Decision-making Model Construction of Enterprise Marketing Outlet Resource Allocation by Integrating Fuzzy Logic

被引:0
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作者
Ma, Xiaoyan [1 ,2 ,3 ]
Xue, Jian [4 ]
机构
[1] School of Economics and Management, Shaanxi University of Science and Technology, Shaanxi, Xi’an,710021, China
[2] Xi’an Siyuan University, Marketing Strategy Research and Consulting Center for Higher Education, Shaanxi, Xi’an,710038, China
[3] Xi’an Siyuan University, Shaanxi, Xi’an,710038, China
[4] School of Economics and Management, Shaanxi University of Science and Technology, University Park of Weiyang District, Shaanxi, Xi’an,710021, China
关键词
In the tide of modern business competition; how to efficiently utilize limited resources to obtain the best economic benefits is the core issue that determines the survival and development of enterprises. This paper integrates fuzzy logic theory with resource allocation and proposes the direction of channel integration of marketing resources. The concept of affiliation degree is introduced in fuzzy logic to describe the relationship between things and form the fuzzy relationship in the fuzzy set; which indicates the degree of association between elements in the fuzzy set. The linguistic rule representation method is used to implement the control strategy for decision-making in enterprise marketing outlets. Improve the construction of the optimization model for enterprise outlets’ marketing resources by establishing a two-layer planning model. The research enterprise’s outlet marketing resources allocation situation is examined by combining actual and simulation; based on the principle of outlet marketing resources allocation. From the point of view of outlet affiliation; the central region with a suitable resource allocation level includes Hunan Province; with an affiliation degree of 0.4655; and the affiliation degrees of some western regions and northeastern regions range from 0.3896 to 0.5663. The fuzzy logic model generates six resource allocation strategies; and they are tested one by one using simulation experiments to determine the best allocation strategy. © 2024 Xiaoyan Ma and Jian Xue; published by Sciendo;
D O I
10.2478/amns-2024-2616
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