Multi-objective Decision-making Model Research Articles

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.

Multiobjective Fusion Decision Model for Key Technologies of Aircraft Intelligent Lightweight.

As a tool for aviation exploration, aircraft plays a pivotal role in the aerospace field. The aircraft's lightweight can ensure that the aircraft can carry more loads, so it is of great significance to the research on the key technologies of aircraft lightweight. This paper organizes the intelligent technology system from the multiobjective fusion technology relationships and considers the complex changes between technology units. A technology group model for the intelligent technology system of spacecraft welding is formed to provide a decision-making basis for the realization of batch, stable, and efficient intelligent and lightweight manufacturing of spacecraft. We expand the technology development research from single technology research to technology system research. We research and build a three-dimensional perspective intelligent technology system based on technology unit, technology system, and technology group. It reveals the internal relationship description of technological development and expands and perfects the current research theory of technological development.

Evaluation of Regional Ecological Efficiency and Intelligent Decision Support for Sustainable Development Based on Environmental Big Data.

To promote urbanization in the next stage, it is of great significance to explore the ecological efficiency of green ecological regions and advance the sustainable development of a social economic system. However, spatial heterogeneity has not been fully considered in the existing evaluation models or methods for regional ecological efficiency (REE), and the corresponding decisions on sustainable development are not the optimal solutions. To solve the problems, this paper explores the evaluation of REE and intelligent decision support for sustainable development by analyzing environmental big data. Firstly, the spatiotemporal evolution of REE was examined based on environmental big data to clarify the spatial layout of REE and the sources of the spatial differences. Next, a multiobjective optimal decision-making model was established for the sustainable development of a regional ecosystem, and the solving method was presented for the model. The proposed model was proved valid through experiments.

Research on Raw Material Ordering and Transportation Model Based on Multi-objective Programming and ARIMA

This paper studies the decision-making problem of raw material ordering and transportation in production enterprises, and establishes the optimal procurement scheme and optimal transportation scheme for production enterprises under different conditions based on genetic algorithm and multi-objective programming. Firstly, five factors affecting the company's decision-making are summarized; Secondly, the analytic hierarchy process (AHP) is used to determine the weight of each impact evaluation factor, and then the comprehensive effect measure matrix is obtained by combining with the multi-objective grey target decision model to rank 402 enterprises, and the 50 most important suppliers were selected.In order to better reflect the materials and transportation model required by the enterprise's production, the mean square deviation of the enterprise's output in the past five years is used to approximate its output in the next 24 weeks. The multi-objective optimization algorithm based on the combination of convergence criteria and genetic algorithm is used to obtain that at least 30 suppliers and 5 transporters should be selected; The sensitivity analysis and stability test of the model are carried out.

Instructor Performance Prediction Model Using Artificial Intelligence for Higher Education Systems

Recent advancements in Artificial Intelligence techniques, including machine learning models, have led to the expansion of prevailing and practical prediction simulations for various fields. The quality of teachers’ performance mainly influences the quality of educational services in universities. One of the major challenges of higher education institutions is the increase of data and how to utilize them to enhance the academic program’s quality and administrative decisions. Hence, in this paper, Artificial Intelligence assisted Multi-Objective Decision-Making model (AI-MODM) has been proposed to predict the instructor’s performance in the higher education systems. The proposed AI-assisted prediction model analyzes the numerical values on various elements allocated for a cluster of teachers to evaluate an overall quality evaluation representing the individual instructor’s performance level. Instead of replacing teachers, AI technologies would increase and motivate them. These technologies would reduce the time necessary for routine tasks to enable the faculty to focus on teaching and analysis. The usage for administrative decision-making of artificial intelligence and associated digital tools. The experimental results show that the suggested AI-MODM method enhances the accuracy (93.4%), instructor performance analysis (96.7%), specificity analysis (92.5%), RMSE (28.1 %), and precision ratio (97.9%) compared to other existing methods.

Interval-valued intuitionistic fuzzy two-sided matching model considering level of automation

Over the past few decades, personnel–position matching (PPM) has garnered increasing attention from scholars. In recent years, with intelligent robots facilitating intelligent production, a new form of problem—personnel–machine position matching (PMPM)—has been derived from PPM. In this study, an interval-valued intuitionistic fuzzy two-sided matching model considering level of automation (LOA) is proposed to solve the PMPM problem in an intelligent production line from the perspective of position homogeneity. The first issue to be considered in this solution is the uncertainty of preference information resulting from the decision-makers’ cognition bias or limitations. By proposing a novel score function based on the centroid method and technique for order preference by similarity to an ideal solution (TOPSIS), this issue is addressed in the information evaluation phase. Another important issue lies in the LOA, which adjusts the degree of human–machine participation. In this study, the classical two-sided matching model was improved by considering the LOA. Furthermore, to maximise the matching satisfaction of multiple sides (personnel, intelligent robots and positions), a multi-objective decision-making model is established. Afterwards, the model is transformed into a single objective model using the combined satisfaction analysis method, which is introduced to produce the final optimisation results in this modelling process. A case is presented to illustrate the practicality of the interval-valued intuitionistic fuzzy two-sided matching model considering LOA, and the results indicate that this model can solve the PMPM problem in an intelligent production line.

Renewable energy portfolio optimization with public participation under uncertainty: A hybrid multi-attribute multi-objective decision-making method

With the demand for energy increasing rapidly, the public is increasingly concerned about renewable energy (RE) planning. However, existing studies on RE planning rarely involved public opinions in the decision-making process. This study devotes to introducing a multi-attribute multi-objective decision-making model for RE portfolio selection with public participation. First, to reduce the complexity of the problem resulted from many participators, a linguistic risk appetite-based method is adopted to classify the public into subgroups. Considering that public opinions may be incomplete, the evidential reasoning approach is then used to aggregate the opinions of individuals within a subgroup. Next, the stochastic multi-attribute acceptability analysis method is applied to aggregate the preferences among subgroups and further derive a robust result regarding the public’s social acceptance of RE technologies. On the basis of the derived social acceptance, a risk-based fuzzy interval goal programming model is proposed to derive the optimal RE portfolio. Finally, an illustrative case of optimizing RE portfolios is given to demonstrate the applicability of the proposed model.

Water surface photovoltaic along long-distance water diversion projects and its co-benefits

As the world encounters insufficient fossil energy and worsening environmental pollution, the significant potential of water surface photovoltaic (WSPV) systems and the remarkable benefits are crucial to promoting cleaner production and sustainable development. This paper proposes installing overhead WSPVs along the open channels of long-distance water diversion projects (WDPs), creating new opportunities for the adaptive traceability and utilization of energy–water resources. A multi-objective decision model for the feasibility evaluation of WSPVs is presented and applied to typical WDPs in China to demonstrate the effects of this modeling framework and explore the co-benefits of WSPV systems. The results indicate that: (1) it is economically and technically feasible to install WSPVs over MLSNWDP with a suggested tilt angle range of 10°–12°; (2) the proposed WSPVs in the eight typical projects evaluated using the decision model with a capacity of 29,486 MW can convert 37,263 GWh of electricity at a 12° tilt angle while saving 3.19 × 108 m3 of water annually; (3) the adaptive utilization of the saved water and electricity can increase the ecological water level guarantee rate of Baiyang Lake by 12.64%, alleviating potential eco-environmental problems. Moreover, a recommended coverage rate of 50% is proposed to inhibit algae outbreaks. Overall, this research demonstrates the technical and economic feasibility of the construction of WSPVs along WDPs and the co-benefits to address challenging research questions on the sustainable development of energy–water resources.

A Multi-Objective Mathematical Model for Level of Repair Analysis with Lead Times and Multi-Transportation Modes

In the event of failure of the product, level of repair analysis (LORA) is used to determine (1) whether the defective component should be discarded or repaired and (2) where this repair is made. In the literature, these repair operations are made with the aim of minimizing the total life cycle cost of the product. In this paper, we develop a multi-objective decision model that minimizes both the repair time (affected by lead times) and the repair costs. Our proposed model also considers the movement of the defective components to be performed by multiple transportation modes such as highway, railway, and airway. We use the epsilon constraint method to generate the Pareto frontier and analyze the trade-off between total repair costs and total repair time. We demonstrate the approach on an example problem.

Solving the Multiobjective Transportation Decision-Making Problem Based on Improved S-Type Membership Function

Aiming at the problem of multiobjective transportation decision-making, a fuzzy compromise method with an improved S-type membership function is proposed. This method not only considers a single objective and evaluates it marginally but also evaluates the overall objective as a whole. First of all, a multiobjective transportation decision-making model is established. Then, each objective function is mapped to a function on the interval [0,1] through the S-type membership function, thereby transforming the multiobjective transportation linear programming model into a multiobjective transportation fuzzy compromise programming model, and its satisfaction is expressed by the global utility function. Finally, through two examples, the results of the example algorithm are compared with the results in the literature, highlighting the superiority of the method. The experimental results show that in the multiobjective transportation decision-making problem, the fuzzy compromise method of the S-type membership function has better flexibility and effectiveness.

Material requirements planning with a fuzzy model

There are numerous uncertainty forms and modes capable of affecting the Material requirements planning (MRP) systems. The aim of this study was to provide a fuzzy multi-objective decision-making model for MRP problem. In this paper, a fuzzy multi-objective linear programming approach was proposed for MRP model with a fuzzy Lead Time (LT), in which, the MRP models of classic numbers were combined up to the probability of occurrence of each of the possible LTs as far as possible. Then, an objective function was considered, which maximizes the probability of occurrence of LTs. The initial values ​​of the MRP model were applied to this objective function and some decision makers were shown that with their risky behavior accept some LTs, which improve other goals, but their probability of occurrence is poor.

Research on Multi-objective Shared Parking Matching Decision Based on Two-side Matching

The purpose of this paper is to find a reasonable method for matching shared parking spaces. The travelers and the parking space sharing party are regarded as two separate subjects, and the matching of parking spaces is regarded as a two-side matching. The final parking space matching result belongs to the stable matching of the bipartite graph and the existence of Nash equilibrium is proved by the knowledge of graph theory. A multi-objective decision-making model for shared parking space matching based on two-side matching is established on the basis of considering the benefits of travelers and parking providers. The multi-objective model is transformed into a single-objective assignment model using weighting coefficients with adjustment parameters, and the corresponding solution algorithm is designed. Finally, an example is used to verify the rationality and feasibility of the established model. The results show that the parking space matching problem under sharing conditions belongs to the category of two-side matching. When the benefits of both parties are fully considered, the matching results obtained are relatively stable.

A multi-objective optimal decision model for a green closed-loop supply chain under uncertainty: A real industrial case study

Green closed-loop supply chain management is an important topic for business operations today because of increasing resource scarcity and environmental issues. Companies not only have to meet environmental regulations, but also must ensure high quality supply chain operation as a means to secure competitive advantages and increase profits. This study proposes a multi-objective mixed integer programming model for an integrated green closed-loop supply chain network designed to maximize profit, amicable production level (environmentally friendly materials and clean technology usage), and quality level. A scenario-based robust optimization method is used to deal with uncertain parameters such as the demand of new products, the return rates of returned products and the sale prices of remanufactured products. The proposed model is applied to a real industry case example of a manufacturing company to illustrate the applicability of the proposed model. The result shows a robust optimal resource allocation solution that considers multiple scenarios. This study can be a reference for closed-loop supply chain related academic research and also can be used to guide the development of a green closed-loop supply chain model for better decision making.

Visualizing knowledge for decision-making in Lean Production Development settings. Insights from the automotive industry

PurposeThe paper focuses on how knowledge visualization supports the development of a particular multiobjective decision-making problem as a portfolio optimization problem in the context of interorganizational collaboration between universities and a large automotive company. This paper fits with the emergent knowledge visualization literature because it helps to explain decision-making related to the development of a multiobjective optimization model in Lean Product Development settings. We investigate how using ad hoc visual tools supports knowledge translation and knowledge sharing, enhancing managerial judgment and decision-making.Design/methodology/approachThe empirical case in this study concerns the setting up of a multiobjective decision-making model as a portfolio optimization problem to analyze and select alternatives for upgrading the lean production process quality at an FCA plant.FindingsThe study shows how knowledge visualization and the associated tools work to enable knowledge translation and knowledge sharing, supporting decision-making. The empirical findings show why and how knowledge visualization can be used to foster knowledge translation and sharing among individuals and from individuals to groups. Knowledge visualization is understood as both a collective and interactional process and a systematic approach where different players translate their expertise, share a framework and develop common ground to support decision-making.Originality/valueFrom a theoretical perspective, the paper expands the understanding of knowledge visualization as a system of practices that support the development of a multiobjective decision-making method. From an empirical point of view, our results may be useful to other firms in the automotive industry and for academics wishing to develop applied research on portfolio optimization.

Optimal allocation of CO2 emission quotas at the city level in Bohai Rim Economic Circle based on multi-objective decision approach.

As the most developed city circle in northern China, allocating CO2 emission quotas at the Bohai Rim Economic Circle (BREC) city level is essential for developing specific abatement policies. Thus, with reflecting multi-principles (fairness, efficiency, sustainability, and feasibility), this paper formulates the CO2 emission quota allocation among cities in BREC in 2030 based on the multi-objective decision approach. We first propose three allocation schemes based on the principles of fairness, efficiency, and sustainability, which are conducted by entropy method, zero-sum gains data envelopment (ZSG-DEA) model, and CO2 sequestration share method, respectively. Then, the CO2 allocation satisfaction is defined and used to measure the feasibility principle which is integrated as the objective function of the multi-objective decision model together with three allocation schemes to obtain the optimal allocation results. The results show that Beijing, Tianjin, Dalian, Shijiazhuang, Yantai, Weifang, and Linyi enjoy the largest CO2 emission quotas, having 1179.94 Mt in total and accounting for 31%. Beijing has the highest quotas, and Laiwu has the lowest emission quotas. Cities with large energy consumption and less CO2 sequestration capacity, such as Tianjin, Handan, and Tangshan, experience a decrease in the emission quota shares from 2017 to 2030, indicating that these cities would undertake large emission reduction obligations. Sensitivity analysis shows that Beijing, Zibo, and Jinan are more sensitive to minimum satisfaction changes, and the total satisfaction experiences an increase first and declines thereafter. Based on the results above, cities with large pressure to reduce CO2 emissions should not only promote economic development but also improve the capacity of CO2 sequestration by enhancing environmental protection to realize emission reduction targets.

Multimarket competition effects on product line decisions – A multi-objective decision model in fast moving consumer goods industry

In a multimarket competition, firms experience increased contacts with competitors under various product-geographic markets, exhibiting mutual forbearance, which in turn deters firms to venture into product proliferation. However, firms do engage in the same through horizontal product differentiation through 'brand extensions.' Since marketers face imprecise goals, we propose a multi-objective product line decision model for pruning or continuing product lines. Specifically, this is done by using a fuzzy goal-programming method while validating it using a dataset (comprising of eighty thousand households) from the Indian tea industry. Our findings reveal that in the festive season, a firm should only focus on limited brands and prune the rest to maximize its revenue while increasing the spending on advertising and the sales and distribution overheads. However, during the end of the financial quarter, the focus should be on achieving targets by maintaining a broader product portfolio.

Production Decision Optimization for Iron and Steel Scrap Remanufacturing considering Carbon Emission and Delivery Time

Under the requirements of the national carbon reduction target, the recycling and remanufacturing of scrap iron and steel are taken by major iron and steel enterprises as requirements for implementing industrial carbon emission reduction and are incorporated into their production strategies. The scheduling optimization of scrap iron and steel remanufacturing processes plays an important role in the energy saving and emission reduction of iron and steel enterprises. In this paper, a remanufacturing production decision model for scrap iron and steel considering both the product delivery time and carbon emissions was established, and a discrete krill swarm algorithm was designed to solve the multiobjective production decision model. The effectiveness of the model and the algorithm was verified by an example, demonstrating a good decision-making reference for the implementation of energy conservation and emission reduction in iron and steel enterprises.

Soft computing tool for aiding the integration of hybrid sustainable renewable energy systems, case of Putumayo, Colombia

This study contributes to the development of a soft computing tool used for the integration of hybrid renewable energy systems. The tool consists of the integration of a forecasting sub-tool for assessing quantitative and qualitative data. These data are input for a fuzzy multi-objective decision model developed in GAMS, where the total present value and the CO2 emissions of the system are minimized to obtain a set of Pareto alternatives. These alternatives are used as input for a fuzzy multi-attribute model developed in MATLAB. The CO2 emissions and the total present value, together with social, technologic, and environmental criteria are used as inputs for choosing the most sustainable alternative. Finally, the tool is tested in Puerto Guzmán, a small village of the department of Putumayo in southern Colombia. The selected and most sustainable alternative is composed of 31.2% diesel, 47.7% gasification of biomass, 7.9% solar photovoltaic and the rest with small hydro. The total present value of the system is 0.56 million USD and CO2 emissions are 0.4 million kg.

A multi-objective decision model for residential building energy optimization based on hybrid renewable energy systems

ABSTRACT Renewable energy sources (RES) are an inevitable environmental option in near future. These sources compete with conventional power generation, where good wind and solar resources are available. Hybrid renewable energy systems improve the economic and environmental aspects of renewable resources to meet energy demand. This paper aims to propose a multi-objective model to size a hybrid renewable system optimally. The system consists of wind turbines, photovoltaic panels, batteries, and a diesel generator as support for the system. This multi-objective optimization problem is solved using non-dominated sorting genetic algorithm (NSGA-II) method, resulting in the number of system components that will maximize the renewable energy efficiency while minimizing net present cost and CO2 emission. Results are compared to another multi-objective optimization algorithm, Epsilon-constraint. The comparison shows the feasibility of our suggested method for the problem. A residential building complex is then chosen in Khansar, Iran, to apply the proposed model and optimally size the hybrid renewable energy system. Results show that under the chosen climate and the building parameters, the renewable energy efficiency of nearly 80% is achievable which is satisfactory. Furthermore, the results show the undeniable impact of using renewable resources on reducing the pollutants’ emission and their related external costs.