Multi-objective Decision-making Model Research Articles

Multi-objective optimal allocation of construction project risks, ant colony optimization algorithm

PurposeThe purpose of this paper is to introduce a new approach for finding the most appropriate risk allocation among construction contract parties. Although risk allocation is a strategic decision that can greatly affect the cost and time of the project, it is often made based on the personal judgments of employers. That is why owners usually find it a very time-consuming and expensive decision process, while most contractors feel that risk sharing is not done fairly. In this paper, a quantitative model for risk allocation is introduced to fulfill clients’ conflicting expectations in the risk allocation process.Design/methodology/approachBy defining conflicting expectation of owners in the form of three quantitative objectives (lowest cost, maximum reliability and minimum risk exposure), a multi-objective optimization algorithm was developed to select the most appropriate risk allocation. Using experts’ knowledge through fuzzy set theory, a multi-objective decision-making model is developed based on an ant colony optimization algorithm. The proposed model is able to find the optimum risk allocation at the lowest cost and highest reliability while protecting the client against risk exposure within multiple parties projects.FindingsThe proposed model has the ability to select the most appropriate risk allocation in multi-parties projects (such as public–private partnerships) and quantitatively measure the impact of each employer’s choice on project results in the form of cost and time. The results of implementing the proposed model in a case study project revealed that optimum risk allocation requires a balanced attitude, and the transfer of all risks to the other parties will not necessarily lead to the lowest cost. The client should bear more responsibilities in risk management to avoid extreme time delay and cost overrun.Research limitations/implicationsThe proposed model can be implemented in multi-parties projects (such as public–private partnership), while other methods introduced in previous studies can only be used for projects with two party (client and contractor).Practical implicationsBy implementing the proposed model in a real project in this article and comparing its results with previous works, it has been shown that the proposed model has a good performance. Using this model can help clients drastically reduce cost and time and ultimately successfully conclude risk allocation negotiations (which is the most difficult part of any contract negotiation).Originality/valueIn this paper, the risk allocation process is modeled in the form of a multi-objective decision problem. This method helps employers to measure their conflicting goals and choose the most appropriate risk allocation according to their objectives. Also, in this article, the conflicting expectations of the employer in the process of contract negotiations are introduced as three measurable goals, which gives the decision-maker the ability to balance his expectations and not miss an objective. In the final step, an optimization model is developed to select the best option, which can choose the most appropriate party to bear the risk in a short time and among an unlimited number of participants.

A novel multi-objective decision-making model to determine optimum resource and capacity configuration for hybrid electricity generation systems: A comparative case study in Türkiye

The rapid depletion of fossil fuels, their negative environmental impacts due to harmful carbon emissions, and the drawbacks of the sole use of intermittent renewable energy resources on system reliability necessitate the use of more advanced energy system technologies. As a result, hybrid electricity generation systems offer an innovative alternative to conventional energy systems. However, the technical complexity and variability of these systems require the application of multi-objective optimization techniques to determine the optimal sizing and system design. Therefore, in this study, we suggest a multi-objective decision-making (MODM) model that involves five conflicting objectives to find the optimal resource configuration and auxiliary source capacity allocation within the framework of the multi-source electricity production method in Türkiye. The MODM model aims to maximize the utilization of resource potentials and the jobs created, while simultaneously minimizing the levelized cost of energy (LCOE), construction duration, and carbon emissions. The MODM model was solved using fmincon and fgoalattain multi-objective optimization algorithms using the Goal Attainment Method in MATLAB and the model results were subsequently compared to the real-world data. According to the model results, the optimal capacity to be allocated for the auxiliary source units is 13,749.58 MWm, while the capacity allocated according to the real-world data as of March 2024 is 2495.49 MWm. In contrast to the model's prediction of 90.8 % solar-based auxiliary unit installations, 99.8 % of auxiliary units are based on solar PV in the actual case. Furthermore, hydro or geothermal-based auxiliary sources are not allocated any capacity, which is consistent with the actual situation. The capacity allocated for the wind (primary source) + solar (auxiliary source) configuration is 76.2 %, whereas in reality, it accounts for 65.6 % of the total installations. Consequently, the model findings suggest that the maximum capacity allocation is for the wind (primary source) + solar (auxiliary source) type hybrid electricity generation system.

Optimizing construction supplier selection in conflict-affected regions: a hybrid multi-criteria framework

AbstractConflicts and wars profoundly impact infrastructure, exacerbating the adversity already caused by natural disasters. Therefore, it is imperative that the reconstruction process be both effective and efficient to expedite a return to normalcy. This study aims to enhance the efficacy of reconstruction efforts through improved construction supplier evaluation and selection. It introduces an innovative hybrid multi-objective decision-making model that integrates a broad spectrum of economic, technical, and humanitarian criteria. The model is designed to optimally select and assign construction suppliers in regions affected by human and natural conflicts and crises. Fifteen criteria have been incorporated into the evaluation process to validate its effectiveness and maximize its contribution to local communities. This methodology streamlines decision-making and enhances transparency in conflict zones, aligning with the interests of all stakeholders. The study incorporates advanced methodologies, including Fuzzy Goal Programming (F-GP), Geographic Information System (GIS)-based Risk Assessment, and Fuzzy Analytic Hierarchy Process (F-AHP), leveraging real-world data and a case study. Additionally, a sensitivity analysis examines the impact of varying inputs on the model's output. The findings attest to the model's utility in conflict-affected regions and its potential applicability in stable settings.

Evaluation and Prediction of Carbon Storage in the Qinghai-Tibet Plateau by Coupling the GMMOP and PLUS Models

Land-use alterations exert a profound impact on carbon storage within terrestrial ecosystems. Exploring the spatiotemporal dynamics of regional land use and carbon storage is crucial for optimizing national spatial planning and fostering low-carbon development. For this study, we utilized land-use data spanning from 2000 to 2020 for the Tibetan Plateau and assessed the spatial and temporal variations in carbon storage using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. We adjusted the carbon density within the provinces in the study area as a prerequisite. Moreover, we integrated the Grey Multi-objective Decision-making (GMMOP) model with the Patch-generating Land-use Simulation (PLUS) model to forecast carbon storage alterations in 2030 across various scenarios. The findings indicated that between 2000 and 2020, the overall carbon storage witnessed a decrease of 18.94 × 108 t. Carbon storage in grassland decreased by 22.10 × 108 t, and carbon storage in unused land, forest land, cultivated land, construction land, and water increased by 1.56 × 108 t, 0.92 × 108 t, 0.66 × 108 t, 158.50 × 104 t and 26.74 × 104 t, respectively. The soil organic carbon pool exhibited the highest average carbon storage of 195.63 × 108 t, whereas the litterfall organic carbon pool contained the lowest average carbon stock of 15.07 × 108 t. In comparison with the levels observed in 2020, the total carbon storage experienced a reduction of 8.66 × 108 t and 5.29 × 108 t under the inherent progression and economic growth scenarios, respectively. Conversely, it rose by 11.87 × 108 t and 16.21 × 108 t under the environmental preservation and holistic progression scenarios, respectively. Under the holistic progression scenario, the belowground biomass organic carbon pool exhibited the highest carbon storage increase of 5.59%. These findings offer valuable insights for the management and enhancement of carbon sinks in the Qinghai-Tibet Plateau.

Improved ant colony optimization algorithms for multi-objective investment decision model based on intelligent fuzzy clustering algorithm

With the continuous changes and development of financial markets, it has brought many difficulties to investment decision-making. For the multi-objective investment decision-making problem, the improved Ant colony optimization algorithms was used to improve the effectiveness and efficiency of the multi-objective investment decision-making. Therefore, based on intelligent Fuzzy clustering algorithm and Ant colony optimization algorithms, this paper studied a new multi-objective investment decision model, and proved the advantages of this method through comparative analysis of experiments. The experimental results showed that the improved Ant colony optimization algorithms has significantly reduced the system’s construction costs, operating costs and financial costs, all of which were controlled below 41%. Compared with the traditional Ant colony optimization algorithms, this method had lower values in policy risk, technical risk and market risk, and can effectively control risks. Meanwhile, the environmental, economic, and social benefits of this method were all above 58%, and the average absolute return rate and success rate in this experiment were 21.5450% and 69.4083%, respectively. Therefore, from the above point of view, the multi-objective investment decision model based on intelligent Fuzzy clustering algorithm and the improved Ant colony optimization algorithms can effectively help decision-makers to find the best investment decision-making scheme, and can improve the accuracy and stability of decision-making. This research can provide reference significance for other matters in the field of investment decision-making.

Green Residential Building Design Scheme Optimization Based on the Orthogonal Experiment EWM-TOPSIS

A multi-objective decision method is proposed based on the combining orthogonal test, entropy weight method (EWM) with TOPSIS (technique for order preference by similarity to an ideal solution) in this article. The method is more objective and efficient than traditional methods in the design of green residential buildings. A villa in Nanjing was taken as an example, and the cost, building energy consumption, and daylight factors were used as decision-making indexes. A total of six control factors were selected: terrain elevation, window area, building orientation, the opening ratio of exterior windows, roof structure, and window glass material. Each factor was designed with three levels. First, a design scheme index system was constructed, and the orthogonal experimental design was used to select representative design schemes. Then, the EWM and the TOPSIS method were used to determine the weight of each index, a comprehensive evaluation of the residential building design scheme was conducted, and, finally, the optimal scheme was confirmed via range analysis. The results show that the multi-objective decision-making model based on the orthogonal experiment EWM-TOPSIS is suitable for green residential building design and the optimal solution obtained is “15 m elevation, a 1200 × 1500 mm size vacuum glass, an orientation of 170° and an external window opening ratio 45% with a 40 mm thick expanded insulation board roof”.

Research on Optimization Strategies of Student Financial Aid System in Colleges and Universities Based on Multi-Objective Decision-Making Model

Research on Optimization Strategies of Student Financial Aid System in Colleges and Universities Based on Multi-Objective Decision-Making Model

Construction of Online Interactive Teaching Platform for Drama, Film and Television Majors Based on Fuzzy Multi-Objective Decision Making Algorithm

Abstract This study introduces a multi-objective decision-making model tailored for developing an educational platform, blending economic considerations with the platform’s operational constraints. It employs a fuzzy set approach for algorithm design, utilizing a method that minimizes deviations in membership values for solving. The research culminates creating an online interactive teaching platform for drama and film studies. This platform is architecturally founded on the B/S framework, incorporates functional modules derived from its educational objectives, and utilizes MySQL for data flow design. Comparing it to conventional teaching methods, a comprehensive evaluation demonstrates its practical application in real-world teaching scenarios. Performance tests reveal module response times between 2.7 to 3.0 seconds, with compatibility tests ranging from 0.11 to 0.36 seconds. Under a load of 200 simultaneous users, the database connection pool maintains an optimal size of about 35 connections, with thread counts near 50 and zero idle database connections. The study also quantifies the platform’s impact on various educational aspects, showing correlation coefficients for learning attitude (0.88), motivation (0.50), behavior (0.72), and teacher-student (0.15) as well as student-student interactions (0.27) with the learning effectiveness of the platform.

A multi-objective decision method for the network security situation grade assessment under multi-source information

Grade assessment of network security situation is summarized as a typical multi-index grade assessment problem. However, the existed methods for grade assessment of network security situation do not consider multi-source information such as the trust information among experts, the preference information among companies and heterogeneous information of companies. The above problems are unable to be solved through traditional assessment methods. The aim of this paper is to establish a novel multi-objective decision model for the grade assessment of network security situation under multi-source information. On the basis of describing the grade assessment problem of network security situation, the membership functions of four-type thresholds for the grades on attribute eigenvalue are put forward. Two trust information uncertainty degrees in social network are defined, and the trust transfer operator based on trust information uncertainty degree and multi-path trust integration method based on the variable weight function is proposed. Afterwards, a new method to generate the incomplete social network is used to identify the weights of experts. Then, a multi-objective decision grade assessment optimization model is further established to obtain the network security situation grades and grade discrimination based on the two-tuple linguistic operator. The proposed method provides a theoretical basis for constructing and testing the grade assessment of network security situation. Meanwhile, it develops the grade assessment system of advanced network security situation and improves the ability to protect network security.

A fuzzy multiobjective team decision model for CODP and supplier selection in customized logistics service supply chain

To address information vagueness and uncertainty of the order demand, the logistics service provider (LSP) service capacity and the target preference caused by emergency events, a multiobjective team optimization model is developed for the customer order decoupling point (CODP) decision, supplier selection and order quantity allocation problems in customized logistics service supply chain. Three important techniques are described: (1) Fuzzy objective coefficients due to the uncertainty of the order and objective weights. (2) Fuzzy constraint parameters due to LSP service capacity and order demand variations. (3) A fuzzy single objective weighting technique with acceptance coefficient of constraint and fuzzy parameters that reflects decision-maker preferences. The acceptance coefficients of the constraints are integrated into the single objective model, which reflects the satisfaction level of the logistics service integrator (LSI) for feasible solutions. Then, we perform fuzzy decision and feasible solution design based on the center of gravity method defuzzification and maximum membership degree principle. To optimize decisions for decision-makers, the effects of some important fuzzy objective coefficients and fuzzy constraint parameters on the CODP decision and the acceptance coefficient of constraint was explored through sensitivity analysis. Numerical experiments show that the scale effect coefficient and objective function threshold have greater effects on the CODP decision.

A fuzzy multi-objective decision-making model for global green supplier selection and order allocation under quantity discounts

A fuzzy multi-objective decision-making model for global green supplier selection and order allocation under quantity discounts

Late-stage diversion risk assessment for high dams considering early initial impoundment: a case study of Lianghekou Station, China

Early initial impoundment can generate additional revenue but bring more flood risk in late-stage construction diversion. In view of the possible flood risk and catastrophic consequences caused by high dam failures induced by early impoundment, a comprehensive assessment is proposed. Taking the Lianghekou high rockfill dam on the Yalong River, southwest China, as an example, this study established the late-stage diversion risk model and predicted the failure probabilities for the original, 15 days ahead, and 30 days ahead schemes varied with the initial impoundment time using the Monte Carlo method. Then, considering overtopping-induced gradual breaking of rockfill dams, the NWS dam-break flood forecasting model (DAMBRK) was used to estimate the break development and the outflow hydrograph. Due to no significant differences being found in the outflow hydrographs of the three schemes, life loss was used an index for the consequences of inundation. Combining the failure probability, life loss, and early impoundment revenues brought by earlier power generation, a satisfied initial impoundment scheme was acquired using the multi-objective decision model. The results revealed this method can find a reasonable initial impoundment time in view of the late-stage diversion risk assessment.

Optimal locating by integrating volumetric fuzzy sets and geographic coordinate system: An application to healthcare

The volumetric fuzzy set (VFS) deals with the uncertainties associated with two-dimensional variables. This study shows the application of VFS in finding the optimal coordinates for a new hospital by considering several fuzzy goals. For this purpose, we first define two types of volumetric fuzzy goals: type one following the proximity to an ideal point and type two following far from an anti-ideal point. Then, we extract the membership functions (MFs) for type-one and type-two volumetric fuzzy goals. These MFs determine the membership degrees of each point for volumetric fuzzy goals based on its distances from the ideal or anti-ideal points. We consider the Manhattan distance to extract these MFs because it better measures the real distance between two points in urban areas. Next, we design a new weighted volumetric fuzzy multi-objective decision-making (VFMODM) model considering type-one and type-two fuzzy goals. This model answers this question: what are the optimal coordinates for a new hospital to maximize overall satisfaction? Compared to existing fuzzy optimization models, the VFMODM model considers uncertainty for two-dimensional variables.

Optimized model for coordinated development of regional sustainable agriculture based on water–energy–land–carbon nexus system: A case study of Sichuan Province

In the context of global warming and resource scarcity, economic development and population growth are challenging the demand for water and land resources, and the complex relationship among water, land, energy and carbon dioxide makes it important that a process for efficiently managing water, energy, land and carbon dioxide in sustainable agricultural systems is developed. Therefore, considering the uncertainty of agricultural systems, this paper proposes a multiobjective optimization model for the sustainable management of water–energy–land–carbon dioxide systems that optimizes the allocation of water resources and land resources under the condition of minimizing agricultural carbon emissions and maximizing irrigation water productivity and energy economic productivity. The model uses the improved nondominated sorting genetic algorithm to address this issue, and the optimal resource allocation strategy is further obtained through the multiobjective decision model. This paper uses Sichuan Province, the main grain-producing region in Southwest China, as a case study to verify the effectiveness of the model and then provide a decision-making process for improving the utilization rate of agricultural resources and developing low-carbon agriculture. The results show that under the coordination of resource consumption, economic benefits and carbon emissions, the total allocation of water resources and the total allocated area of crop planting after optimization were 118.678 × 108m3 and 4598.796 × 103ha, respectively, decreases of 22.87% and 7.9%. The planting structure changed. The area planted with rice increased by 4.25%, the area planted with corn decreased by 15.5%, and the area planted with rapeseed decreased by 14.96%. Electricity, fertilizer and straw burning contributed significantly to agricultural carbon emissions, with average contributions of 68.0%, 15.9% and 6.6%, respectively. The multiobjective optimization model established in this paper can improve resource utilization and reduce the environmental impact of agriculture, which are of great significance in terms of achieving the goal of “carbon neutrality” and promoting the sustainable development of regional agriculture. The established model framework can be applied to regions with limited resources around the world.

Optimal Financial Resource Allocation Using Multiobjective Decision Making Model

The management of each industry must strive to meet multiple financial objectives, including capital structure, dividend policy, and earnings growth. The paper proposes an approach to analyze how financial resources should be allocated optimally using a multi-objective decision-making model. As part of the study, Al Rajhi banks' financial statements are used as a case study. All of the data is drawn from the banks' financial statements. Overall, the study's results show that all objectives have been achieved. This model enables banking and other industries to formulate strategies for dealing with various financial situations. The study's results are calculated and verified using LINGO 18.0 x64 software. Hence, the proposed model can determine financial decisions and develop strategies for dealing with various economic frameworks.

Inclusion of “managing flexibility” valuations in the pricing of PPP projects: a multi-objective decision-making method

PurposeThe Public–Private Partnership (PPP) modality plays an important role in the procurement of global infrastructure projects. Regarding PPP's complex transaction structure, pricing of a PPP project is critical to both parties where the government pursues a high value for money (VFM) and the investor strives to maximize its financial gains. Despite the straightforward win–win principle, a formidable compromise is often the case to end up with a fairly acceptable price, subject to many determinants such as the risk profile, expected return, technological innovation and capacities of both parties. Among them, this study chooses to examine the “managing flexibility” (MF) capacity of investors in pricing of a PPP project, in light of the widely recognized importance of a real-option perspective toward the long term, complex and uncertain PPP arrangement. This study addresses two major questions: (1) how is MF in PPP projects to be valued and (2) how are PPP projects to be priced when considering a project's MF value.Design/methodology/approachA binomial tree model is used to evaluate the MF value in PPP projects. Based on the developed MF pricing model, net present value (NPV) and adjusted VFM value are then calculated. Finally, a multi-objective decision-making method (MODM) was adopted to determine the optimal level of returns based on invested capital (ROIC), return on operation maintenance (ROOM) and concession period.FindingsThe applicability and functionality of the proposed model is investigated using a real project case. For a given return, extended NPV and adjusted VFM value were calculated and analyzed using sensitivity analysis. Factor influence is shown by the model to be dependent on factor impact on cash flow. Subsequently, a multi-objective decision-making (MODM) model was adopted to determine the optimal level of returns, where the solution approximates the real-world bidding price. Results confirm that the pricing model provides a reliable and practical PPP proposal pricing tool.Originality/valueThis study proposes an integrated framework for valuing MF in PPP projects and thus more accurately determine optimal pricing of PPP projects than revealed in extant research. The model offers a practical tool to aid in the valuation of PPP projects.

Bilateral Matching Decision-Making Method Considering Regret Avoidance in Second-Hand Transactions

Effective methods of bilateral matching decision-making could make the second-hand transaction market and platforms more dynamic and healthier for development. However, the loss of description about the characteristics of sellers and buyers and the weight information of their inner preference can affect the matching result. By developing a method for a second-hand transaction, this paper proposes a novel approach that considers the matching parties' psychologies, which can improve the matching efficiency. A mathematical model of seller and buyer is performed, and then the price and other evaluation indicators of sellers and buyers are described by five different types. A bilateral matching multi-objective decision-making model is constructed and solved. A real case study of property trade platform is presented to illustrate how the proposed approach could be applied in practice. The results of this research indicate that the proposed approach is a useful tool for matching the sellers and buyers and has substantial practical application.

A bi-level decision-making system to optimize a robust-resilient-sustainable aggregate production planning problem

This study introduces a sustainable-robust aggregate production planning (APP) problem taking into account workforce productivity, outsourcing option and supplier resilience. In this regard, a bi-level decision-making system is designed using multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models, respectively. In the MADM section, a hybrid method called BWM-WASPAS-Neutrosophic based on best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) under type-2 neutrosophic number (T2NN) is utilized to investigate resilient supplier selection. To design the MODM section, a multi-objective mixed-integer linear programming (MILP) model is suggested which is then treated with the help of weighted goal programming (WGP) method. The multi-objective model honors sustainable development goals as it aims not only to minimize total cost and maximize weighted purchasing from suppliers, but also to minimize negative environmental impacts simultaneously. Robust optimization (RO) technique is then implemented to the MODM model to address the demand uncertainty. In order to validate the suggested methodology, a real case study from the literature is examined. The obtained results reveal the efficiency of our decision-making system in finding the optimal policy in less than 1 s where sensitivity analyses also contribute to practical managerial implications. Finally, it is revealed that the total weighted purchase from suppliers has the highest sensitivity to the conservatism levels, which determine the extent to which our uncertain demand parameter can deviate from its nominal value.

Collaborative decision-making model for capacity allocation of photovoltaics energy storage system under Energy Internet in China

Solving the problem of photovoltaics abandonment and power limitation and improving resource utilization is particularly important to promote the sustainable development of the PV industry. With the innovative development and continuous application of energy storage technology, energy storage has become an indispensable part of photovoltaic power generation, realizing the consuming goal of abandoned photovoltaics. This paper studies the synergistic management of PV power generation based on the perspective of value chain, and constructs a complex value chain system with PV power generation subsystem and energy storage subsystem as the key subsystem—photovoltaics energy storage system (PVESS). Firstly, a value co-creation analysis framework for promoting capacity allocation of PVESS under the Energy Internet is analyzed. Secondly, the basic model of hybrid energy storage system (HESS) combining battery energy storage system (BESS) and superconducting magnetic energy storage system (SMES) is constructed. Thirdly, a multi-objective collaborative decision model is established with the objective functions of minimum economic cost, minimum abandoned photovoltaics and minimum load outage rate for the hybrid energy storage system (HESS). And the hybrid particle swarm algorithm (HPSO) algorithm and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method are used to determine the optimal system configuration. Finally, the validity and feasibility of the model are proved by the results of collaborative decision making and sensitivity analysis. The proposed collaborative decision suggestions for capacity allocation is adopted to facilitate the collaborative and effective operation of the system. This study can also provide insightful enlightenment for PVESS project investors, collaborative decision-making professionals and decision makers.

Improving the WEEE recycling fund system in China: Multi-objective decision-making model based on EPR system

China has established a government recycling fund system based on the principle of extended producer responsibility (EPR). The principle of EPR is the key to the sustainable development of waste electrical and electronic equipment (WEEE) recycling. The implementation of the recycling fund system has been facing major problems. This article proposes a plan on refining the producer's recycling target to improve the current fund system in China. With the aim to maximize the amount of WEEE recycling, minimize the overall producer's recycling cost, and providing the best supports to the recycling industry, a multi-objective decision-making model is constructed to identify the optimum producer's recycling target for WEEE. Data from the recycling fund system and EPR pilot project of the Ministry of Industry and Information Technology are used to test the model. The results show that, under the condition that the amount of recycling is equal to the current level, the levy of recycling fund can be reduced by 20%, which can effectively drive the producers to take more responsibility in the collection and recycling of their end-of-life products. The multi-objective decision-making model for producer's recycling target provides a basis for the adjustment of China's WEEE recycling fund system policy, which will alleviate the challenge at present, and involve more producers to devote in collection systems. It also shows the alternative solution for EPR in non-fund business models in the recycling industry.