Search Results (47)

The electrification of field service fleets introduces complex constraints: shift limits, overtime fairness, and battery–range feasibility. This paper proposes the Multi-Shift Single Electric Vehicle Routing Problem under Possibilistic Uncertainty (MS-SEVRP-PU), a formulation focused on a single-vehicle multi-shift planning unit and capturing imprecise travel/service times and state-of-charge dynamics. Travel durations and energy consumption are modelled as triangular fuzzy numbers to reflect expert knowledge when probabilistic data is limited. A closed-form credibility function evaluates overtime risk, while an Ordered Weighted Averaging (OWA) aggregation of per-shift risks ensures fairness by discouraging systematic overload on specific shifts. To solve this multi-objective problem, we develop a Pareto-Conditioned Transformer with risk-aware and battery-conscious large neighbourhood search (PCT-RABLNS), combining a preference-conditioned attention policy with targeted local search. Computational experiments on calibrated municipal maintenance case studies indicate that PCT-RABLNS improves hypervolume by 2–5% over strong baselines and reduces maximum shift overtime risk by 15–25%, with a marginal makespan overhead of only 1–3%. The results demonstrate that the proposed framework is a promising decision-support approach for energy-aware, risk-fair, and operationally compliant planning of single-vehicle, multi-shift electric service operations, jointly integrating multi-shift routing, fuzzy uncertainty, and preference-conditioned reinforcement learning. The paper also discusses how the framework can be extended to multi-vehicle settings. Full article

Locust outbreaks are major biological disturbances in grassland ecosystems of arid and semi-arid regions. Accurate identification of locust suitable habitats is important for regional monitoring and management. However, direct comparisons between multi-criteria analysis (MCA) and species distribution models (SDMs) under a unified framework remain limited. In this study, we compared these two approaches for dominant locust species in Xinjiang, China, including Calliptamus italicus, Gomphocerus sibiricus, and Locusta migratoria manilensis. We used the same environmental variables and occurrence records for all models. The MCA methods included the analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), and ordered weighted averaging (OWA). The SDMs included the generalized linear model (GLM), maximum entropy model (MaxEnt), extreme gradient boosting (XGBoost), and an ensemble model. The results showed that SDMs had higher area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) values than MCA under the internal point-based evaluation framework, although both approaches effectively identified locust-suitable habitats. The two approaches also showed high spatial agreement in moderately and highly suitable habitats, with Jaccard indices of 0.88–0.92, and consistently identified the northern slopes of the Tianshan Mountains, the Ili River Valley, and the margins of the Junggar Basin as core suitable areas. These results indicate that the two approaches are complementary for locust monitoring and management. Full article

Load forecasting is a key component of power network planning and an essential approach to achieving the efficient cooperative optimization of integrated economic energy services. To improve the accuracy of the power load prediction and ensure the stable dispatch of power grid, this paper takes County A as a case study. The fish bone diagram method is applied to analyze the influence of four categories of factors on the county’s power load, and stepwise regression, the unit energy consumption method, and an optimized grey model are adopted to forecast and analyze the planned load of the county over the past 5 years. In addition, the spatial load density method, the optimized grey prediction model, and the General Regression Neural Network (GRNN) are used to predict and analyze the county’s planned power grid load based on data from the past ten years. The Ordered Weighted Averaging (OWA) operator is then applied to integrate the results, and the predictive performance of different methods is assessed with an evaluation function. The results show that this combined multi-method approach achieves a higher accuracy. It also accounts for the evolving political, economic, and social conditions of the country, making the predictions more useful for power grid planning. Based on these findings, corresponding countermeasures and suggestions are proposed to support the improvement of spatial planning for electric power facilities in County A. Full article

Decision-making and planning in eco-wellness tourism can vary depending on time, resources, and the perspectives of stakeholders, as it is often challenging to generalize the results of decision-making models across different scenarios. Hence, the primary objective of this study was to propose a scenario-based framework for optimising eco-wellness tourism development. For this purpose, maps of 26 factors affecting the evaluation of nature-based eco-wellness tourism, including water, climatic, and kinetic therapies, were used in the Ardabil province of Iran. Weighted criteria maps are integrated into suitability maps for various wellness tourism products under different scenarios, ranging from very pessimistic to very optimistic, using the Ordered Weighted Averaging (OWA) operator. Then, to identify areas of consensus, scenario-based maps for water, climate, and kinetic therapies are combined. In the very pessimistic (optimistic) scenario, climate-only therapy accounts for 0.91% (2.23%), water-only therapy for 1.07% (8.44%), and kinetic-only therapy for 3.5% (5.81%) of the area. The most significant expansion is observed in areas integrating all three therapies—climate, water, and kinetic—which increase from 3.23% in the very pessimistic scenario to 14.5% in the very optimistic scenario. The findings have substantial insights for policymakers, tourism planners, and investors in developing and promoting unique eco-wellness experiences that benefit tourists. The methodical approach and choice of data and parameters in the study can be inspirational and adjustable for relevant studies. Full article

New energy vehicles (NEVs) have gained increasing favor among NEV consumers due to their dual advantages of “low cost” and “environmental friendliness.” In recent years, the share of NEVs in the global automotive market has been steadily rising. For instance, in the Chinese market, the sales of new energy vehicles in 2024 increased by 35.5% year-on-year, accounting for 70.5% of global NEV sales. However, as the diversity of NEV brands and models expands, selecting the most suitable model from a vast amount of information has become the primary challenge for NEV consumers. Although online service platforms offer extensive user reviews and rating data, the uncertainty, inconsistent quality, and sheer volume of this information pose significant challenges to decision-making for NEV consumers. Against this backdrop, leveraging the strengths of the quasi OWA (QOWA) operator in information aggregation and interval basic uncertain linguistic information (IBULI) information aggregation and two-dimensional information representation of “information + quality”, this study proposes a large-scale group data aggregation method for decision support based on the IBULIQOWA operator. This approach aims to assist consumers of new energy vehicles in making informed decisions from the perspective of information quality. Firstly, the quasi ordered weighted averaging (QOWA) operator on the unit interval is extended to the closed interval $\left[0,\tau \right]$, and the extended basic uncertain information quasi ordered weighted averaging (EBUIQOWA) operator is defined. Secondly, in order to aggregate groups of IBULI, based on the EBUIQOWA operator, the basic uncertain linguistic information QOWA (BULIQOWA) operator and the IBULIQOWA operator are proposed, and the monotonicity and degeneracy of the proposed operators are discussed. Finally, for the problem of product decision making in online service platforms, considering the credibility of information, a product decision-making method based on the IBULIQOWA operator is proposed, and its effectiveness and applicability are verified through a case study of NEV product decision making in a car online service platform, providing a reference for decision support in product ranking of online service platforms. Full article

Recommendation systems applied to tourism are widely recognized for improving the visitor’s experience in tourist destinations, thanks to their ability to personalize the trip. This paper presents a hybrid approach that combines Machine Learning techniques with the Ordered Weighted Averaging (OWA) aggregation operator to achieve greater accuracy in user segmentation and generate personalized recommendations. The data were collected through a questionnaire applied to tourists in the different points of interest of the Special, Tourist and Cultural District of Riohacha. In the first stage, the K-means algorithm defines the segmentation of tourists based on their socio-demographic data and travel preferences. The second stage uses the OWA operator with a disjunctive policy to assign the most relevant cluster given the input data. This hybrid approach provides a recommendation mechanism for tourist destinations and their cultural heritage. Full article

Conducting ecological restoration has emerged as a critical governance strategy for enhancing ecosystem diversity, stability, and sustainability. The scientific identification of priority restoration areas is a prerequisite for effective ecological restoration projects. Current research on identifying priority restoration zones predominantly relies on administrative-scale frameworks, and the reliability and scientificity of the identified results are somewhat insufficient. To address this gap, this study selected Dali Prefecture in Yunnan Province, a region characterized by dense river networks, as the research area to identify the priority areas of ecological restoration. In view of the application of the InVest model in watershed-scale restoration, biodiversity assessment, and other fields, we utilize sub-watershed units and the InVEST model, and five key ecosystem services—water conservation, water purification (N/P), habitat quality, climate regulation, and soil retention—were quantified. Temporal changes in these services from 2015 to 2020 were analyzed alongside ecological risk assessments and restoration zoning. Priority areas were further identified through Ordered Weighted Averaging (OWA) operators under varying decision-making preferences. The optimal threshold for watershed delineation was determined as 11.04 km², resulting in 1513 refined sub-watershed units after correction, with 71.59% concentrated in the 10–50 km² range. A spatial analysis revealed an east-to-west gradient in ecosystem service distribution, where eastern regions consistently exhibited lower values compared to central and western areas. From 2015 to 2020, soil retention per unit area increased by 5.09%, while water purification for N and P showed marginal improvements of 0.97% and 0.39%, respectively. Conversely, water conservation declined significantly by 10.00%, with carbon sequestration and biodiversity protection experiencing slight reductions of 1.74% and 1.92%, all within a 2% variation margin. Ecological risk zoning identified low-risk areas (grades 1–3) predominantly in western and northeastern Dali, encompassing 1094 sub-watersheds (77.36% by count and 73.92% by area), while high-risk zones (grades 4–5) covered 386 units (26.08% by area). Integrating ecological quality and risk levels, the study area was classified into four functional zones: Zone I (high quality, high risk), Zone II (low quality, high risk), Zone III (low quality, low risk), and Zone IV (high quality, low risk). With increasing risk tolerance, the priority restoration areas expanded from eastward to central regions. Based on the scenario simulations under ecological priority, status quo, and development-oriented policies, the critical restoration areas include the Sangyuan River Basin, mid-reach of the Juli River, and upper Miyu River. This methodology provides a theoretical and technical foundation for ecosystem service enhancement and degraded ecosystem rehabilitation in Dali Prefecture and similar regions. Full article

Ordered functional weighted averaging (OFWA) operators are a generalization of the well-known ordered weighted averaging (OWA) operators in which functions, instead of single values, are considered as weights. This fact offers an extra level of flexibility; for example, in multi-criteria decision-making, it can be used to aggregate available information and provide recommendations. This paper furthers the analysis of these general operators, studying how they can be combined to obtain conservative and aggressive perspectives from experts and studying the algebraic structure of the whole set of these operators. Full article

This paper introduces a fatigue detection model specifically designed for immersive virtual reality (VR) environments, aimed at facilitating upper limb rehabilitation for individuals with spinal cord injuries (SCIs). The model’s primary application centers on the Box-and-Block Test, providing healthcare professionals with a reliable tool to monitor patient progress and adapt rehabilitation routines. At its core, the model employs data fusion techniques via ordered weighted averaging (OWA) operators to aggregate multiple metrics captured by the VR rehabilitation system. Additionally, fuzzy logic is employed to personalize fatigue assessments. Therapists are provided with a detailed classification of fatigue levels alongside a video-based visual representation that highlights critical moments of fatigue during the exercises. The experimental methodology involved testing the fatigue detection model with both healthy participants and patients, using immersive VR-based rehabilitation scenarios and validating its accuracy through self-reported fatigue levels and therapist observations. Furthermore, the model’s scalable design promotes its integration into remote rehabilitation systems, highlighting its adaptability to diverse clinical scenarios and its potential to enhance accessibility to rehabilitation services. Full article

The evolution of beyond fifth generation (B5G) wireless networks poses significant technical and economic challenges across urban, suburban, and rural areas, demanding increased capacity for users whose positions continually change. This study investigated the dynamic positioning of an unmanned aerial vehicle (UAV), acting as a mobile base station (MoBS) to enhance network efficiency and meet ground terminals (GTs) expectations for data rates, particularly in emergency scenarios or temporary events. While UAVs show great promise, existing research often assumes certainty in network architecture, overlooking the complexities of unpredictable user movements. We introduce a decision-making framework utilizing the ordered weighted averaging (OWA) operator to address uncertainties in GT locations, enabling the optimization of UAV trajectories to maximize the overall network data rate. An optimization problem is formulated by modeling GT dynamics through a Markov process and discretizing UAV movements while accounting for communication thresholds and movement constraints. Extensive simulations reveal that our approach significantly improves expected data rates by up to 48% compared to traditional fixed base stations (BSs) and predefined UAV movement patterns. This research underscores the potential of UAV-assisted networks to bolster communication reliability while effectively managing dynamic user movements to maintain optimal quality of service (QoS). Full article

When information is incomplete or uncertain, Fermatean hesitant fuzzy sets (FHFSs) can provide more information to help decision-makers deal with more complex problems. Typically, determining attribute weights assumes that each attribute has a fixed influence. Introducing probability information can enable one to consider the stochastic nature of evaluation data and better quantify the importance of the attributes. To aggregate data by considering the location and importance degrees of each attribute, this paper develops a Fermatean hesitant fuzzy multi-attribute decision-making (MADM) method with probabilistic information and an ordered weighted averaging (OWA) method. The OWA method combines the concepts of weights and sorting to sort and weigh average property values based on those weights. Therefore, this novel approach assigns weights based on the decision-maker’s preferences and introduces probabilities to assess attribute importance under specific circumstances, thereby broadening the scope of information expression. Then, this paper presents four probabilistic aggregation operators under the Fermatean hesitant fuzzy environment, including the Fermatean hesitant fuzzy probabilistic ordered weighted averaging/geometric (FHFPOWA/FHFPOWG) operators and the generalized Fermatean hesitant fuzzy probabilistic ordered weighted averaging/geometric (GFHFPOWA/GFHFPOWG) operators. These new operators are designed to quantify the importance of attributes and characterize the attitudes of decision-makers using a probabilistic and weighted vector. Then, a MADM method based on these proposed operators is developed. Finally, an illustrative example of selecting the best new retail enterprise demonstrates the effectiveness and practicality of the method. Full article

The 360 degree feedback evaluation method is a multidimensional, comprehensive assessment method. Evaluators may hesitate among multiple evaluation values and be simultaneously constrained by the biases and cognitive errors of the evaluators, evaluation results are prone to unfairness and conflicts. To overcome these issues, this paper proposes a consensus-based 360 degree feedback evaluation method with linguistic distribution assessments. Firstly, evaluators provide evaluation information in the form of linguistic distribution. Secondly, utilizing an enhanced ordered weighted averaging (OWA) operator, the model aggregates multi-source evaluation information to handle biased evaluation information effectively. Subsequently, a consensus-reaching process is established to coordinate conflicting viewpoints among the evaluators, and a feedback adjustment mechanism is designed to guide evaluators in refining their evaluation information, facilitating the attainment of a unanimous evaluation outcome. Finally, the improved 360 degree feedback evaluation method was applied to the performance evaluation of the project leaders in company J, thereby validating the effectiveness and rationality of the method. Full article

The ordered weighted averaging (OWA) operator was proposed by Yager back in 1988 and constitutes a parameterized family of aggregation functions between the minimum and the maximum. The purpose of this paper is to perform a bibliometric review of this aggregation operator during the last 35 years through the Web of Science (WoS) Core Collection database and the Visualization of Similarities (VOS) viewer software. The results show that the OWA operator is an increasingly popular aggregation operator, especially in Computer Science. The results also allow the assertion that Yager, as expected, is still the most influential and productive author. Moreover, the study reveals that institutions from over 80 countries have contributed to OWA research, highlighting the high presence of Chinese universities and the emergence of Pakistani ones. Other interesting findings are presented to provide a comprehensive and up-to-date analysis of the OWA operator literature. Full article

Agricultural product prices are subject to various uncertainties, including unpredictable weather conditions, pest infestations, and market fluctuations, which can significantly impact agricultural yields and productivity. Accurately assessing and understanding price is crucial for farmers, policymakers, and stakeholders in the agricultural sector to make informed decisions and implement appropriate risk management strategies. This study used the ordered weighted average (OWA) operator and its extensions as mathematical aggregation techniques incorporating ordered weights to capture and evaluate the factors influencing price variation. By generating different vectors related to different inputs to the traditional formulation, it is possible to aggregate information to calculate and provide a new view of the outcomes. The results of this research can help enhance risk management practices in agriculture and support decision-making processes to mitigate the adverse effects of price. Full article

A well-researched field is the development of Computer Aided Diagnosis (CADx) Systems for the benign-malignant classification of abnormalities detected by mammography. Due to the nature of the breast parenchyma, there are significant uncertainties about the shape and geometry of the abnormalities that may lead to an inaccurate diagnosis. These same uncertainties give mammograms a fuzzy character that is essential to the application of fuzzy processing. Fuzzy set theory considers uncertainty in the form of a membership function, and therefore fuzzy sets can process imperfect data if this imperfection originates from vagueness and ambiguity rather than randomness. Fuzzy contrast enhancement can improve edge detection and, by extension, the quality of related classification features. In this paper, classical (Linguistic hedges and fuzzy enhancement functions), advanced fuzzy sets (Intuitionistic fuzzy set (ΙFS), Pythagorean fuzzy set (PFS), and Fermatean fuzzy sets (FFS)), and a Genetic Algorithm optimizer are proposed to enhance the contrast of mammographic features. The advanced fuzzy sets provide better information on the uncertainty of the membership function. As a result, the intuitionistic method had the best overall performance, but most of the techniques could be used efficiently, depending on the problem that needed to be solved. Linguistic methods could provide a more manageable way of spreading the histogram, revealing more extreme values than the conventional methods. A fusion technique of the enhanced mammography images with Ordered Weighted Average operators (OWA) achieves a good-quality final image. Full article