Programming Technique For Multidimensional Analysis Research Articles

Optimization of a syngas-fueled SOFC-based multigeneration system: Enhanced performance with biomass and gasification agent selection

In light of abundant sources of biomass feedstocks and regarding the higher performance in syngas-fueled-based SOFC systems, this work proposes a novel multigeneration system based on the combination of syngas-fuel SOFC, Kalina cycle, humidification-dehumidification desalination unit, and proton exchange membrane electrolyzer. This system is designed for simultaneous production of power, heating, freshwater, and hydrogen. Comprehensive energy, exergy, exergoeconomic, environmental, and economic analyses have been conducted to evaluate its performance. The optimum input biomasses, gasification agents, and operating conditions were determined using a coupled approach of an artificial neural network, multi-objective Particle Swarm Optimization algorithm, and the Linear Programming Technique for Multidimensional Analysis of Preference decision-making method. Under base input conditions, the energy and exergy efficiencies of the proposed system were found to be 53.66 % and 38.2 %, respectively. The system also demonstrated the capability to produce 0.1633 kg/s of freshwater and generate a net power output of 377.6 kW. The results indicate that using oxygen-enriched air in the gasification process notably enhances efficiency while reducing emissions. Straw and CO₂ were identified as the optimal feedstock and gasification agent, yielding an exergy efficiency of 42.71 % and a total product cost of 6.29 $/GJ at the optimum point. Moreover, with a selling price of $0.20/kWh for electricity and a fuel cost of $6/GJ, the system can achieve total revenue of approximately $1.65 million with a payback period of 4.01 years. These findings underscore the proposed plant's profitability under specific pricing conditions, making it an attractive investment opportunity.

Multi-objective optimization of twisted tri-lobed tube geometry for enhanced heat transfer and reduced resistance in turbulent flow

This study presents a detailed multi-objective optimization analysis of turbulent flow and heat transfer within a twisted tri-lobed tube, employing an integrated approach that combines Central Composite Design (CCD), Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto frontier analysis, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The investigation focuses on optimizing key design parameters: Reynolds number (Re), twist pitch (P), and minor circle radius (r), targeting Nusselt number (Nu), Poiseuille number (fRe), and Performance Evaluation Criterion (PEC) as optimization objectives. Our major findings reveal that the developed second-order regression model precisely predicts the influences of these parameters on Nu, fRe, and PEC, with sensitivity analysis showing Re's strong positive impact on Nu and fRe, P's negative effect on fRe, and r's minimal impact. The study notably highlights the significant Nu enhancement with Re increase. Utilizing multi-objective optimization, we identified a Pareto optimal set, and through LINMAP and TOPSIS methods, derived compromise solutions that optimally balance heat transfer efficiency, flow resistance, and overall performance. These outcomes underscore the critical importance of parameter selection to meet specific engineering needs, offering a strategic framework for enhancing twisted tri-lobed tube designs.

A big data quality evaluation method based on group heterogeneity rationality perception information fusion

Big data is complex, and it is difficult to evaluate big data quality with only a few evaluation subjects. This paper designs a universal evaluation method for big data quality based on the idea of group heterogeneity rationality perception information fusion. Group evaluation information is usually large-scale and heterogeneous. The linear programming technique for multidimensional analysis of preference (LINMAP) model has the performance of large-scale evaluation information processing. Because the existing LINMAP models cannot effectively process group evaluation information with heterogeneous rationality, the LINMAP model of heterogeneous rationality (HR-LINMAP) based on group evaluation information is proposed. The research results show: under certain conditions, the LINMAP model of bounded rationality (BR-LINMAP) can be transformed into the LINMAP model of perfect rationality (PR-LINMAP), and the HR-LINMAP is the general form of PR-LINMAP and BR-LINMAP. The scientific research big data resources with mature trading markets are selected as the evaluation objects, and HR-LINMAP is used to analyze the big data quality evaluation cases. The empirical analysis shows that the HR-LINMAP model is feasible and effective, which provides a new idea for big data quality evaluation.

Multi-objective optimization of thermal modules in high heat flux laptops

The thermal module, as an important cooling pathway for chips, is a major factor affecting the performance of laptops. However, thermal analysis and optimization of the whole thermal module of a laptop is absent. Moreover, cost is also a major consideration in the design of a laptop’s thermal module. In this study, based on the thermal module of a high heat flux prototype laptop, we constructed an efficient thermal resistance network model and a cost model to evaluate the impact of different components. Furthermore, we employed NSGA-II (non-dominated sorting genetic algorithm II) to enhance the thermal module's heat dissipation capacity while minimizing cost. By obtaining Pareto solutions that provided optimal thermal resistances for the components in the thermal module, we then utilized LINMAP (linear programming technique for multidimensional analysis of preference) as a decision-making method to determine the best compromise solution. The best compromise solution focuses on reducing the thermal resistances of the thermal interface material on the high heat flux APU side, the shared heat pipe, and heatsinks to enhance the heat dissipation capacity of the thermal module. To control cost, sacrifices were made in the heat transfer performances of the thermal interface material on the GPU side and the independent heat pipes. Comparing the best compromise solution with the prototype, the cooling capacity is improved by 24% and cost is reduced by 9%. The efficient model coupled with optimization method may provide a new theoretical guideline for the laptop designers.

When to switch from heat-up to start-up in the warming-up process of a solid oxide fuel cell: A numerical study and multi-objective planning

Solid oxide fuel cells (SOFCs) at rest need to undergo warming-up process to reach normal performance. This process begins with cell heating (so called heat-up process) and continues with the activation of electrochemical reactions (so called start-up process) until the cell performance reaches steady state. This paper develops a transient numerical model to investigate the influence of the switching temperature (ST), defined as the electrolyte average temperature at the beginning of the start-up process, on the SOFC warming-up process. Also, an optimal ST is proposed by considering the different combinations of the objectives of warming-up time duration, energy efficiency and thermal shock. The results indicate that a lower ST (or an earlier onset of start-up process) results in a shorter warming-up time duration and a greater energy efficiency (as two desirable impacts), but causes a higher thermal shock (as a negative point). Using a multi-objective decision making approach (namely, linear programming technique for multi-dimensional analysis of preference), a ST of 823 K is proposed as an optimal compromise among the three mentioned objectives. However, when the energy efficiency is disregarded, a ST of 998 K is found to be optimum.

Decision-Making Model for Evaluating Joint Venture Contractors in Construction of Complex Infrastructure Megaprojects

Due to the complexity of infrastructure megaprojects, using joint venture contractors (JVCs) is an effective method to deliver projects. However, this process is constrained by several factors, including the quality of competing JVCs and knowledge level of decision-makers (DMs). However, existing decision-making methods for megaprojects and JVC selection lack comprehensive JVC evaluation criteria and cannot handle decision-making scenarios using incomplete data. To address these issues, in this study, we propose a novel model based on a hesitant fuzzy soft set and a linear programming techniques for multidimensional analysis of preferences called LINMAP, which can process incomplete decision data and improve decision accuracy. Moreover, a set of criteria was constructed from four dimensions (economy, society, environment, and cooperation), which upholds the sustainable development and internal cooperation of the built environment. Finally, by applying the approach to a complex infrastructure megaproject and conducting a sensitivity analysis, the feasibility and effectiveness of the proposed model were verified. Research shows that DMs in different specialized areas choose different criterion subsets, which leads to different decision-making results. For one, allowing DMs to choose a subset of the criteria can improve the accuracy of a decision and reduce the accompanying risks; further, a large gap exists between partial and global evaluations, so partial and global consistence must be included in the context of multicriteria decision-making; finally, cooperation is the premise of competition, i.e., good cooperation has a positive effect on competition, so the sustainable development of a JVC needs to consider not only economic, environmental, and social factors but also cooperation. This study contributes to the knowledge domain of multicriteria decision-making, extends the knowledge domain in terms of clarifying complicated evaluation scenarios in infrastructure megaprojects, and renders practical managerial implications for future complex evaluation processes in infrastructure megaprojects.

Optimal Heat‐Up Planning of a Solid Oxide Fuel Cell with and without Hot Air Recycling by Considering Energy, Time, and Temperature Gradient

A solid oxide fuel cell (SOFC) needs to be heated to a high temperature to be able to start generating electricity. This study aims to manage the heat‐up process of a SOFC by considering the three objectives of time duration, energy consumption, and temperature gradient, simultaneously. Mass flow rate (MFR) and rate of temperature rise (RTR) of the hot air, passing through the cathode channel of the SOFC, are considered as decision variables. The transient heat‐up process is numerically simulated for six different MFR values (5–50 mg s−1) and six different RTR values (0.1–5 K s−1). The results indicate that higher RTR leads to lower heat‐up duration, lower energy consumption, but higher temperature gradient. Also, higher MFR results in lower heat‐up duration, lower temperature gradient, but higher energy consumption. The results also reveal that hot air recycling increases thermal efficiency from below 40% to nearly 100%. Finally, when hot air recycling is employed and all the objectives are considered simultaneously, the heat‐up plan with a RTR of 0.5 K s−1 and a MFR of 50 mg s−1 is selected as the best choice by using linear programming technique for multidimensional analysis of preference.

A property perceived service quality evaluation method for public buildings based on multisource heterogeneous information fusion

The basic information of property perceived service quality (PPSQ) evaluation for public buildings is characterized by multisource heterogeneity, which cannot be achieved using the existing evaluation methods. Based on the classical linear programming technique for multidimensional analysis of preference (LINMAP), a multiobjective LINMAP model is constructed, and a new PPSQ evaluation method is proposed to solve the above problem. Constructing the multiobjective LINMAP model is the core of the new PPSQ evaluation method design. The proposed multiobjective LINMAP model includes the following: First, multisource heterogeneous evaluation information is collected by the generalized multi-index multi-scale (MIMS) method, and normalized by the TOPSIS method. Then, the LINMAP model is extended by the objective function, which is constructed with the minimum mean standard deviation (MMSD) between multiple information sources. Finally, the source weight and index weight are determined by the above model, the ranking order of each property service project is calculated, and the comprehensive value of the property service project is obtained. The example study verifies the feasibility and effectiveness of the multiobjective LINMAP model which is suitable for the new requirement of comprehensively and objectively evaluating PPSQ for public buildings.

4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer.

Refrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. Firstly, the interaction between the system components and their cycle behavior is analyzed by building four surrogate models using RSM. The model fit statistics indicate that they are statistically significant and agree with the design data. Three conflicting scenarios in bi-objective optimization are built focusing on the overall system following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) decision-making methods. The optimal solutions indicate that for the first to third scenarios, the exergetic efficiency (EE) and capital expenditure (CAPEX) are optimized by 33.4% and 7.5%, and the EE and operational expenditure (OPEX) are improved by 27.4% and 19.0%. The EE and global warming potential (GWP) are also optimized by 27.2% and 19.1%, where the proposed HMOGWO outperforms the MOGWO and NSGA-II. Finally, the K-means clustering technique is applied for Pareto characterization. Based on the research outcomes, the combined RSM and HMOGWO techniques have proved an excellent solution to simulate and optimize two-stage VCRS.

A fairness-concern-based LINMAP method for heterogeneous multi-criteria group decision making with hesitant fuzzy linguistic truth degrees

Heterogeneous multi-criteria group decision making (MCGDM) is a hot topic in the decision analysis field. This paper proposes a fairness-concern-based LINMAP (Linear Programming Technique for Multidimensional Analysis of Preference) method for heterogeneous MCGDM with hesitant fuzzy linguistic (HFL) truth degrees. Heterogeneous evaluation information includes crisp numbers, interval numbers, intuitionistic fuzzy values (IFVs), trapezoidal fuzzy numbers (TrFNs) and hesitant fuzzy sets (HFSs). This paper introduces the fairness concern to calculate the HFL consistency and the HFL inconsistency indices. Based on the framework of LINMAP, a bi-objective HFL programming model is built to derive the criteria weights, the positive ideal fairness vector (PIFV) and the negative ideal fairness vector (NIFV) for each decision maker (DM) simultaneously. Based on the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), a multi-objective programming model is built to obtain DMs’ weights. The alternatives ranking is derived by comprehensive collective relative closeness degrees. Finally, a real example is applied to verify effectiveness and superiority of this heterogeneous MCGDM method. The proposed heterogeneous MCGDM method provides a very useful approach for MCGDM with heterogeneous information.

Energy and economic assessment of a hybrid solar/gas heating system using a combined statistical-based multi-objective optimization method

The hybrid solar/gas heating systems are considered as promising technologies to reduce energy consumption and ecological impact, for a more sustainable environment. However, the complexity related to the process of hybridization often calls for multi-objective optimization problems to deal with the constraining objectives. In this paper, a multi-objective optimization methodology combining genetic algorithm, design of experiments and dynamic simulations is proposed. The present work is carried out to maximize the primary energy saving ratio (PESR), solar fraction (SF) and to minimize the levelized cost of heat of a hybrid solar/gas heating system intended for an office building located in Algiers, Algeria. The performance analysis of three control strategy modes showed how important the control strategies can improve the SF and the PESR. The suggested control mode demonstrated a seasonal SF and PESR of 40.31% and 31.32%, respectively. The proposed multi-objective optimization approach gave a better comprehensive energy and economic performance by identifying the pareto fronts. The Linear Programming Technique for Multidimensional Analysis of Preference decision-making technique was employed to determine the best optimal solution. The optimized system exhibited SF of 72.65%, PESR of 42.07% and the Levelized Cost of Heat of 0.054 $/kWh as per the design parameters, collector area of 10 m2, tank volume of 1.7 m3 and a flow rate of 0.2 kg s−1. Overall, the results showed that the proposed methodology is time-efficient and can be applied to optimize hybrid solar/gas systems.

Technical and economic modeling and optimization of a Ford-Philips engine for power production

In this study, for the first time, the effects of various parameters including engine rotational speed, engine pressure, heater temperature, and piston stroke on technical (energy and exergy) and economic (cost savings and payback period) performance of a Ford-Philips 4-215 engine is investigated. Considering the different engine losses, a thermodynamic model of the engine is employed and validated with available experimental data. The results reveal that the increase in engine pressure and heater temperature, augments the capacity and efficiency of the engine, respectively, and increasing the engine rotational speed and piston stroke first shortens the payback period and thereafter makes it longer. Moreover, the engine parameters are designed from six different technical and economic standpoints, and the optimal parameters are selected using two well-known decision methods, namely, the linear programming technique for multidimensional analysis of preference (LINMAP) and the technique for order preference by similarity to ideal solution (TOPSIS). In one of the cases, from energy and economic viewpoints, engine parameters have been designed and the maximum electric power and minimum payback period have been gauged at 260.46 kW and 6.10 years, respectively. In another case, from the exergy perspective, the maximum exergy efficiency and minimum exergy destruction are 65.17 % and 6.81 kW, respectively. Finally, by performing a three-objective optimization (energy, exergy, and economic), the maximum output power and exergy efficiency are evaluated 258 kW and 44.5%, respectively at a 5-year payback period.

Multi-objective optimization of Stirling heat engine with various heat and mechanical losses

The finite time thermodynamics is used to perform the thermodynamic analysis of the Stirling heat engine in this paper, and the multi-objective optimization of the heat engine cycle is carried out by Non-dominated Sorting Genetic Algorithm II (NSGA-II). In addition to various heat transfer losses (thermal resistance, regeneration loss and heat leakage), there are also mechanical losses in the cycle. The temperature ratio (x) and volume compression ratio (λ) are taken as the optimization variables, the multi-objective optimization is carried out for four objectives of cycle dimensionless shaft power, braking thermal efficiency, dimensionless efficient power and dimensionless ecological function, and effects of the two variables on the characteristics of the four optimization objectives are analyzed. Three decision-making methods, Linear Programming Techniques for Multidimensional Analysis of Preference (LINMAP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Shannon Entropy, are used to compare and analyze the optimization results of different combinations of optimization objective. The results show that the deviation indexes of quadru-objective optimization are smaller than those of single-objective optimization results, and it means that comparing with the single-objective optimization, multi-objective optimization can make better balance the four objective functions.

Assessing Human Performance Influencing Factors through LINMAP and Bayesian Belief Networks

This study aims at identifying and ranking the performance influencing factors (PIFs) that cause error in human operations. The failure weight and rate for the tasks carried out by each operator were investigated. Assessing these factors reduces human error, hence increasing safety, efficiency and job satisfaction. The methods of linear programming technique for multidimensional analysis of preference (LINMAP) and Bayesian Belief Networks were used to investigate an aircraft tire manufacturing industry. All operators in the workshops were evaluated. Based on data analysis, the tasks of each operator were weighted and the potential error rate of each task was obtained. PIFs for each workshop were ranked and prioritized so that the factors having most influence can be easily distinguished and the tasks where the operators have the highest rate of failure, can be identified. The probability of human error was obtained and in a predictive model, it can be determined that when an error occurs, which factors are most influential in its occurrence. The method utilized does not include copious pairwise, exhausting and at times confusing comparisons for operators.

OSWMI: An objective-subjective weighted method for minimizing inconsistency in multi-criteria decision making

In Multi-Criteria Decision Making (MCDM), alternatives are evaluated by considering different criteria. In MCDM, there is a requirement to integrate the objective and subjective weights, since the objective weighting methods ignore the decision-maker’s (DM’s) experiences and the subjective weighting methods ignore the performance ratings of the alternatives with respect to different criteria. To integrate the two types of weights and evaluate the best alternative, three well-established methods, namely “CRiteria Importance Through Intercriteria Correlation (CRITIC)”, “Best Worst Method (BWM)”, and “LINear programming techniques for Multidimensional Analysis of Preferences (LINMAP)” are considered in our study. Based on these methods, we have proposed a new method, namely “Objective-Subjective Weighted method for Minimizing Inconsistency (OSWMI)” which considers both pairwise comparisons of the criteria and alternatives along with their corresponding performance ratings. We have first improved both the methods, CRITIC (named as improved CRITIC) and LINMAP (named as LINMAP II). Finally, the proposed OSWMI method is developed by integrating the improved CRITIC method, BWM, and LINMAP II using a multi-objective non-linear programming (MONLP) model. The OSWMI method may reduce the problem of strategic weight manipulation, since the integrated weights and the two ideal solutions are priori unknown and obtained simultaneously for selecting the best alternative. A case study of the web service selection is used to demonstrate the implementation of the OSWMI method. From the analysis, the proposed OSWMI method reveals a promising result. Further, sensitivity of the OSWMI method is checked by using the standard regression coefficients obtained by multiple linear regression.

Uncertainty handling in wellbore trajectory design: a modified cellular spotted hyena optimizer-based approach

It is vital to optimize the drilling trajectory to reduce the possibility of drilling accidents and boosting the efficiency. Previously, the wellbore trajectory was optimized using the true measured depth and well profile energy as objective functions without considering uncertainty between the actual and planned trajectories. Without an effective management of the uncertainty associated with trajectory planning, the drilling process becomes more complex. Prior techniques have some drawbacks; for example, they could not find isolated minima and have a slow convergence rate when dealing with high-dimensional problems. Consequently, a novel approach termed the “Modified Multi-Objective Cellular Spotted Hyena Optimizer” is proposed to address the aforesaid concerns. Following that, a mechanism for eliminating outliers has been developed and implemented in the sorting process to minimize uncertainty. The proposed algorithm outperformed the standard methods like cellular spotted hyena optimizer, spotted hyena optimizer, and cellular grey wolf optimizer in terms of non-dominated solution distribution, search capability, isolated minima reduction, and pareto optimal front. Numerous statistical analyses were undertaken to determine the statistical significance of the algorithm. The proposed algorithm achieved the lowest inverted generational distance, spacing metric, and error ratio, while achieving the highest maximum spread. Finally, an adaptive neighbourhood mechanism has been presented, which outperformed fixed neighbourhood topologies such as L5, L9, C9, C13, C21, and C25. Afterwards, the technique for order preference by similarity to ideal solution and linear programming technique for multidimensional analysis of preference were used to provide the best pareto optimal solution.

A heterogeneous opinion-driven decision-support model for tourists’ selection with different travel needs in online reviews

The advancement of tourism websites has greatly improved the travelling experiences of tourists. One such way is the recommendation of restaurants by tourism websites. As a result, online restaurant reviews have grown tremendously in recent times. Using online reviews of restaurants in Ghana, this article deeply examines tourists’ restaurant experiences. Specifically, we employ unsupervised machine learning techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) and K-means algorithms to detect restaurant factors and evaluation attributes. We further develop an improved probabilistic linguistic linear programming technique for multidimensional analysis of preference (PL-LINMAP) to derive the attributes’ weight importance for different tourist groups. Additionally, we propose an uncertain decision-support model known as probabilistic linguistic Measurement Alternatives and Ranking according to the COmpromise Solution (PL-MARCOS) to aid different tourist groups in satisfactory restaurant selection. This study provides a comprehensive model for restaurant managers in understanding heterogeneous tourist preferences.

A multi-granular linguistic distribution-based group decision making method for renewable energy technology selection

The scarcity of resources requires a decrease in nonrenewable energy consumption, which progressively promotes the development of renewable energy due to its immense potential and environmental friendliness. Hence, the use of renewable energy technology is critical for realizing the economic effect, the environment effect and the social benefit unified. Generally, renewable energy technology selection is treated as a multiple criteria group decision making problem. However, decision makers are not allowed to express multiple preferences via personalized linguistic distribution assessments deliberating on diverse criteria in the existing approaches. This work proposes a multi-granular linguistic distribution-based group decision-making method by linking multi-granular linguistic distribution assessments and LINMAP (Linear Programming Technique for Multidimensional Analysis of Preference) method with a mathematical model that can simultaneously yield the credible weights of the considered criteria and prioritize the sequence of optimal renewable energy technologies. To this end, the linguistic distribution-based Hellinger distance measure and linguistic hierarchy-based multi-granular linguistic distribution transformation method are proposed. The decision framework is applied to a case study of power generation-based technology selection, generating reliable criteria weights and yielding acceptable outcomes based on collected assessments. Eventually, the sensitivity analysis and comparative analysis are conducted to verify the feasibility and practicability of our proposal. This flexible decision support technique is geared towards managers and strives to provide reference and inspiration for renewable energy technology selection.

Multi-objective optimization of tensile properties of the corrugated composite sheet

Corrugated sheets with optimized mechanical properties are crucial for lightweight design in industrial applications. This study considered and optimized a corrugated sheet with a sinusoidal profile to enhance elastic modulus, tensile-bending coupling, and weight reduction. For this aim, first, flat specimens consisting of E-glass woven fiber and epoxy resin were made by hand lay-up method, following ASTM D3039. The tensile test determined young’s modulus of flat samples. Afterward, two molds with supports were fabricated. The corrugated specimens were constructed and exposed to a standard tensile test. The finite element analysis was used to simulate the tensile test of corrugated samples. The numerical force-displacement curve is derived from numerical analysis and verified by experimental results. After that, two multi-objective optimization problems, mass-constraint and global optimization, were implemented. Analytical formulations were verified by numerical and experimental results and utilized for optimization purposes. The genetic algorithm was used to examine and confirm trade-off behavior between objective functions. The Pareto fronts diagrams for mentioned two multi-objective optimization problem were obtained. Finally, the optimum parameters are calculated by using the LINMAP (Linear Programming Technique for Multi-dimensional Analysis of Preference) method.

Hesitant Fuzzy 2-Dimension Linguistic Programming Technique for Multidimensional Analysis of Preference for Multicriteria Group Decision Making

The hesitant fuzzy 2-dimension linguistic element (HF2DLE) allows decision makers to express the importance or reliability of each term included in a hesitant fuzzy linguistic element as a linguistic term. This paper investigates a programming technique for multidimensional analysis of preference for hesitant fuzzy 2-dimension linguistic multicriteria group decision making. Considering the flexibility of HF2DLEs in expressing hesitant qualitative preference information, we first adopt HF2DLEs to depict both the evaluation values of alternatives and the truth degrees of alternative comparisons. To calculate the relative closeness degrees (RCDs) of alternatives, the Euclidean distances between HF2DLEs are defined. Based on RCDs and preference relations on alternatives, the group consistency and inconsistency indices are constructed, and a bi-objective hesitant fuzzy 2-dimension linguistic programming model is established to derive the criteria weights and positive and negative ideal solutions. Since the objective functions and partial constraint coefficients of the established model are HF2DLEs, an effective solution is developed, through which the RCDs can be calculated to obtain the individual rankings of alternatives. Furthermore, a single-objective assignment model is constructed to determine the best alternative. Finally, a case study is conducted to illustrate the feasibility of the proposed method, and its effectiveness is demonstrated by comparison analyses.