Multi-attribute Decision-making Model Research Articles

A TWO-STAGE MATHEMATICAL PROGRAMMING MODEL FOR DISTRIBUTED PHOTOVOLTAIC PROJECT PORTFOLIO SELECTION WITH INCOMPLETE PREFERENCE INFORMATION

With the rapid growth of the solar photovoltaic (PV) market, many distributed PV power projects are introduced to the market. Selecting a rational project investment portfolio is a complex and challenging task for energy enterprises as both financial and non-financial factors of projects are needed to be considered under limited information and resources. This study presents a two-stage hybrid multi-attribute decision-making and integer programming model for distributed PV project portfolio selection. In Stage I, a multiple attribute group decision-making method based on mathematical programming is used to evaluate the non-financial value of projects under incomplete preference information. Compensative weighted averaging operators with an adjustable parameter are utilized to capture the subjective attitudinal character of an expert in the aggregation process. Then, a rank acceptability index is developed to measure each project’s group support degree in non-financial dimension. In Stage II, a bi-objective integer programming model is constructed to optimize project portfolios, which considers both financial and non-financial values of projects under resource, carbon emission and other strategic constraints. The applicability and effectivity of the proposed approach are demonstrated by a case study of a distributed PV project portfolio selection.

Spatial evaluation of groundwater quality based on toxicological indexes and their effects on ecology and human health

Groundwater quality is affected by numerous uncertain factors, and the evaluation process is liable to randomness and fuzziness. In order to evaluate the groundwater quality effectively and know its potential risks to ecology and human health. Taking 87 wells and 174 groundwater samples during wet and dry seasons in Mihe-Weihe River Basin as the research object, samples were collected randomly in the field and sent to the laboratory for instrument testing. Four toxicological indexes (NO2−, NO3−, F−, As) were selected, and a multi-attribute decision-making model was proposed with three dimensions. First, a subjective method based on T-spherical fuzzy theory coupling with TOPSIS and an objective method based on rough set theory were adopted to calculate weights, both of which are recombined by game theory. On this basis, the single-index unascertained measurement function was adopted to classify water quality in two dimensions. Moreover, for the vertical direction, a model based on the VIKOR method was built. Consequently, a three-dimensional water quality index (TWQI) was used to assess the water quality level in three dimensions. The results show that the groundwater quality is classified as medium (classification Ⅲ) in the Mihe-Weihe River Basin, and samples with poor water quality (classification Ⅳ) are mainly distributed downstream of the Mihe River, with groundwater depth ranging from 0 to 15 m, near Laizhou Bay. Ecological and health risk assessment (EHRA) shows that the potential risks to ecology and human health are classified as high, with nitrates and nitrites being the major contributors. Moreover, the risk in the wet season is greater than that in the dry season. This highlights an urgent need to improve groundwater quality in the Mihe-Weihe River Basin, to reduce the potential risks to ecology and human health caused by groundwater pollution.

Fuzzy Distribution Sets

We introduce a new type of fuzzy set called Fuzzy Distribution Set (FDS). Fuzzy distribution sets are fuzzy sets defined on a finite domain subject to a sum of membership values equal to 1. Such fuzzy sets can serve as models of subjective probability distributions and subjective weight distributions. Considering these distributions as fuzzy sets gives a possibility to extend on such distributions the operations of fuzzy sets and, more generally, the calculus of fuzzy restrictions developed during the last decades. Recently Yager introduced the concept of negation of probability distributions. In our works, we studied several classes of such negations. Here we consider an involutive negation of probability distributions as a complement of FDS. We introduce the operations of union and intersection of fuzzy distribution sets. These basic operations on FDS can serve as a basis for the application of fuzzy logic methods to subjective probability and weight distributions. These operations can be used for the development of reasoning models with subjective probability distributions and subjective weighting functions. Weight distributions can be used in multi-criteria, multi-person, and multi-attribute decision-making models.

Evaluation of the pharmaceutical distribution and warehousing companies through an integrated Fermatean fuzzy entropy-WASPAS approach

PurposePharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.Design/methodology/approachTo achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.FindingsThe feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.Practical implicationsThe proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.Originality/valueA new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.

Smart Sharing Plan: The Key to the Water Crisis

Over the years, the Colorado River has become inadequate for development due to natural factors and human activities. The hydroelectric facilities in Lake Mead and Lake Powell are also not fully utilized. Downstream, Mexico is also involved in the competition for water. The resulting allocation of water and electricity resources and sustainable development are hanging over our heads and waiting to be solved. In this work, a simplified Penstock Dam model and a Distance Decay model are designed based on publicly available data, and a Multi-attribute Decision model for hydropower based on the Novel Technique for Order Preference by Similarity to an Ideal Solution method is proposed. In addition, an Improved Particle Swarm Optimization model is proposed by adding oscillation parameters. The Mexican equity problem is also explored. The theoretical results show that the average error of the Penstock Dam model is 3.2%. The minimum water elevation requirements for Lake Mead and Lake Powell are 950 ft and 3460 ft, respectively; they will not meet demand in 2026 and 2027 without action, and they will require the introduction of 3.69×1010 m3 and 2.08×109 m3 water in 2027 and 2028, respectively. The solution shows that the net profit for the United States is greatest when 38.6% of the additional water is used for general purposes, 47.5% is used for power generation, and the rest flows to Mexico. A final outlook on the sustainability of the Colorado River is provided.

Plant species selection by hybrid multiple-attribute decision-making model for promoting green mining in the Sungun copper mine, Iran.

Adopting the most suitable plant species selection is a multi-dimensional problem. Many parameters affect judges' decisions. Accordingly, the present study aimed to develop a multi-attribute platform for plant species selection consisting of parameters such as aesthetic outlook, resistance in front of insects, plant disease resistance, economic efficiency, pollution prevention, erosion reduction, and growth rate. The plant species selection was performed according to the primary factors. Along with the priorities mentioned above, a multi-attribute decision-making (MADM) model was presented to define the selected species based on the secondary factors. This study used two methods (Entropy and AHP) to attribute weighting because plant species selection is highly case sensitive, and global weighting was fundamental. Therefore, attribute weighting was calculated by two objective and subjective methods, respectively. Then, the ELECTRE method was applied for ranking plant species in acidic and alkaline soil types in the Sungun copper mine of Iran. This case study results showed that Acer campestre, Robinia pseudoacacia, Juniperus communis, Betula pendula, Ulmus minor, and Rhus coriria had more priority in acidic soil type, respectively. Similarly, Juglans regia was the best type for alkaline soil, and either Ficus carica or Fraxinus excelsior is located in the following ranking. When the number of possible options was more significant, the outranking result taken by the ELECTRE method was more reliable.

Development of a Relative Similarity Degree Based Engineering Construction Multi-Attribute Decision Model and Its Application.

Generally, there are large amounts of uncertain factors in the multi-attribute decision system. By using the gray relational degree and fuzzy gray relational degree, the weights of the comprehensive indexes are extracted. Then, a novel decision model is established based on the concept of relative similarity degree. Finally, comparative research is carried out taking the maritime safety engineering construction in Hunan Province, China, as an example to verify that the developed model is rather effective and practical for its high resolution and sensitivity in multi-attribute decision.

Evaluation of the government entrepreneurship support by a new dynamic neutrosophic operator based on time degrees

PurposeEntrepreneurship as a development engine has a distinct character in the economic growth of countries. Therefore, governments must support entrepreneurship in order to succeed in the future. The best way to improve the performance of this entrepreneurial advocacy is through efficient measurement methods. For this reason, the purpose of this paper is to propose a new integrated dynamic multi-attribute decision-making (MADM) model based on neutrosophic set (NS) for assessment of the government entrepreneurship support.Design/methodology/approachDue to the nature of entrepreneurship issues, which are multifaceted and full of uncertain, indeterminate and ambiguous dimensions, this measurement requires multi-criteria decision-making methods in spaces of uncertainty and indeterminacy. Also, due to the change in the size of indicators in different periods, researchers need a special type of decision model that can handle the dynamics of indicators. So, in this paper, the authors proposed a dynamic neutrosophic weighted geometric operator to aggregate dynamic neutrosophic information. Furthermore, in view of the deficiencies of current dynamic neutrosophic MADM methods a compromised model based on time degrees was proposed. The principle of time degrees was introduced, and the subjective and objective weighting methods were synthesized based on the proposed aggregated operator and a nonlinear programming problem based on the entropy concept was applied to determine the attribute weights under different time sequence.FindingsThe information of ten countries with the indicators such as connections (C), the country's level of education and experience (EE), cultural aspects (CA), government policies (GP) and funding (F) over four years was gathered and the proposed dynamic MADM model to assess the level of entrepreneurial support for these countries. The findings show that the flexibility of the model based on decision-making thought and we can see that the weights of the criteria have a considerable impact on the final evaluations.Originality/valueIn many decision areas the original decision information is usually collected at different periods. Thus, it is necessary to develop some approaches to deal with these issues. In the government entrepreneurship support problem, the researchers need tools to handle the dynamics of indicators in neutrosophic environments. Given that this issue is very important, nonetheless as far as is known, few studies have been done in this area. Furthermore, in view of the deficiencies of current dynamic neutrosophic MADM making methods a compromised model based on time degrees was proposed. Moreover, the presented neutrosophic aggregation operator is very suitable for aggregating the neutrosophic information collected at different periods. The developed approach can solve the several problems where all pieces of decision information take the form of neutrosophic information collected at different periods.

BAG-DSM: A Method for Generating Alternatives for Hierarchical Multi-Attribute Decision Models Using Bayesian Optimization

Multi-attribute decision analysis is an approach to decision support in which decision alternatives are evaluated by multi-criteria models. An advanced feature of decision support models is the possibility to search for new alternatives that satisfy certain conditions. This task is important for practical decision support; however, the related work on generating alternatives for qualitative multi-attribute decision models is quite scarce. In this paper, we introduce Bayesian Alternative Generator for Decision Support Models (BAG-DSM), a method to address the problem of generating alternatives. More specifically, given a multi-attribute hierarchical model and an alternative representing the initial state, the goal is to generate alternatives that demand the least change in the provided alternative to obtain a desirable outcome. The brute force approach has exponential time complexity and has prohibitively long execution times, even for moderately sized models. BAG-DSM avoids these problems by using a Bayesian optimization approach adapted to qualitative DEX models. BAG-DSM was extensively evaluated and compared to a baseline method on 43 different DEX decision models with varying complexity, e.g., different depth and attribute importance. The comparison was performed with respect to: the time to obtain the first appropriate alternative, the number of generated alternatives, and the number of attribute changes required to reach the generated alternatives. BAG-DSM outperforms the baseline in all of the experiments by a large margin. Additionally, the evaluation confirms BAG-DSM’s suitability for the task, i.e., on average, it generates at least one appropriate alternative within two seconds. The relation between the depth of the multi-attribute hierarchical models—a parameter that increases the search space exponentially—and the time to obtaining the first appropriate alternative was linear and not exponential, by which BAG-DSM’s scalability is empirically confirmed.

Grey Multiattribute Emergency Decision-Making Method for Public Health Emergencies Based on Cumulative Prospect Theory

In view of the different risk preferences of decision-makers and the information gap in the timing of the subjects under evaluation, a multiattribute dynamic decision-making model that considers decision risk in addition to the priority of time is proposed. First, to account for the uncertainty of the evaluation information, the interval grey number method was applied to perform numerical abstraction and key data feature extraction on the scene of public health emergencies and to obtain the information after attributes were unified. Second, acknowledging the bounded rational behavior of actual decision-makers, the decision-makers’ risk preference was introduced into emergency decision-making. In combination with the time degree, the multiattribute unified decision matrix was transformed into an expected dynamic cumulative prospect decision matrix. Furthermore, based on cumulative prospect theory (CPT), the prospect value function and the weight function are constructed; a new optimization model is proposed to obtain the attribute weight. And according to the comprehensive prospect value of each alternative, the priority of the alternatives was determined. Finally, with the COVID-19 epidemic serving as an example, the results confirm that the emergency decision-making method proposed in this paper is feasible and effective.

A New Method for Reconstructing Data Considering the Factor of Selected Provider Nodes Set in Distributed Storage System

In the distributed storage system, when data need to be recovered after node failure, the erasure code redundancy method occupies less storage space than the multi-copy method. At present, the repair mechanism using erasure code to reconstruct the failed node only considers the improvement of link bandwidth on the repair rate and does not consider the impact of the selection of data providing node-set on the repair performance. A single node fault data reconstruction method based on the Software Defined Network (SDN) using the erasure code method is designed to solve the above problems. This method collects the network link-state through SDN, establishes a multi-attribute decision-making model of the data providing node-set based on the node performance, and determines the data providing nodes participating in providing data through the ideal point method. Then, the data recovery problem of a single fault node is modeled as the optimization problem of an optimal repair tree, and a hybrid genetic algorithm is designed to solve it. The experimental results show that under the same erasure code scale, after selecting the nodes of the data providing node-set, compared with the traditional tree topology and star topology, the repair delay distribution of the designed single fault node repair method for a distributed storage system is reduced by 15% and 45% respectively, and the repair flow is close to the star topology, which is reduced by 40% compared with the traditional tree repair.

An optimization viewpoint on evaluation-based interval-valued multi-attribute three-way decision model

Multi-attribute decision-making (MADM) aims to rank alternatives based on their attributes and evaluation information, which provides decision support for decision-makers. Most existing MADM methods can only obtain ranking results, decision-makers usually need to subjectively choose priority alternatives to make a decision based on the preset evaluation level and ranking results, which does not meet the requirements of complex decision situations and uncertain information processing. A method is urgently needed, which can objectively produce classification results and automatically provide priority objects for decisions. Three-way decisions (3WDs) offer an effective research technique to solve decision-making problems under uncertainty and risk by objectively dividing a universal set into three pairwise disjoint parts: acceptance, non-commitment and rejection, and implementing the corresponding strategy for each part. Considering that it is difficult to accurately determine the exact value of attributes in some cases, interval numbers can be a useful concept to flexibly describe uncertain information and satisfy the decision-maker’s cognition. Therefore, under an interval-valued MADM environment, this paper proposes an evaluation-based interval-valued multi-attribute three-way decision (IVMA3WD) model from an optimization viewpoint. The model achieves an effective fusion of 3WDs and MADM problems, and further expands evaluation-based 3WD models on the totally ordered set. Specifically speaking, in order to depict uncertain information in decision-making, interval numbers are used to describe the evaluation information included in the multi-attribute evaluation matrix. Then, considering a general case that there are no decision attributes in the multi-attribute evaluation matrix, we determine the evaluation function by use of the idea of the interval-valued TOPSIS method. In addition, unlike the fixed-valued loss functions in the majority of 3WD models, the relative loss function in this article is not provided straightly but computed by attribute evaluation values expressed by interval numbers. Furthermore, based on an optimization viewpoint, we construct two optimization models and attempt to find the optimal solution to obtain the threshold pair. A housing market investment decision problem is utilized as an illustrative example to find out the applicability of our built IVMA3WD model. In the end, we adopt a sensitivity analysis and a comparative analysis to demonstrate our proposed model’s characteristics and advantages.

Data-driven method to learning personalized individual semantics to support linguistic multi-attribute decision making

In parallel with the development of information and network technology, large amounts of data are being generated by the Internet, and data-driven methodologies are now often being used in decision-making. Recent studies have investigated personalized individual semantics (PIS) in various decision-making contexts to model a fact that words mean different things to different people. However, few studies have investigated PIS in the context of multi-attribute decision-making (MADM). In MADM, in addition to multi-attribute linguistic information, pre-existing classification of the alternatives is always present, which have not been considered in prior research. Most previous studies have simply demonstrated the feasibility of PIS methods with numerical examples using small-scale models, and not with realistic datasets. Therefore, in this study, we propose a data-driven learning model to analyze the PIS of decision makers to support a multi-attribute decision-making model that considers pre-existing classification of the alternatives. Specifically, we first propose a PIS multi-attribute learning function to define a general computation form for comprehensive evaluation of the value of alternatives. Then, considering this pre-existing classification of the alternatives, a PIS learning model is constructed by analyzing the relations between calculated values of alternatives and corresponding class assignments to obtain personalized numerical scales of linguistic terms for a decision maker. Finally, we present a case study based on two datasets and a comparison with other methods to justify the feasibility of the proposed model.

Selection of an Appropriate Solar Thermal Technology for Solar Vapor Absorption Cooling—An MADM Approach

Different solar thermal collectors have been used recently to meet the thermal requirements of single effect and double effect vapor absorption cooling systems, making selection of the correct method a challenge. Different attributes of each vapor absorption cooling and solar collector combination are taken into account, and a Multi-Attribute Decision-Making model is used to select the best option. The model requires variables indicating the performance of the system, which are called attributes that are organized into a hierarchical structure called a tree of attributes. The dependent attributes finally end up in the basic attributes representing the input to the model. The technology options considered are flat plate water heating collectors with forced circulation, flat plate air heating collectors with forced circulation, evacuated tube collectors, parabolic trough collectors, and collectors with compound parabolic concentrator. Two types of cooling, namely single effect and double effect, are compared. Three climatic zones with three different cooling loads, ambient temperatures and solar radiation intensity have been considered. Comparison of the solar vapor absorption technologies with vapor compression technology is made considering the major performance factors. The major attributes, namely cost and emissions produced, are compared with conventional vapor compression methods.

A Multi-Attribute Decision-Making Model for the Selection of Polymer-Based Biomaterial for Orthopedic Industrial Applications.

The potential of quantifying the variations in IR active bands was explored while using the chemometric analysis of FTIR spectra for selecting orthopedic biomaterial of industrial scale i.e., ultra-high molecular weight PE (UHMWPE). The nano composites UHMWPE with multi-walled carbon nano-tubes (MWCNTs) and Mg-silicate were prepared and irradiated with 25 kGy and 50 kGy of gamma dose. Principal component analysis (PCA) revealed that first three principal components (PCs) are responsible for explaining the >99% of variance in FTIR data of UHMWPE on addition of fillers and/or irradiation. The factor loadings plots revealed that PC-1 was responsible for explaining the variance in polyethylene characteristics bands and the IR active region induced by fillers i.e., 440 cm−1, 456 cm−1, from 900–1200 cm−1, 1210 cm−1, 1596 cm−1, PC-2 was responsible for explaining the variance in spectra due to radiation-induced oxidation and cross linking, while the PC-3 is responsible for explaining the variance induced because of IR active bands of MWCNTs. Hierarchy cluster analysis (HCA) was employed to classify the samples into four clusters with respect to similarity in their IR active bands which is further confirmed by PCA. According to multi attribute analysis with PCA and HCA, 65 kGy irradiated sample is optimum choice from the existing alternatives in the group of irradiated pristine UHMWPE, UHMWPE/Mg-silicate irradiated with 25 kGy of gamma dose was the optimum choice for UHWMPE/Mg-silicate nano composites, and UHMWPE/γMWCNTs composites containing 1.0% dof γ MWCNTs for UHMWPE/MWCNTs nanocomposites, respectively. The results show the effectiveness of quantifying the variance for decision as far as optimization of biomaterials in orthopedic industrial applications is concerned.

Knowledge-based engineering approach for defining robotic manufacturing system architectures

Robotic manufacturing systems have proven to be an effective solution for modern manufacturing enterprises to deal with increasing in customer demands and market competition. However, these systems may be unable to completely satisfy user requirements because of the difference between user and design perspectives. Thus, designing robotic manufacturing systems requires iterative processes that significantly increase development costs and lead time. A user-customised design approach is needed that enables users to customise robotic manufacturing systems as well as alleviate the burden on designers of eliciting user requirements. However, most users may not be able to customise their systems because of a lack of engineering knowledge. The authors propose a knowledge-based engineering approach to aid users in customising the architectures of robotic manufacturing systems. Two models — an ontological knowledge model and a multi-attribute decision-making model — are defined and integrated in the proposed KBE architecture definition method. A rule-based reasoning process is proposed in the ontological knowledge model based on explicit semantic descriptions of users’ unstructured or semi-structured requirements and the components of robotic manufacturing systems, which infers the possible architecture of the required system. The MADM model is adopted to evaluate the architecture alternatives to determine the optimal solution.

Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model.

Nursing medical histories are copied randomly, leading to the occurrence of incorrectly altered histories, and medical errors are on the rise year by year. Moreover, erroneous falsification of nursing medical history is a challenging issue that needs to be urgently addressed in domestic nursing medical history. In this manuscript, a caregiver association method, which is based on a multiattribute decision model, is proposed which is specifically designed for the caregiver association selection problem in intelligent caregiving decision making. In this mode, through the selection and modeling of key performance indicators and the design of combined weights, the purpose of regulating the priority of attributes is achieved. At the same time, a comprehensive evaluation index is designed to realize the joint optimization of multiple attributes. Simulation results show that the proposed nursing workstation can not only tradeoff the quality of nursing staff experience and system performance but also balance the distribution of nursing work and effectively reduce the association delay and system energy consumption．

Application of the MADM Method for the Admission System for Islamic Religious Trustees

The selection process for Islamic tutor and the decision to pass or fail is made by subjective views of the selector. The konvensional process takes a lot of time. It is necessary to have Decision Support System (DSS) to minimize the subjective side of the selectors and streamline the selection process so that effective decision can be obtained. The character of this problem matches the characteristic of Multi Attribute Decision Making (MADM) model. A combination of Simple Addictive Weighting (SAW) and Technique for Others Reference by Similarity to Ideal Solution (TOPSIS) are selected to solve MADM model. SAW used to rank decision while TOPSIS used to calculate level 2 criteria. Based on testing of 30 data samples using Single Detection Threshold method, the system performance accuracy rate is 83,33% and precision rate is 77%. The combination of the two method has been successfully applied and provide accurate and precise recommendations.

A novel spatiotemporal multi-attribute method for assessing flood risks in urban spaces under climate change and demographic scenarios

The combined effects of global warming and population growth induce policymakers to enhance flood risk management practices in order to reduce substantially the causes of floods and to mitigate their future and multiple impacts simultaneously. This way, this paper puts forward a new multi-attribute and non-stationary decision model for managing flood risks in urban areas under climate demographic changes, which we call the Non-Stationary Multi-Attribute Utility Theory (NSMAUT). Under expected utilities, our model inserts time-dependency into the decision-maker's (DM's) preference statements based on his/her trade-offs regarding psychological distance induced by delayed prospects. The NSMAUT analyzes flood risks under five attributes, linked to environmental, financial, human, mobility, and social concerns of society. By combining climate and demographic scenarios for this century, our findings make use of statistical, graphic, and risk performance measures that increase the DM's perception of risk in order to control and monitor the dynamism of circumstances of risk and thus to facilitate on what he/she should base future decisions on adapting cities for future adverse events arising from flooding. A numerical application in a Brazilian municipality from 2021 – 2100 is used to validate our approach. Moreover, the model can be replicated in other urban contexts.

Artificial Logging or Natural Growth

Global climate change makes forestry carbon sequestration a hot issue. In order to improve the comprehensive benefits of forest management, this paper studies the carbon accounting problem, and uses the forest stock conversion factor method to create a carbon sequestration accounting model based on the reserve transformation method. Then, the HWP carbon sequestration accounting algorithm is obtained after the improvement of the reserve change method and the atmospheric flow method with the HWP half-life as a bridge. Based on the ecological and economic benefits, a multi-objective and multi-attribute decision-making model for forest management plan is constructed, and the optimal strategy of stand structure based on selective cutting is proposed. Finally, the entropy weight TOPSIS method is used to quantitatively analyze the comprehensive benefit value and provide suggestions for forestry departments. To verify the model, we chose the Greater Khingan Mountains forest region as the research site. Through successive iterations of CSAM, we calculate that the forest will absorb 534 million tons of live forest and forest products in 100 years. From the stand structure of the forest area, when the selected cutting intensity is 20% and the selected cutting cycle is 10.7 years, the comprehensive benefit value of the Greater Khingan Mountains is the highest.