Multi-objective Decision-making Model Research Articles

Doctor-patient bilateral matching considering diagnosis and treatment perception in the absence of public health resources.

The public health crisis is one of the main threats affecting the sustainable development of the economy and strengthening the rational allocation of medical resources is essential for building a strong public health system. Therefore, the study of the doctor-patient bilateral matching has important theoretical and practical significance and perception of diagnosis and treatment is taken as a key consideration in the research. Based on the current situation of the medical industry and the main contradiction between supply and demand of medical services, an evaluation index of doctor-patient satisfaction is constructed in this paper. Then, based on the different forms of evaluation, calculate the doctor's satisfaction and patient's satisfaction respectively. Taking maximizing the overall satisfaction of doctors and patients, maximizing the number of patients and minimizing the workload difference between doctors as the decision-making objectives, considering the upper limit of doctors' working hours as the constraint condition, a multi-objective decision-making model is constructed and solved by NSGA-II algorithm to realize the matching between doctors and patients. Finally, through the comparison with NSGA-III algorithm in three dimensions: the degree of convergence to the reference set, the propagation range of the solution and the running time of the algorithm, it is proved that NSGA-II algorithm has good performance in solving the matching problem of medical service supply and demand.

A Multi-Objective Decision Model for Water Pollution Load Allocation under Uncertainty

In order to control the discharge of regional total pollutants in the region and improve the ability of water environment management and decision making, a multi-objective decision-making optimization model of water pollution load allocation was constructed, which took into account economy and fairness. The model takes the maximum environmental benefit and the minimum weighted comprehensive Gini coefficient as the objective function and takes into account the uncertainty and multi-objectives of the model, which is conducive to promoting economic development and ensuring the fairness of regional water pollutant discharge. A method based on Monte Carlo simulation coupled with a genetic algorithm was designed to obtain the optimal solution set through multiple simulation optimization. This model is applied to Anhui Province to solve the allocation optimization problem of total pollutant reduction in the 13th Five-Year Energy Conservation and Emission Reduction Plan. After the optimization of water pollution load distribution, the comprehensive Gini coefficients of COD and NH3-N are reduced by different ranges. The comprehensive Gini coefficient after COD optimization decreased by 2.4–4.6%, and the comprehensive Gini coefficient after NH3-N optimization decreased by 25.1–32.5%, which verified the feasibility and rationality of the model in the optimal allocation of the total discharge of regional water pollutants. The model takes into account uncertain subjective and objective factors that have an important impact on water pollutant discharge targets and decision variables, thus optimizing the total emissions of the entire regional control unit in both space and time.

A Decision-Making Method for Distributed Unmanned Aerial Vehicle Swarm considering Attack Constraints in the Cooperative Strike Phase

In view of the growing military forces of various countries, unmanned aerial vehicle (UAV) swarms, as a new type of weapon, are gradually attracting the attention of more and more countries. Decision-making, as the core link in its application, has also become the focus of research in these countries. In this work, the distributed UAV swarm cooperative strike decision-making problems are separated from the distributed UAV swarm cooperative search strike decision-making problems, and the distributed UAV swarm cooperative strike multiobjective decision-making model is established, with the relevant constraints clarified. Besides, according to the analysis of the motion characteristics of UAV and the speed requirements of the distributed UAV swarm cooperative strike decision, an arc tangent trajectory planning method, conforming to the motion characteristics of UAV, is proposed. Moreover, a distributed cooperative strike decision-making method, based on the idea of “campaign endorsement and invitation cooperation,” is put forward, with the effectiveness and superiority validated by simulation experiments.

A Multi-objective Chance-constrained Information-gap Decision Model for Active Management to Accommodate Multiple Uncertainties in Distribution Networks

The load demand and distributed generation (DG) integration capacity in distribution networks (DNs) increase constantly, and it means that the violation of security constraints may occur in the future. This can be further worsened by short-term power fluctuations. In this paper, a scheduling method based on a multi-objective chance-constrained information-gap decision (IGD) model is proposed to obtain the active management schemes for distribution system operators (DSOs) to address these problems. The maximum robust adaptability of multiple uncertainties, including the deviations of growth prediction and their relevant power fluctuations, can be obtained based on the limited budget of active management. The systematic solution of the proposed model is developed. The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term. Considering the stochastic characteristics and correlations of power fluctuations, the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution. The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network. The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties, which corresponds to an optional active management strategy set for future selection.

Building a Multi-Objective Flexible Optimal Decision Model for Green Supply Chains

Due to climate change, the importance of environmental protection, and the operation of the global supply chain influenced by the pandemic, building a flexible green supply chain model can help enterprises keep operational sustainability and strengthen competitive advantages. However, it can be found from relevant literature that research on green supply chain flexibility is still insufficient. This study aims to fill the research gap, and attempts to develop a multi-objective mixed integer programming model for a flexible green supply chain network design to maximize the profit, the amicable production level. To our knowledge, this proposed model is the first effort to take economic factors, environmental factors, supply flexibility, manufacturing flexibility, distribution flexibility and reverse logistics flexibility into account simultaneously, and can be a reference for supporting effectively management of the green supply chain network design. The research result and findings can be a reference for related academic researches and also can be used to guide the development of a green supply chain model for making better decision.

An improved multi-objective gray target model for project resource allocation selection Research

In response to the traditional method of using a single subjective or objective assignment, AHP (hierarchical analysis) subjective weights and CRITIC (hierarchical relevance of importance criteria method) objective weights are coupled with the Lagrange multiplier method to obtain combined weights, and the multiobjective gray target decision model is used to establish event set, countermeasure set, and situation set to obtain the resource ranking that affects the project resource deployment problem, which provides a basis for the project participants to make decisions and plan for the project. Taking the municipal road project as an example, a judgment matrix is established. The results show that the model constructed in this paper is relatively consistent with the actual situation.

Collaborative Ecological Flow Decision Making under the Bengbu Sluice Based on Ecological-Economic Objectives

The construction of dams destroys the integrity of a watershed system and the continuity of natural water flow, creating a watershed with segmented and fragmented rivers. This, in turn, affects and even destroys the health and stability of the watershed ecosystem. This study selected the downstream area of Bengbu Sluice in the Huai River Basin of China as the study area. To address the increasingly prominent ecosystem degradation in the Huai River Basin, ecological flow thresholds were determined using habitat simulation and hydrological approaches for mutual validation. A multi-objective synergistic decision model incorporating ecological and socioeconomic objectives was developed to coordinate the economic and ecological water use conflicts in the study area. The optimal coordinated solution for the ecological flow of important biological habitats in the basin was determined with the multi-objective synergistic method. The results demonstrated that a coordinated solution could guarantee the ecological and economic water demands of the basin. The findings of this study can be used as a reference for scientific guidelines on future ecological operations in dam-controlled rivers.

Study on Mix Proportion Optimization and Microstructure of Coal-Based Solid Waste (CSW) Backfill Material Based on Multi-Objective Decision-Making Model.

The preparation of underground-backfill material from CSW can be used for large-scale disposal of solid waste. The proportion of backfill material plays an important role in transportation and backfilling effect, and the mix-proportion optimization of backfill material is essentially a multi-factor and multi-objective optimization problem. In this paper, to obtain the mix proportion of backfill materials with optimal comprehensive-evaluation indexes, and suitable for the engineering application, the fluidity and strength of backfill material, mainly composed of coal gangue(CG), fly ash (FA), flue gas desulfurization gypsum (FGD gypsum), and gasification coarse slag (GCS), were tested by single-factor transformation method, and the effects of various solid wastes on the slump-flow, bleeding rate and early strength of backfill material were analyzed. The optimal mix proportion of CSW with the slump-flow, bleeding rate, and 3-day and 7-day strengths as the evaluation indicators is FA: GCS: FGD gypsum: CG = 25%:25%:25%:25%, according to the multi-objective decision model. Furthermore, the comprehensive evaluation index that meets the requirements of mine backfilling is obtained by changing the ordinary portland cement (OPC) content, that is, the optimal OPC content is 10% of the total solid waste, and the mass concentration is 78%. Finally, the pore structure, micromorphology, and composition of the backfill material with the optimal mix proportion were studied by Mercury Intrusion Porosimetry (MIP), X-ray Diffraction (XRD), and Scanning Electron Microscope-Energy Dispersive Spectrometer (SEM-EDS). The research results provide a good reference for the field application of CSW for underground backfilling.

An intelligent retrofit decision-making model for building program planning considering tacit knowledge and multiple objectives

Due to the enormous building stock and high energy consumption of the construction sector, green retrofitting of existing buildings has recently become a critical issue. China has implemented building retrofitting on a large scale based on various norms and standards. In practice, however, the effectiveness of the whole building retrofit program is often jeopardized because some decision-makers are limited by their experience and fail to evaluate the program in its entirety. To overcome this problem, this study offers an intelligent decision support model considering tacit knowledge for program decision-making with conflicting objectives. By comparing several data mining approaches and using 152 retrofitted existing buildings as examples, a tacit knowledge mining model based on the XGBoost algorithm with an accuracy of 73.91% is constructed. The predicted results of the knowledge mining model can be used as the input of the multi-objective decision-making model. In addition, the retrofit cost, thermal insulation requirement, and total retrofit area are chosen as the objectives of the multi-objective decision-making model. Then, the model's applicability to building retrofit programs is tested using five buildings as examples. Finally, the results demonstrate that the proposed model can partially replace experts in supporting policymakers and owners throughout the planning stage.

A chaos-coupled multi-objective scheduling decision method for liner shipping based on the NSGA-III algorithm

Reducing sailing costs, time and emissions are three important decision-making objectives for ship operators. In this paper, a multi-objective decision model for liner shipping is established to optimize ship route, speed and number of ships deployed based on the three objectives. Moreover, the above model also takes into consideration the three latest ship emission reduction policies, including Emission Control Area, the 2020 Sulfur Limit Order and maritime Carbon Emission Taxation regulations. To solve the multi-objective scheduling problem, an improved non-dominated sorting genetic algorithm-III (NSGA-III) algorithm based on a chaos mechanism is proposed to find the Pareto solutions. And then a Multi-Criteria Decision Making (MCDM) method, the Hyperplane-based ranking method (HYRM) is developed to find the trade-off solution from the Pareto solutions. The proposed method was applied to two liner shipping service routes around southeast Asia and eastern United States, the results show that the proposed method can simultaneously reduce sailing costs, time and ship emission according to the users' preferences, and it can help shipping companies cope with fuel price fluctuations effectively.

Multiobjective Decision-Making Model for Power Scheduling Problem in Smart Homes

The aim of this study was to solve power scheduling issues in smart homes to enable demand response in smart grids. The objective of demand response is to match demand with supply by reflecting supply expectations through consumer price signals, and especially to avoid peak demand during times of high prices and when supply is limited. Three objectives were considered: first, economic rationing by minimizing the total costs for consumers with the given hourly prices; second, to achieve better efficiency in terms of supply and greater stability in a power system by reducing peaks in usage or load, which is defined by minimizing the percentage of power rate; third, related to consumer comfort levels, by reducing variance in the schedule of appliances to actual usage periods requested. This multiobjective power scheduling problem for smart homes (PHPSH) was explored using a nondominated sorting genetic algorithm, called NSGA-II. The results showed that the Pareto-optimal solutions from NSGA-II are compatible with the weighted-sum-based model from the literature, and viable alternatives are available for end users with different weighted objectives.

Selection of Landfill Cover Materials Based on Data Envelopment Analysis (DEA)—A Case Study on Four Typical Covering Materials

Against the background of sustainable development, landfill covers can consist of a range of materials, from clay to geocomposite and polymer composites. Given engineering and environmental requirements, we analyzed the performance and sustainability of four sanitary landfill cover materials, namely clay, HDPE, PVC, and GCL. Within the principles of environmentally sustainable design, we constructed a material selection index based on the performance as well as the economic and environmental impacts of the materials. In addition, using a data envelopment analysis (DEA) model with an analytic hierarchical process (AHP) preference cone, we developed a C2WH model to evaluate the performance of the selected materials. Through the calculation, we found that the comprehensive indexes of the four covering materials were E1 = 0.2600, E2 = 0.5757, E3 = 0.7815, and E4 = 1.0000, respectively. Our results indicated that the investigated materials could be ranked according to performance as follows: GCL > PVC > HDPE > clay. Thus, our results showed that GCL, with the highest efficiency value, was the optimal cover of the investigated materials. The multiobjective decision model developed in our study can be used as a technical reference and offers support for the selection of eco-friendly landfill cover materials.

Energy, economic, and environmental (3 E) examinations of the indirect-expansion solar heat pump water heater system: A simulation-oriented performance optimization and multi-objective decision-making

In this paper, a simulation-oriented optimization integrated with the multi-objective decision-making (MODM) model for an indirect-expansion solar-assisted heat pump (IDX-SAHP) water heater is proposed and the energy, economic, and environmental (3 E) aspects of the system are examined. The developed model is verified and its performance is scrutinized in a humid continental climate region of China. Next, the sensitivity analysis is conducted via the one-way analysis of variance (ANOVA) approach. Then, the single-objective optimization (SOO) and multi-objective optimization (MOO) are conducted via the migrating birds optimization (MBO) method to maximize the coefficient of performance (COP) and the collector efficiency (CE), singly and concomitantly. The SOO results indicate the IDX-SAHP design as per the COP-oriented optimization causes the system to operate less for about 120 hours a year in contrast to the system design as per the CE-oriented optimization. Also, the results of MOO integrated with MODM indicate that MOO gives better performance in contrast to the SOOs so that although the CE of the maximized IDX-SAHP reduces a little from 51% to 46%, its COP increases greatly from 2.5 to 7. Therefore, the overall power consumption of the system decreases about 269 hours in contrast to the initial IDX-SAHP. Besides, the environmental examination illustrates the use of IDX-SAHP in Beijing buildings can prevent the burning of 2,877, 000, 000 cubic meters of natural gas per year in the fossil fuel power plants and prevent the release of 8,055,600 tons of CO2 per year into the environment. Furthermore, the economical examination illustrates that Beijing residents may save approximately one billion USD per year on the purchase of electricity if they use the IDX-SAHP. Besides, the Chinese government can achieve about 109 billion USD per year if the equivalent electricity is exported instead of domestic consumption for Electric Water Heaters (EWHs). Moreover, the external costs decrease by 928 million USD per year if the IDX-SAHP is replaced with the EWHs. This paper discloses the significance of energy, economic, and environmental (3 E) examinations, optimization, and MODM during the IDX-SAHP design to increase the system efficiency and decrease the costs and environmental impacts by opting the most appropriate design parameters of the system.

RETRACTED: Selecting and balancing market portfolio using artificial intelligence and fuzzy multiobjective decision-making model

Most enterprises focus on product portfolio management (PPM) and exclude market portfolio management, and individual markets are selected solely based on financial performance which may not be appropriate because other factors may dictate the outcome of the market selection. This study proposes a market portfolio model that considers market share, market growth, market competition, market risk, and market cost to maximize overall profit. A three-stage approach is proposed to identify potential product markets using an AI algorithm. A set of the most promising market is then determined based on the results of Stage 1 using a fuzzy multi-objective mathematical programming model by concurrently maximizing the total market share and market growth, and minimizing the market competition and market risk. A single-objective mathematical programming model is then used to determine a market portfolio to maximize the total profit using the results for Stage 2. The single-objective model is tested using two sets of threshold constraints. The balance of the market portfolio is discussed and the results are compared. The novelty of the article: (1) the first study involves a product market portfolio, (2) the decision of market selection is scientific (using AI instead of personal judgment), holistic (factors include market share, growth, competition, risk, cost, and profit) and optimized (using mathematical optimization), and (3) the market portfolio maximizes total profit. (4) the proposed method produces a portfolio with a higher profit than the TOPSIS method.

An improved multi-objective grey target decision model for corporate decision making

To address the problem of enterprise decision-making, an evaluation index system for the proposed solution is established based on integrating the components of the enterprise decision-making evaluation solution. In the assignment method, the CRITIC assignment method is improved by using Kendall coefficients instead of Pearson coefficients. It overcame the non-linear and non-normal distribution that occurs in the actual situation. The improved CRITIC assignment method is introduced into the attribute hierarchy analysis method, which uses the multiplier normalization synthesis method. It is combined the subjective assignment method with the objective assignment method to determine the weights of the evaluation indexes. The AHM-CRITIC comprehensive assignment method is introduced to establish a multi-objective grey target model, which takes the comprehensive effect of the solution to be selected as the evaluation criterion. An empirical analysis was conducted based on the financing strategy of a labor service company in Wuhan to verify the rationality and feasibility of the method and provide a reference for the evaluation of enterprise decision-making.

Two-sided matching decision-making model for complex product system based on life-cycle sustainability assessment

Since the different matching schemes between manufacturing tasks and services have a great influence on the life cycle sustainability of complex product system (CoPS), the strategy used to find cost-effective, environmentally friendly, and socially acceptable matching results is essential for sustainable development. This paper proposed a novel two-sided matching model for CoPS based on life-cycle sustainability assessment (TSMM-CoPS-LCSA). First, the two-sided matching model based on the life-cycle sustainability assessment of manufacturing tasks and services for CoPS is presented. Then, a bi-level decision-making method is proposed, where learning and synergy effect are considered to ensure stability in the matching process. Sequentially, to facilitate the optimization solution, the Karush-Kuhn-Tucker (KKT) conditions are adopted to convert the bi-level model to a single model which aims to minimize the total cost, environmental impact, and social impact. Subsequently, for solving this nonlinear and complex multi-objective decision-making model, an improved hybrid algorithm that integrated Tabu Search and non-dominated sorting genetic algorithm II (TS-NSGA-II) is applied to solve the above model. Finally, a case study of CoPS manufacturing project is illustrated to demonstrate the effectiveness and superiority of the proposed TSMM-CoPS-LCSA model. The results indicate that the proposed method can find a better services scheme in line with actual demand, which provides a guiding significance for promoting the sustainable development of the manufacturing industry.

A multi-objective dynamic covering location problem for hierarchical agricultural machinery maintenance facilities

It is essential to design a product service system with the adequate location of maintenance facilities for guaranteeing the fast and dependable response to failures of extremely busy agricultural machinery during harvest time. This paper aims to optimize where to locate/relocate two hierarchical levels of facilities respectively composed of maintenance stations and mobile service fleets over multiple periods of a harvest season to support the safe and stable operation of agricultural machinery. We formulate a multi-objective dynamic covering location problem for these hierarchical facilities that seeks to maximize the total demand covered within some response radii while minimizing the modification to facility locations between the periods. The augmented ɛ-constraint method based on lexicographic optimization is then developed to obtain a set of non-inferior solutions that allow us to evaluate the trade-off between multiple decision objectives. Lastly, a real-life problem is handled to illustrate the effectiveness of our multi-objective decision model and solution method. Computational results recommend the optimal decision of facility locations for implementation, examine the sensitivity analysis of selected parameters, and compare the solution obtained from the proposed model with the period-specific solutions that take no account of the changing locations of facilities between the periods.

A multi-objective decision-making model for renewable energy planning: The case of Turkey

The rapid increase in the world population along with the rising trend of global economic growth triggers serious problems related to energy consumption. In this regard, a comprehensive analysis of the current and potential energy situation in the light of demographic trends, main economic indicators, technical conditions, social and environmental concerns has become a significant issue for developing well-planned global and regional energy strategies. In this study, a multi-objective decision-making model is constructed for renewable energy planning to determine the most appropriate resource diversity for Turkey by focusing on five renewable energy sources, namely solar, wind, geothermal, hydroelectric, and biomass. There are four objective functions presented in the proposed multi-objective model including maximization of the technical score of regions, maximization of the job creation, maximization of the environmental score, and minimization of the cost. As a solution methodology, a two-phase fuzzy goal programming approach is performed. The proposed model is solved using LINGO software as a comprehensive solver for model optimization. The appropriateness of the proposed method is analyzed by comparing it with other multi-objective methods. Results reveal that the majority of the energy demand should be met via solar and hydroelectric energies for the satisfaction of the indicated objectives.

A multi-objective stochastic-information gap decision model for soft open points planning considering power fluctuation and growth uncertainty

Recently, soft open points (SOPs) attract much attention due to providing power flow control and voltage regulation to the distribution system (DS). However, multiple uncertainties and expensive capital cost are referred to as thorny issues in the SOPs planning. In this paper, a multi-objective stochastic-information gap decision (MS-IGD) model is presented to alleviate multiple uncertainties with the limited budget. First, a deterministic SOP planning model is built to minimize the comprehensive cost of distributed system operator (DSO). Then, a MS-IGD model is formulated considering power fluctuation and growth uncertainty in distributed generators (DGs) and loads. To solve this model effectively, it is transformed to a mixed integer linear programming (MILP) by the polyhedron linearization technology, linear programming theory and ε constraint algorithm. In case studies, based on a prospective DS from northern China, the SOPs planning scheme with corresponding accommodation range of multiple uncertainties is presented. Numerical results not only authenticate that the worst-case envelope bound should not be decided in advance because of the complicated relationship between multiple uncertainties and investments, but also demonstrate the negative correlation between the deviation of power fluctuation and growth uncertainty under the same budget.

Behavior analysis of travel route selection with consideration of risk aversion

In order to study the effect of travelers' attitudes on route selection behavior when risk aversion is considered in intelligent transportation system, this study provides a methodology about multi-attribute route selection for travelers with different characteristics. A multi-objective decision-making model of route selection with consideration of risk aversion is established by integrating the cumulative prospect theory to describe the traveler's risk aversion attitude and the solution algorithm based on the assignment model. A case in practice verifies the rationality of the model. The result shows that traveler's risk aversion has a significant impact on the route selection result. The result of path selection varies with the traveler's different composite impedance and comprehensive influence weight. The target sensitivity with consideration of risk aversion is stronger than that without consideration of risk aversion, and the consideration of risk aversion is closer to the actual situation of route selection. The proposed travel route selection decision-making model which considers risk avoidance can be used to study the problem of multi-type travelers and multi-attribute route selection in intelligent transportation system, also can be used as the basis for the study of traffic problems such as traffic flow allocation and network design.