods are included,

such as Simulated Annealing method. Each method is explained in its fundamental

aspects, while some notable bibliography is provided for further reading.

In speciﬁc, the outline of the present book is structured as follows: in the current

chapter, the general aspects of optimization are summarized and a brief literature sur-

vey is presented with a view to underline the popularity and relevance of the described

methods in the ﬁeld of industrial engineering. In Chap. 2, several evolutionary-based

metaheuristics, e.g., the Imperialist Competitive algorithm, the Biogeography-Based

Optimization method or the Teaching-Learning Based Optimization method are pre-

sented, with emphasis set on the most popular of them, namely the Genetic Algorithm.

In Chap. 3, the Swarm Intelligence-Based methods, such as Particle Swarm Opti-

mization, Artiﬁcial Bee Colony optimization, Ant Colony Optimization, and others,

are described. Finally, in Chap. 4, other optimization techniques, not pertinent to the

aforementioned categories, such as Simulated Annealing method, are presented.

1.2 General Aspects and Deﬁnitions on Optimization

Methods

An optimization problem is essentially the problem of determining the best solu-

tion from the feasible solutions of this problem. Optimization problems may involve

either continuous or discrete value variables; the latter problems are also called com-

binatorial optimization problems. Furthermore, one of the most important aspects of

optimization problems is that they can be also subjected, or not, to several restrictions.

1.2 General Aspects and Deﬁnitions on Optimization Methods 5

A continuous optimization problem can be formulated generally as follows [16]:

• minimize F(x), in respect to x,

• subject to g

(x) ≤ 0, i =1, …,m and h

(x)=0, i =1, …, p

where F(x ) is the function that is required to be optimized and is referred to as the

objective function. F(x) is usually a function of multiple variables, so x is usually

deﬁned as a vector; this vector is referred to as the vector of design variables of the

problem. For most engineering optimization problems, the lower and upper bound

of design variables value is deﬁned according to the exact requirements of each

problem, or deﬁned as a reasonable range for each problem, otherwise. Furthermore,

(x) represents the equality constraints and g

(x) represents the inequality constraints

that are imposed.

When the purpose of optimization is the determination of the best solution accord-

ing to multiple goals, then a multi-objective optimization problem arises. In order to

solve these problems, some modiﬁcations are required and the use of Pareto charts

is essential for the determination of favorable solutions, according to the criteria set.

Although the optimization methods are actually related with the mathematical

problems of determining the extrema of a function, optimization problems in the

ﬁeld of engineering are nowadays usually resolved with the aid of special compu-

tational methods. Optimization can be conducted either by a deterministic method,

in which an initial solution is guided towards the region of optimum solution in a

deterministic way, by taking advantage of analytical properties of the problem [17]

or by a stochastic method, which incorporates stochastic processes and random vari-

ables in order to provide more ﬂexibility and be capable to avoid reaching a local

optimum instead of the global one. Deterministic or exact optimization methods can

guarantee the determination of an optimal solution, while stochastic methods cannot

[18]. Generally, there are methods that may require no further information except for

the objective function, e.g., zero-order methods, or they may require the computation

of ﬁrst, second, and rarely third derivative of this function in respect to the design

variables in order to establish a better guidance of the solution towards the optimum

point, i.e., a technique mostly related to deterministic optimization methods. Some

of the main differences between deterministic and stochastic techniques are the con-

vergence speed or computational cost and also the ability to prevent the optimization

process from premature convergence in local optima. Usually, deterministic algo-

rithms can provide a quicker solution but most of them cannot handle the situation

when the solution is attracted to a local optimum [19]. All stochastic optimization

algorithms described in the present work do not require other information for the

problem, which is attempted to be solved except for the optimization function, the

design variables bound and the constraints, so they can be employed as “black-box”

without the need to intervene with the physics or other details of the optimization

problem.

As far as computational optimization techniques are concerned, many categories

of methods exist; especially before presenting details of the methods, which will

be presented in the current work, it is important to deﬁne two basic categories of

methods closely related to optimization, namely heuristics and metaheuristics.

6 1 General Aspects of t he Application of Computational …

Heuristic techniques constitute a common approach for determining appropriate

and feasible solutions for various problems without ensuring that these solutions are

optimal. These techniques often involve the use of practical methods or even trial-

and-error method in order to approach the solution of a problem. Regarding computer

science, heuristic techniques are employed to solve a large variety of problems. The

characteristics of these techniques are the relatively low computational cost and high

speed but also lower accuracy and no guarantee that the optimal solution can be

determined; however in many practical problems for which the optimal solution is

hard t o be found in a reasonable time, solutions of heuristic can be acceptable. It is

to be noted that these algorithms can be combined with optimization techniques to

enhance their capabilities.

Metaheuristics are considered to be a heuristic or generally a similar procedure of

a higher level which manages the optimization procedure. In fact, they are capable

of guiding underlying heuristics to solve optimization problems [20]. These algo-

rithms do not ensure that a globally optimum solution will be determined, but they

are capable to determine good solutions by conducting search in large regions and

perform better than other optimization techniques or simple heuristics. It is usual

for metaheuristics to employ stochastic procedures and they can be used for generic,

problem independent applications. One of the basic features of these methods is the

appropriate way t o balance between global and local search capability, termed also

as exploration and exploitation (or diversiﬁcation and intensiﬁcation), respectively.

Methods should be capable of exploring many promising regions of the search space

and move away from regions of local optima before converging to a less optimal

solution. However, they also should be able to conduct local search in the proximity

of promising solutions in order to ﬁnd a potentially better neighboring solution.

A considerable amount of metaheuristics and actually the majority of recently

invented ones are based on metaphors related to nature. It is generally assumed from

the researchers that natural processes, derived from thousands or even millions of

years of evolutionary, possess incredibly optimum features, which can be interpreted

as algorithms. However, this cannot be sufﬁcient in some cases and hybridization of

different metaheuristics or other techniques is performed. The reason for the devel-

opment of such methods is that some of the metaheuristics are based on metaphors

which cannot guarantee efﬁcient performance. For example, some algorithms are

weak in the exploration part and are prone to trapping in local minima, whereas

others cannot conduct efﬁcient local search in the vicinity of promising candidate

solutions.

As far as optimization problems are concerned, it should be necessary to mention

some of the most important problems encountered in the ﬁeld of engineering. These

problems arise usually from the everyday practice in industry and most of them are

shown to be almost impossible to be solved by regular optimization methods, such

as exact optimization methods [20], which are capable of solving problems whose

cost grows polynomially with the problem size [18]. For difﬁcult problems (NP-

Hard), the cost of solution required by an exact method grows exponentially with the

problem size, so it is necessary to use a metaheuristic in order to obtain a solution

in reasonable time [18]. Some of the problems often encountered in production

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