Enterprise Performance Optimization Management Decision-Making and Coordination Mechanism Based on Multiobjective Optimization

Abstract

Today’s society is a society of the knowledge economy, and the competition of enterprises is the competition of talents. The rapid development of science and technology and the fierce development of market competition have made the importance of performance management increasingly prominent in corporate management. The purpose of performance management is to explore and deal with some of the effects of various factors on employee performance and to tap the potential of employees, improve employee performance, and also bring a qualitative leap to the performance of the organization. The improvement of the employee performance management level has laid a solid foundation for the improvement of the organizational performance management level. However, there are still some difficulties in the implementation of performance management in my country at this stage, and the management effect is not obvious. Therefore, building a scientific, reasonable, and complete multiobjective optimization-based corporate performance optimization management decision-making and coordination mechanism is the primary task of today’s enterprises. This article will give a brief theoretical overview of the combination of system management theory and behavior management theory, MBO target management, and KPI indicators, build a multiobjective optimization model on an effective theoretical basis, and use genetic algorithms to obtain a weak Pareto effective solution that can optimize the enterprise with consideration of performance appraisal indicators. It also builds an agency model and an analysis of employee incentive plans, which clearly shows the relationship between the company and the management and employees, conducts a cross analysis of the needs of the company’s management and employees, and puts forward the best corporate performance considering the needs of employees; among them, the multiobjective optimization of corporate performance increased by 14% under the optimal management decision.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Performance, from the perspective of management, is the desired result of the organization and the effective output that the organization displays on different levels to achieve its goals. It includes both personal performance and corporate performance. The realization of enterprise performance should be based on the realization of individual performance, but the realization of individual performance does not necessarily guarantee the overall performance. Because of this, performance management is even more important in the optimization and improvement of the overall performance of an enterprise.

The multiobjective optimization problem is to select the optimal solution according to a certain index from all possible alternatives of a problem. Mathematically speaking, optimization is the study of the minimization or maximization of a function on a given set S. Broadly speaking, optimization includes mathematical programming, graphs and networks, combinatorial optimization, inventory theory, decision theory, queuing theory, and optimal control. In a narrow sense, optimization is only exponential planning. The optimization method is widely used in production management, economic planning, engineering design, system control, and other fields.

The heterogeneous cloud system proposed by Liu provides distributed but tightly integrated services that have rich functions in large-scale management, reliability, and fault tolerance. As far as big data processing is concerned, newly built cloud clusters are facing performance optimization challenges, which are focused on faster task execution and more efficient use of computing resources. The currently proposed methods focus on time improvement, that is, shorten the MapReduce time, but pay little attention to storage usage. However, an unbalanced cloud storage strategy may exhaust the MapReduce cycle heavy nodes and further challenge the security and stability of the entire cluster. This paper proposes an adaptive method for space-time efficiency in heterogeneous cloud environments. A prediction model based on an optimized kernel-based extreme machine learning algorithm is proposed to predict job execution time and space occupation faster, thereby simplifying the task scheduling process through multiobjectives. However, it is feasible to use multiobjective optimization methods. The calculations performed in the operation may exceed tens of millions of times [1]. Bandyopadhyay performed empirically studies on the relationship between corporate sector performance and capital structure and macroeconomic environment. He used balance panel data of 1,594 Indian companies in 14 years and found empirical evidence to support assumptions about the relevance of factors such as asymmetric information, agency costs, trade-off theory, signals, and liquidity in determining the corporate capital. The structural decisions of emerging market economies obviously affect the company's financing decisions through the macroeconomic cycle, which in turn affects performance. The endogenity between capital structure and company performance has also been resolved by the two-step dynamic panel generalized method of moments (GMM). The research shows that the performance of any company depends on its ability to operate on the capital structure. As the scope of capital procurement expands, it is necessary to carefully design the right tool combination to optimize the cost of capital. However, he did not propose a management decision-making plan or system mechanism for the optimization of company performance [2]. Blahová investigated the current trends in the selected management systems and analyzed the synergies between them to regain control of contemporary corporate performance management systems in the business field. Design/Methodology/Methods. This research involves the compilation of major academic works and other literature on changes in global management systems and their impact on the reconstruction of contemporary corporate performance management systems. The literature is reviewed using a systematic approach. It identified and analyzed more than 3000 papers and studies. Once the survey results are determined, the main trends and emerging themes of current management practices in the business world and their synergies should be classified. The field of originality/value-performance management system and its remake based on the needs of individual companies is an emerging research field. There are still shortcomings in research experience and research empirical [3].

The innovations of this article are as follows: (1) this article uses a combination of empirical research and normative research, such as the research on the employee incentive and restraint mechanism of the Y enterprise in the fourth part; (2) this article uses a combination of qualitative research and quantitative analysis. This method is concentrated in the analysis of the multiobjective optimization model; (3) this article uses a combination of theoretical analysis and countermeasure research and gives countermeasures, while establishing model analysis. This method runs through this article always.

2. Enterprise Performance Optimization Management Decision-Making and Coordination Mechanism Method Based on Multiobjective Optimization

2.1. Combine Contemporary System Management Theory with Behavior Management Theory

Modern management theory is the synthesis of all modern management theories. It is a knowledge system and a group of disciplines [4]. Its basic goal is to establish a creative and dynamic adaptive system in the face of a rapidly changing modern society. To enable this system to be continuously and efficiently output, it not only requires modern management thinking and management organization but also modern management methods and means to form modern management science.

The foundation of behavioral science management theory is classical management theory, which overcomes the shortcomings of classical management theory. It is mainly to study the production behaviors of enterprise employees and the reasons and related factors of these behaviors [5]. It is a comprehensive application of psychology, sociology, social psychology, anthropology, economics, political science, history, law, education, psychiatry, marketing, and management theories and methods to study human behavior borderline subjects. Behavioral science was once called interpersonal relations [6].

Its research content is mainly in the following three aspects: incentive theory is the core content of behavioral science. Specifically, it needs to be carried out in three aspects: level theory, behavioral transformation theory, and process analysis theory; group behavior theory is the core of behavioral science management theory. An important pillar, mastering group psychology is an important part of the study of group behavior; leadership behavior theory is an important part of behavioral science management theory, including research on the quality of leaders, leadership behavior, leadership ontology, and leadership styles [7, 8].

Throughout the management of each school, choose system management theory and behavioral science management theory and draw something in common, and the commonalities can be summarized as follows:

(1) Pay attention to the integrity of the system: first of all, the people, things, and environment in the enterprise are regarded as a complete system, which in turn lives in a larger system. Then, use system thinking and system analysis method to view the problem from the overall framework, recognize the problem, analyze the problem, and finally solve and deal with the problem [6].

(2) Emphasize the importance of people: the core part of management theory is the subject of people. People are subjectively active. Everyone’s thoughts, behaviors, language, and emotional expressions are different. Even in the same organizational department, there is no guarantee that everyone and the organization will move forward. The direction and pace are the same. Therefore, it is necessary to conduct business management, guide employees and the company to stand on the same side, and work together to face difficulties, overcome challenges, and complete tasks.

(3) Actively try advanced management theories first: the changes of society and enterprises, the rapid development speed, and traditional management theories can no longer keep up with the new development model of modern enterprises, so in the process of management theory innovation, we need to continue to try, explore, and accept the emergence of new things and development, for example, in corporate performance management, combining management methods with emerging technologies to improve management efficiency.

(4) Combine management efficiency and management effect: different from traditional enterprise management, modern management theory emphasizes that the management efficiency of the enterprise should be combined with the management effect of the organization. The goals or objectives of management show a diversified trend, which are to realize the performance management of the enterprise [9]. The core of performance management has three modules, as shown in Figure 1.

(5) Combination of theory and practice: talking about management theory is only on paper. It is necessary to combine theory and practice to make the theory operability and implementability so that the theory is useful. In the process of corporate performance management, the management should be good at applying new theories to practice, summarize, innovate, and promote the development of management [10, 11].

(6) Emphasize the predictability in advance: social development is changing rapidly, and the business environment is also constantly changing. If an enterprise can adapt to changes in the objective environment, it must have certain planning capabilities and feedforward control capabilities, accept the facts of upgrading, and ensure the normal operation of enterprise operations and management.

(7) Be brave in innovation: management not only includes the management of existing people, things, and the environment but also includes the management of predictable content in the future. It is necessary to actively reform and innovate and constantly pursue progress so that the development of the company can be “unchanged in response to changes” and continuous development [12].

[figure omitted; refer to PDF]

It can be seen from Figure 3 that the weak Pareto solution obtained by the legacy algorithm is some irregular discrete points. According to their dense and sparse degree, the left side can be appropriately regarded as a smooth curve, and the right side is more sparse.

This paper randomly selects 5 groups of weak Pareto effective solutions (see Table 2) corresponding to the distribution ratio and satisfaction curve shown in Figures 4 and 5. The overall satisfaction level in Table 2 is balanced, and the square sum of the satisfaction difference shows signs of fluctuation.

Table 2

Five groups of weak Pareto effective solutions and related parameters.

	Total satisfaction	Sum of squares of satisfaction difference	$\epsilon$	X1	X2
First group	60.05	3.51	0.50	169.50	351.46
Second group	61.24	13.72	0.50	23.36	126.44
Third group	61.58	20.36	0.49	16.97	13.64
Fourth group	61.74	30.15	0.56	15.49	3.72
Fifth group	61.76	32.49	0.58	15.45	3.66

[figure omitted; refer to PDF][figure omitted; refer to PDF]

Observing Figures 4 and 5, we can see that, in the face of randomly selected weakly effective Pareto solutions, the overall satisfaction and distribution ratio of the research subject are only slightly different, which can be approximated as a coincident curve. Therefore, in the process of performance management, managers adopt a multiobjective optimization model and only need to select a set of weak Pareto solutions arbitrarily to achieve optimal performance decision-making and coordination.

In addition, in order to fully consider the rigor of the experiment, the weak Pareto frontiers at different confidence levels are given. As shown in Figure 6, the changing trends of the weak Pareto frontiers at the five different confidence levels are basically the same, that is, different confidence levels affect the results. The impact is small.

[figure omitted; refer to PDF]

It can be seen from Table 4 and Figure 8 that when many companies analyze the needs of employees and formulate incentive policies, they often rely on the subjective assumptions of the operators. Due to the differences in the status and division of labor between operators and employees, there will always be some differences in their grasp of real needs. The incentive measures formulated by the operators for employees based on their own perceptions do not address the real needs of the employees, so there is no incentive. On the contrary, due to the complexity of the specific situation of enterprise groups, one enterprise can refer to the situation of another enterprise to formulate an incentive mechanism, but it must not be copied. The needs of employees of different companies vary greatly. For example, employees of state-owned enterprises usually regard the enterprise as the support of themselves and their families and have a strong sense of dependence. They yearn for stable work, proper medical care, housing, and childcare. In contrast, do they have to take it? High wages are not as important as the above factors. The employees of high-tech companies require high salaries, more knowledge and skills, promotion, etc. It does not matter whether the job is stable or not. As long as they have the ability and knowledge, they can find better job opportunities at any time. The age of employees also affects their needs. Generally speaking, 18 to 28 years old employees have less family burdens, have full enthusiasm for work, and are not sure about their self-reliance. Therefore, they prefer to be appreciated by the leader and have a harmonious team relationship and desire to have a lot of exercise and training opportunities to increase their abilities. The one-year-old Peng’s employees are in the period of forming families, having children, and, at the same time, eager to have better job prospects in the career. Therefore, he has a door to demand high salaries and generous benefits to ensure the needs of the family such as promotion, corporate growth, and a good working environment to achieve their career pursuits and ambitions. For employees over 48 years old, their children can already support themselves. They can and are willing to devote time to work, especially some older employees hope that through happy work, they can give full play to their abilities. I also want to get a high salary and good benefits, but when my income is not high, I can also maintain good labor relations and team relations and can be recognized and praised by the leaders for my work. It can also stimulate their enthusiasm. Kemen can even categorize the age group more carefully and consider the actual situation of employees more comprehensively, and then, more specific characteristics of needs can be derived. According to the understanding of super Y theory, people are different from each other, and each person's needs are different.

In summary, the business managers should first start from the actual situation of the employees, understand the real needs of the employees of the company through careful observation and care, and then formulate incentive and restraint mechanisms in a targeted manner.

5. Conclusions

This paper mainly studies the decision-making and coordination mechanism of enterprise performance optimization management based on multiobjective optimization. Based on system management theory and performance management theory, through constructing the multiobjective optimization model and agency model, using the genetic algorithm, the Pareto effective solution is obtained to help enterprise performance management make optimal decision-making and coordination. By analyzing the relationship between the company and the management and the company and its employees, multiobjective optimization is carried out to promote the healthier development of the company and its employees.

The innovation of this article is that this article uses a combination of empirical research and normative research, such as the research on the employee incentive and restraint mechanism of the Y enterprise in the fourth part; it uses a combination of qualitative research and quantitative analysis, and this method has been reflected in the analysis of multiobjective optimization models; the method of combining theoretical analysis and countermeasure research is used, and countermeasure suggestions are given, while establishing model analysis. This method runs through this article.

The research in this paper still has shortcomings: the data samples selected in this paper are small, and the research results need to be more comprehensively considered. The representativeness of the samples needs to be evaluated; the selection of target parameters has certain limitations, and the research subjects of the samples are subjective and not conducive to the objective and fairness of the research results. The research in this article has certain guidance and practical significance for the optimal choice of corporate performance. This research topic will help the company to develop more sustainably and healthy in the long run.

References

[1] L. A. Orozco, J. Vargas, R. Galindo-Dorado, "Trends on the relationship between board size and financial and reputational corporate performance," European Journal of Management and Business Economics, vol. 27 no. 2, pp. 183-197, DOI: 10.1108/ejmbe-02-2018-0029, 2018.

[2] H. K. Kam, Y. J. Shin, "The effect of working capital management on corporate performance," Journal of the Korea Academia-Industrial Cooperation Society, vol. 17 no. 6, pp. 173-180, DOI: 10.5762/kais.2016.17.6.173, 2016.

[3] F. Laghari, Y. Chengang, "Investment in working capital and financial constraints," International Journal of Managerial Finance, vol. 15 no. 2, pp. 164-190, DOI: 10.1108/ijmf-10-2017-0236, 2019.

[4] N. Aubert, H. Ben Ameur, G. Garnotel, J.-L. Prigent, "Optimal employee ownership contracts under ambiguity aversion," Economic Inquiry, vol. 56 no. 1, pp. 238-251, DOI: 10.1111/ecin.12478, 2018.

[5] M. Madanoglu, E. Karadag, "Corporate governance provisions and firm financial performance," International Journal of Contemporary Hospitality Management, vol. 28 no. 8, pp. 1805-1822, DOI: 10.1108/ijchm-09-2014-0470, 2016.

[6] S. J. Lin, "Hybrid kernelized fuzzy clustering and multiple attributes decision analysis for corporate risk management," International Journal of Fuzzy Systems, vol. 19 no. 3,DOI: 10.1007/s40815-016-0196-7, 2016.

[7] R. Reskino, "Zakat and islamic corporate social responsibility: does it take effect to the performance of shari'a banking?," Shirkah Journal of Economics and Business, vol. 1 no. 2, pp. 161-184, 2016.

[8] T. Verheyden, R. G. Eccles, A. Feiner, "ESG for all? The impact of ESG screening on return, risk, and diversification," Journal of Applied Corporate Finance, vol. 28 no. 2, pp. 47-55, 2016.

[9] V. Nanda, B. Onal, "Incentive contracting when boards have related industry expertise," Journal of Corporate Finance, vol. 41,DOI: 10.1016/j.jcorpfin.2016.08.014, 2016.

[10] G. Cokins, "Enterprise performance management (EPM) and the digital revolution," Performance Improvement, vol. 56 no. 4, pp. 14-19, DOI: 10.1002/pfi.21698, 2017.

[11] C. Wells, A. Farhat, C. Richardson, "A vine copula-GARCH approach to corporate exposure management," The Journal of Risk, vol. 20 no. 2, pp. 27-51, 2017.

[12] W. Liu, S. Niu, H. Xu, "Optimal planning of battery energy storage considering reliability benefit and operation strategy in active distribution system," Journal of Modern Power Systems and Clean Energy, vol. 5 no. 2, pp. 177-186, DOI: 10.1007/s40565-016-0197-4, 2017.

[13] Q. Liu, W. Cai, J. Shen, Z. Fu, X. Liu, N. Linge, "A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment," Security and Communication Networks, vol. 9 no. 17, pp. 4002-4012, DOI: 10.1002/sec.1582, 2016.

[14] Z. Fei, B. Li, S. Yang, C. Xing, H. Chen, L. Hanzo, "A survey of multi-objective optimization in wireless sensor networks: metrics, algorithms, and open problems," IEEE Communications Surveys & Tutorials, vol. 19 no. 1, pp. 550-586, DOI: 10.1109/comst.2016.2610578, 2017.

[15] M. Li, S. Yang, X. Liu, "Pareto or non-pareto: Bi-criterion evolution in multiobjective optimization," IEEE Transactions on Evolutionary Computation, vol. 20 no. 5, pp. 645-665, DOI: 10.1109/tevc.2015.2504730, 2016.

[16] M. Hamdy, A.-T. Nguyen, J. L. M. Hensen, "A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems," Energy and Buildings, vol. 121 no. 6, pp. 57-71, DOI: 10.1016/j.enbuild.2016.03.035, 2016.

[17] R. Saborido, A. B. Ruiz, J. D. Bermúdez, E. Vercher, M. Luque, "Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection," Applied Soft Computing, vol. 39 no. 2, pp. 48-63, DOI: 10.1016/j.asoc.2015.11.005, 2016.

[18] Y. Boada, G. Reynoso-Meza, J. Picó, "Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case," BMC Systems Biology, vol. 10 no. 1,DOI: 10.1186/s12918-016-0269-0, 2016.

[19] N. Gupta, A. Bari, "Fuzzy multi-objective optimization for optimum allocation in multivariate stratified sampling with quadratic cost and parabolic fuzzy numbers," Journal of Statal Computation & Simulation, vol. 87 no. 10–12,DOI: 10.1080/00949655.2017.1332195, 2017.

[20] H.-S. Kang, Y.-J. Kim, "A study on the multi-objective optimization of impeller for high-power centrifugal compressor," International Journal of Fluid Machinery and Systems, vol. 9 no. 2, pp. 143-149, DOI: 10.5293/ijfms.2016.9.2.143, 2016.

[21] L. Yu, Z. Yang, L. Tang, "Prediction-based multi-objective optimization for oil purchasing and distribution with the NSGA-II algorithm," International Journal of Information Technology & Decision Making, vol. 15 no. 2, pp. 423-451, DOI: 10.1142/s0219622016500097, 2016.

[22] T. Chen, K. Li, R. Bahsoon, "FEMOSAA: feature guided and knee driven multi-objective optimization for self-adaptive software at runtime," ACM Transactions on Software Engineering and Methodology, vol. 27 no. 2, pp. 51-55, DOI: 10.1145/3204459, 2018.

[23] Y. Li, Q. Ye, A. Liu, "Seeking urbanization security and sustainability: multi-objective optimization of rainwater harvesting systems in China," Journal of Hydrology, vol. 550, pp. 42-53, DOI: 10.1016/j.jhydrol.2017.04.042, 2017.

[24] C. Prakash, H. K. Kansal, B. S. Pabla, S. Puri, "Multi-objective optimization of powder mixed electric discharge machining parameters for fabrication of biocompatible layer on β -Ti alloy using NSGA-II coupled with Taguchi based response surface methodology," Journal of Mechanical Science and Technology, vol. 30 no. 9, pp. 4195-4204, DOI: 10.1007/s12206-016-0831-0, 2016.

[25] A. Subasi, B. Sahin, I. Kaymaz, "Multi-objective optimization of a honeycomb heat sink using Response Surface Method," International Journal of Heat and Mass Transfer, vol. 101 no. 2, pp. 295-302, DOI: 10.1016/j.ijheatmasstransfer.2016.05.012, 2016.