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CHEMICAL ENGINEERING TRANSACTIONS
VOL. 66, 2018
A publication of
The Italian Association
of Chemical Engineering
Online at www.aidic.it/cet
Guest Editors: Songying Zhao, Yougang Sun, Ye Zhou
Copyright © 2018, AIDIC Servizi S.r.l.
ISBN 978-88-95608-63-1; ISSN 2283-9216
Application of Extension Strategy Generation System
Algorithm in Chemical Enterprise Management
Wei Wu
Anhui Finance & Trade Vocational College, Anhui 230601,China
weiwu28319120@126.com
This paper aims to study the feasibility and effectiveness of extension strategy generation system algorithm in
the chemical enterprise management. To this end, the problems that often occur in the management process
of domestic chemical enterprises were analysed, and then by introducing the extension strategy generation
system algorithm, a data model conforming to the characteristics of enterprise management was established.
The results indicate that in the chemical enterprise management, the applied extension strategy generation
system algorithm is highly feasible. It provides an important reference for solving the problems in the
enterprise management process. Therefore, in the related management work of chemical enterprises, the
application of extension strategy generation system algorithm can not only quickly improve the efficiency and
level of management, but also provide effective guarantees for promoting the long-term sound development of
enterprises. This is of great value of promotion and application.
1. Introduction
With the rapid development of the social economy, the information technological level has been continuously
improved. At the same time, the systems that are related to decision-making have become more and more
complex, so, more parameters should be considered in the actual application process. In this context, the
optional strategies continue to emerge. If only relying on the human brain to generate strategies, the
effectiveness of their decisions cannot be guaranteed. In the increasingly fiercely competitive market
environment, in order to maintain their core competitive advantages and stand firm, the enterprises have
expected to promote strategic generation and problem development or early warning through the use of
computer and network technologies during the massive information processing and reprocessing, so as to
provide the reliable reference for subsequent decisions. But this isn’t supported by the auxiliary decision
support system and the management software in the current market. The fundamental reason is that firstly,
the related research results for the basic theory of strategy generation are still in the embryonic stage;
secondly, in terms of the strategy generation method and model, the feasibility and effect of computer
application need to be further discussed and studied. The extension strategy generation system is mainly
based on Extenics. Its application provides the key information for the joint development of artificial
intelligence and decision science.
Since the operational extension transformation research is realized on the basis of the computer, it is
necessary to comprehensively analyse and measure the relevant computer technologies, such as the
database, data mining, object-orientation and artificial intelligence etc. The domestic research on the
extension transformation has been conducted officially, but there have been very few research reports on its
implementation on computers and the strategy generation on the basis of extenics. Therefore, in the research
process of strategy generation algorithms, the Extenics method and its principles must be introduced, which
should be all presented on the computer. This shall offer a new direction for the research and development of
extenics, and also provide the important reference for the realization of intelligent management in chemical
enterprises.
DOI: 10.3303/CET1866245
Please cite this article as: Wu W., 2018, Application of extension strategy generation system algorithm in chemical enterprise management,
Chemical Engineering Transactions, 66, 1465-1470 DOI:10.3303/CET1866245
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2. Literature review
With the changes of the times, the development of the world economy is complicated. China's economic
development has entered a downward path. With the trend of economic globalization is more and more
obvious, trade protectionism is in vogue. The company is in a very special period. At this stage, there is
insufficient demand in the domestic market and the situation in foreign markets is complex. The operation and
management of an enterprise is affected by various factors. When it comes to decision-making, it is more
difficult than ever before. Corporate management strategies are increasingly difficult. Some strategies have
flaws and cannot take into account the overall situation. In the aspect of corporate management, chemical
companies are even more strenuous. Enterprise management, employee management, production
management, equipment management, and safety management are all considerations for business
managers. In recent years, the government has paid more and more attention to the safe production and
environmental protection of enterprises. In 2017, many companies were ordered by the environmental
protection department to stop work for rectification due to environmental problems. These factors make
chemical companies more and more pressure on management. In particular, in the face of a complex and
ever-changing environment, it is very difficult for management personnel to change management decisions.
Chemical company managers began to use the extension strategy generation system algorithm to manage
the enterprise. Extension strategy management is based on the theory of extenics. Combining computers, and
based on the different data presented, strategies are intelligently generated (Borland et al., 2016).
Cai Wen's land mark article "Extended Sets and Non-Compatibility Issues" was published in 1983. Therefore,
it declares the birth of Extenics. The goal of Extenics is to study the uncompromising and incompatible issues
in the real world (Ertek et al., 2017). The theoretical basis of Extenics is matter-element theory and extended
set theory. Extended control, extended information, extended systems, extended logic, etc. are all part of this
theoretical system. Its practical methods include the expansion of engineering methods, further including the
concept of extended feature analysis, bridge conversion, extended cognition, extended decision making, and
extended forecasting. The three pillar theories of extension theory are elementary theory, extension set theory
and extension logic. Extenics mainly studies the dynamic set theory based on transformation to express the
quantized tool that transforms the contradiction problem into the non-contradictory and the nature
transformation process by transformation. A formal model that combines quality and quantity is studied to
overcome the limitation of the mathematical model, the transformation of the contradiction and the basis of the
transformation of the object. The basic theory and method of extenics and the combination of knowledge in
various fields are studied.
The process of management is to solve contradictions by developing new ideas (Kumar et al., 2017). Extenics
is a discipline that solves this problem. It uses formal positive models to study the possibility of expanding
things and develop new rules and methods of innovation, and to solve contradictions (Lieder and Rashid,
2016). Based on the current state of application of extenics in the field of management, the prospects of
applying extenics in conflict management, system thinking, and complex system management are proposed
(Lin et al., 2017). In the era of knowledge economy, information and knowledge have become an important
resource. At the same time, it also caused information explosion and knowledge overload. The intelligence of
information management and knowledge management has put forward new requirements (McDonald-Buller,
et al., 2016). In exploring the problems encountered in the process of information management and
knowledge management, based on the concept of extenics, it proposes a complementary development model
between information management, knowledge management and extenics. The existing research results have
enriched the foundation and expanded the basis for transformation methods. In the process of information
management and knowledge management, the introduction of extenics methods and concepts can make the
development of information management and knowledge management more intelligent (Rangaiah et al.,
2015).
Chinese scholars have continued to conduct in-depth studies of extensions and integrate ideas with business
management. It provides a good idea and foundation for chemical enterprise extension strategy management.
The bottlenecks encountered in the management of domestic chemical companies can be solved based on
the research of these outstanding scholars. At present, domestic data mining, data analysis, machine learning,
artificial intelligence, big data analysis and other technologies have developed rapidly. All these provide an
excellent technical basis for the development of the extension strategy generation system. In the current era,
enterprise management technology has also been continuously updated to better solve the complex problems
encountered by enterprises in the real world. The extension strategy generation system is an artificial
intelligence strategy generation system that uses computers to assist people in generating contradictory
problems (Simon et al., 2014).
In summary, the theory of extenics is of great significance in business management. Chemical companies
face complex situations at home and abroad, and risk factors have greatly increased. Enterprises must rely on
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enough information to make decisions in business management. Obviously, the traditional way of human
decision-making is not entirely competent in enterprise management. Research, development, and application
of extension strategy generation systems are of great significance to chemical companies. The extension
strategy generation system can greatly improve the accuracy of chemical companies' decision-making.
According to the environment in which the company is located, this system can consider the factors affecting
the company in multiple ways. It provides the chemical industry with a variety of decision-making programs
that are fast, comprehensive, and scientific, so that managers of chemical companies can select and make
decisions. The operating efficiency of the company is improved, human resources are saved, and the
management efficiency and capability of chemical companies are improved. This provides comprehensive
support for the development of the company.
3. Methods
Through the study of extenics, database technology, and object-oriented technology, the main contents of this
study are as follows: Based on the basic extension theories and methods, the theoretical requirements and
system design requirements related to this topic are discussed; the study is based on the theory and method
of extenics which can be used in the formalized description of computer-generated strategy; the combination
and application of extension method and database, data warehouse and object-oriented technologies were
explored; the related definitions and implementation methods were discussed by the extension data mining
method; based on the theoretical results of the above discussion and research, a strategy generation system
for clothing sales based on extension transformation was designed and implemented; By adopting a more
reasonable human-computer interaction interface and using the data mining method, the strategy generation
and evaluation process based on the extension transformation technology are achieved more completely.
Figure 1 shows the effect of the intelligent extension algorithm applied in the chemical enterprise management
under the strategy of extension strategies, where, a is the initial state and b is the end state.
Figure 1: Effect diagram of the algorithm can be extended
3. Results and analysis
3.1 Knowledge about extension strategy generation
The Extenics is to adopt the method of extension transformation on the conditions or purpose of the problem
through analysis of the inconsistency problem, produce the extension strategy set, and apply effective
strategies to solve the problem. Extension strategy is the specific measure to solve contradictory problems. It
is the ultimate goal of applying Extenics and also the way that Extenics can be applied to actual production
and life. The extension strategy is to use the extension transform equation to change the compatibility degree
of incompatible problems from ≤0 to ＞0. The solution transformation of incompatible problems is an extension
strategy. The process to generate an extension strategy is called extension strategy generation. Extension
strategy generally means to apply extension transformation to one certain problem or target based on the
problem related tree, and then generate the strategy set for solving the contradictory problem. According to
the nature of related networks and correlations, this extension transformation will also cause conduction
transformation on the primitives of problem-related tree. Through the transformation of one certain leaf
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primitive in the initial correlation tree, the generated implication tree of extension transformation is called the
extension strategy generation tree. In the specific application system of extension strategy generation,
according to the extension model of the contradiction problem, the conditions or targets of this problem are
conducted with extension analysis and extension transformation so as to generate the strategy set over 0;
then through the evaluation of goodness, the high-evaluated strategy is recommended to decision makers for
selection. Table 1 lists the relevant conditions of the generated objects.
Table 1: Calculation table
work ACTIM
1-2-3-4 3+2+4 9
1-3 5 5
2-3 2 2
2-3-4 2+4 6
3-4 4 4
The experiments were made for the two algorithms with more applications, by taking the SKA algorithm in
literature [6] for comparison. The experimental platform and main parameters include: (1) The computer with
CPU intelP43.06 and the memory 1G; (2) The operating system of Windows XP; (3) The simulation software
Matlab 7.01. The experimental results are shown in Figure 2.
Figure 2: Calculation time comparison between GKA and SKA
Experiments show that for both GKA and SKA algorithms, the computing time have exponentially increased,
but that of GKA growth is relatively slow. It’s because that in the beginning phase of test, the key identification
file in GKA needs to be continuously updated, which spends extra more time.
3.2 Generation algorithm method and simulation experiment
3.2.1 Fractal recursive algorithm
The recursive algorithm can be simply understood as a “self-replicating” process. Essentially, it uses the
computer’s “push-and-pop the stack” to store the breakpoints of the function call, and repeatedly applies some
specific rules to generate nested structures. Fractals have self-similar features, so recursive algorithms can be
used to generate fine fractals. For the generation of those classic fractal graphs, most of them can use the
fractal recursive algorithm. It is the simplest algorithm for generating fractal graphs. The Cantor-ternary set is a
special point set constructed by the German mathematician Cantor when studying the trigonometric series
problems.it is a classic self-similar fractal graph. Later, the meteorologist, Richardson (1881-1953) applied it to
the practical measurement for the length of the west coast of the United Kingdom. The Cantor-ternary set has
a great influence, so, the fractal recursive algorithm is used to achieve its mapping. Figure 3 shows the
principle of using the recursive algorithm to realize the Cantor-ternary set.
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Figure 3: Cantor form principle figure
The basic idea of IFS is to use the fractal self-affinity feature to realize the generation of two-dimensional
fractal graphs. Many objects of two-dimensional fractal graph have a certain degree of similarity in their locality
and entirety. The IFS algorithm is through the continuous use of fractal self-similarity features and repeated
iterative computing, to find the fractal affine transformation parameters, make some formula derivations, and
achieve the final two-dimensional fractal graph. The resulting two-dimensional fractal map is dependent on the
iteration rules and the affine transform coefficients, with no relation to the selected initial point. In general, to
generate a very complex graph, many different affine transformations must be made. But affine
transformations require artificial modifications because affine transformations may not be needed in a certain
direction. Therefore, the probability P that the affine transformation is invoked was introduced as one variable.
When the probability P is larger, it means that the number of affine transformations in this direction is greater;
if P is smaller, the number of affine transformations is smaller. If this phenomenon is reflected on the
generated two-dimensional map, it can be understood that if the portion with a large probability P occupies a
large proportion of the entire graph, the plotted points are more intensive. If the portion with the small
probability P occupies a small proportion of the entire graph, the dots are sparse and the colors naturally look
relatively dark. By introducing the probability P, the affine transformation has the distinction between primary
and secondary, and also makes the generated two-dimensional fractal image more harmonious and more
realistic. Table 2 lists the IFS code of the Sierpinski gasket.
Table 2: The IFS code of Sierpinski gasket
i ai bi ci di ei fi pi
1 0.5 0 0 0.5 0 0 0.34
2 0.5 0 0 0.5 0.5 0 0.33
3 0.5 0 0 0.5 0.25 0.5 0.34
In Table 3 below, for example, based on the characteristics of chemical enterprises, the corresponding
management chart was created, in which (a) and (b) are the materials required for management process, and
the material generated by (c) can not only be taken as the reference for the management of chemical
enterprises, but can also be applied in other fields
Table 3: Generating system algorithm generating rules
Corresponding illustrations The number of W P
a 90 F-F-F P：F-F[F]+F+F[F]-F
b 90 F-F-F P:F-F[F]+F-F[++F]-F+F
c 90 F-F-F P:F-FF[-F+F+F]F
According to the principle of L-system algorithm to generate two-dimensional fractal maps, the L system
algorithm program flow was designed. By code writing, the fractal graph corresponding to the rules shown in
Table 3 above was implemented.
4. Conclusions
After a series of studies in this paper, the following conclusions have been drawn:
Based on the basic extension theories and methods, the related theoretical requirements and system design
requirements were discussed;
It uses the extension theory and method to solve contradictory problems, adopts the extension model to
describe things and laws in the objective world, applies extension reasoning and extension transformation to
establish inference rules for generating strategies, and sets extension and association as a quantitative tool
for strategy generation and strategy evaluation so as to provide new methods for computer intelligence.
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Due to the limitations in research time and our academic level, there remain still many aspects of this system
for further improvement and optimization, and also much work to be completed and improved in future.
Through the combination of extension theory and computer technology, the development of the extension
strategy generation system implemented on the computer are still in the preliminary stage, and further
research should be conducted later.
Acknowledgement
Supported by the Program for Excellent Young Talents of Universities in Anhui Province in 2017.
(gxyqZD2017092);
Supported by the Research Project of Major Educational Reform of Anhui Province in 2017. (2017jyxm0803).
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