Preference Ranking Research Articles

The Influences of Ties in Preference on Market Matching

The ties of preference and priorities are commonly seen and widely used in daily life. When the agent considers the priority order of some selected objects to be the same, the final matching result may be affected. People sometimes require strict preference ranking in order to facilitate the use of matching models, which can cause controversy and dissatisfaction with people in the matching. In order to effectively solve the above problems, this paper surveys what these impacts are and how they affect the final result of matching. According to the analysis, it is proven that when there is a preference of indifference, Pareto efficiency is unstable and the deferred acceptance algorithm cannot be used for market matching. In addition, the Pareto-improving draft mechanism has proven to be powerful without imposing a strict preference. This mechanism can also avoid the complex process and other negative consequences of handing ties arbitrarily.

Justification within and between social contexts with the possibility of choice deferral

This paper provides axiomatic characterizations for a class of models where a decision maker (henceforth, DM) maximizes a single preference ranking subject to a shortlist that depends on the underlying choice set and social context. Thus, the primitive is a dataset of choices from every choice set – social context pair that is called the context. The DM is supposed to choose the most preferred option from the shortlist of each context. However, due to existence of conflicting norms (or norms uncertainty), he/she might choose no option in some contexts. Therefore, unlike existing justification models, choice deferral is possible in our framework.

Rankings without U.S. News: A revealed preference approach to evaluating law schools

AbstractSince their inception in 1989, the U.S. News & World Report law school rankings have influenced how schools, students, and the legal profession itself think about legal education. In the Fall of 2022, however, several of the most selective law schools formally withdrew from the annual rankings. In so doing, these schools laid bare longstanding criticisms of the rankings' questionable criteria and opaque methodology. While the long‐term effect of this boycott remains to be seen, school rankings are likely here to stay. In this Article we design a more informative approach to rankings, based on actual decisions students make. Using individual‐level data provided by the Law School Admissions Council (LSAC), we analyze the universe of applicants to U.S. law schools for the period 1988 through 2017. In so doing, we are the first to create a revealed preference ranking based solely on where applicants matriculate given offers of admission. Our approach relies neither on potentially faulty data collection from schools nor arbitrary decisions about which factors to emphasize in rankings, thereby minimizing the scope for manipulation. It also allows us to quantify the magnitude of differences in preferences among schools and to test their statistical significance. Matriculants reveal a strong preference for a handful of the most selective schools; outside of the top tier, however, matriculants do not appear to draw meaningful distinctions between schools ranked adjacently or even near to each other. While existing school rankings sow more confusion than clarity, our analysis provides a rigorous and transparent alternative, and a blueprint for redesigning school rankings.

Woven fabric selection and grading for ladies’ winter shawls using multicriteria decision-making approach

Purpose Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making. Design/methodology/approach In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method. Findings The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market. Practical implications In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness. Originality/value The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

Enhancing Polybutylene Terephthalate (PBT) Composites: A PROMETHEE-Based Comparative Study of Reinforcement Materials

Polybutylene terephthalate (PBT) composites hold significant potential across various industrial applications due to their desirable mechanical, thermal, and electrical properties. In this study, we utilized the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) method to evaluate six alternative reinforcement materials for PBT composites: Glass Fibers (GF), Carbon Fibers (CF), Natural Fibers (NF), Carbon Nanotubes (CN), Nano-clay Particles (NP), and Aramid Fibers (AF). The evaluation parameters considered were Tensile Strength (MPa), Flexural Strength (MPa), Thermal Conductivity (W/mK), Electrical Conductivity (S/m), and Cost ($). Through comprehensive analysis, Carbon Nanotubes emerged as the top-ranked reinforcement material, exhibiting exceptional performance across all evaluation parameters. With high Tensile Strength, Flexural Strength, and Thermal Conductivity, combined with significant Electrical Conductivity, Carbon Nanotubes demonstrated their suitability for demanding applications. Additionally, while the Cost factor was comparatively higher, its superior performance justifies the investment. Conversely, Natural Fibers received the lowest rank among the alternatives. Despite potential advantages in cost-effectiveness and environmental sustainability, Natural Fibers exhibited inferior mechanical and thermal properties compared to other materials. Their low Tensile Strength, Flexural Strength, and negligible Electrical Conductivity highlight limitations in performance for many industrial applications. This study provides valuable insights for engineers and material scientists in selecting suitable reinforcement materials for PBT composites based on specific performance criteria. The PROMETHEE method offers a systematic approach to decision-making, facilitating informed choices in material selection for diverse applications. Future research could explore optimization strategies and further investigate the properties and potential applications of emerging reinforcement materials for PBT composites.

Conflict analysis of multiple decision-makers ecological environment governance of water resources based on graph model with heterogeneous preference in China's South-to-North water transfer project

Ecological environment governance of water resources is the foundation and key measures to ensure good water quality in China's South-to-North Water Transfer Project. However, as a limited rational economic person, considering their economic benefit, different involvers in China's South-to-North Water Transfer Project show different types of preference relations and make different action decisions as well as play conflicts with each other, which have a negative effect on ecological environmental governance. To overcome above-described shortcomings, heterogeneous preferences are integrated into graph model for conflict resolution (GMCR) to resolve ecological environment governance conflict of water resources. Specifically, first, four different structures of preference expression, including preference orderings, utility functions, multiplicative preferences, and additive preferences, are used to represent decision-maker's (DM's) preference information over states. Then, a novel preference ranking method is designed to calculate the ordinal preferences over all states by using matrix representation. Based on this, a set of heterogeneous stability definitions is proposed in GMCR to describe human diversified choice and behavior in detail, and it is applied to the ecological environment governance conflict of water resources in China's South-to-North Water Transfer Project. The heterogeneous stability analysis result showed that incentive policies used by water resources agencies can effectively encourage local governments and enterprises to actively participate in the ecological environment governance, and promote the ecological environment governance conflict to evolve into a state of equilibrium.

Evaluation of new scintillation crystals with MCDM methods for brain PET

Brain Positron Emission Tomography (PET) imaging has been increasingly important in recent decades for improved detection and staging of brain cancer and other brain illnesses. Many elements, including the choice of radiation detecting medium, contribute to the overall image quality used to characterize a PET system's performance. In our previous study, novel transparent optical scintillator crystals for brain PET system were simulated. Therefore, there is a need to evaluate these materials using advanced computational techniques. Hence, in this study, we evaluated and compared them using two Multi-Criteria Decision-Making (MCDM) techniques, namely Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Visekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The crystals used in this study were strontium hafnate (SHO), gadolinium aluminum gallium garget (GAGG), gadolinium yttrium gallium aluminum garget (GYGAG), gadolinium lutetium gallium aluminum garget (GLuGAG), and lastly lutetium oxyorthosilicate (LSO) for comparison. Density, effective atomic number, energy resolution, light output, and decay time were selected as important criteria. Importance weights of each criteria were then assigned by considering the high resolution and high sensitivity detectors. With both MCDM methods, the results showed that SHO outranked the other scintillator materials followed by LSO and GLuGAG. GYGAG, and GAGG is revealed as the least favorable crystals, in agreement with the previous simulation studies. This study can be extended by including more scintillators as they become available in the future.

Forum Text Processing and Summarization

Frequently Asked Questions (FAQs) are extensively studied in general domains like the medical field, but such frameworks are lacking in domains such as software engineering and open-source communities. This research aims to bridge this gap by establishing the foundations of an automated FAQ Generation and Retrieval framework specifically tailored to the software engineering domain. The framework involves analyzing, ranking, performing sentiment analysis, and summarization techniques on open forums like StackOverflow and GitHub issues. A corpus of Stack Overflow post data is collected to evaluate the proposed framework and the selected models. Integrating state-of-the-art models of string-matching models, sentiment analysis models, summarization models, and the proprietary ranking formula proposed in this paper forms a robust Automatic FAQ Generation and Retrieval framework to facilitate developers' work. String matching, sentiment analysis, and summarization models are evaluated, and F1 scores of 71.31%, 74.90%, and 53.4% were achieved. Given the subjective nature of evaluations in this context, a human review is used to further validate the effectiveness of the overall framework, with assessments made on relevancy, preferred ranking, and preferred summarization. Future work includes improving summarization models by incorporating text classification and summarizing them individually (Kou et al, 2023), as well as proposing feedback loop systems based on human reinforcement learning. Furthermore, efforts will be made to optimize the framework by utilizing knowledge graphs for dimension reduction, enabling it to handle larger corpora effectively

Sustainability Strategy of Gringsing Woven Fabrics on the Basis of Multifunction Through the PROMETHEE Approach

Gringsing weaving is a multifunctional traditional fabric of Bali, it’s used in various activities such as traditional ceremonies, war rituals, souvenirs, fashion, and medication. The objectives of this research are: (1) Identifying respondent's perceptions of the multifunctionality of gringsing woven fabric (2) Analysing the sustainability strategy for gringsing woven fabric as a typical woven fabric of Tenganan Village. This research uses analytical methods, descriptive analysis to analyse respondents' perceptions of the multifunctionality of gringsing woven fabric involving 19 respondents (consisting of craftsmen and stakeholders) and Preference Ranking Organization METHODs for Enrichment Evaluation (PROMETHEE) analysis to analyse alternative sustainability strategies for gringsing woven. The research results show that respondents have a good perspective on the potential of gringsing woven as a modern fashion classic product with sustainable principles. An alternative priority sustainability strategy to make gringsing woven fabric a sustainable industry is to develop innovations in modifying gringsing woven fabric motifs.

Red Yeast Rice and Optimal Fermentation Periods Improve the Quality of Esan Fermented Fish Sausage.

Esan fermented fish sausage (EFFS) has an unappealing off-white color. The incorporation of an appropriate amount of red yeast rice (RYR) and the selection of an optimal fermentation period may yield visually appealing, high-quality sausages. This study is aimed at investigating the effects of different RYR levels (0, 0.35, and 0.7%) and fermentation periods (0, 2, 4, and 6 days) on the quality parameters of EFFS. The following parameters were examined for raw EFFS: CIE color values (L∗, a∗, and b∗), microbial analyses (total viable count, lactic acid bacteria, and yeast and mold counts), titratable acidity (TA), pH, weight loss, cooking loss, texture profile analysis (TPA), and sensory evaluation (color, odor, hand-feel texture, overall acceptability, and overall preference ranking). The quality parameters of the cooked EFFS were CIE color values and sensory evaluation (color, odor, mouthfeel, texture, flavor, overall acceptability, and overall preference ranking). The results showed that 0.35 and 0.7% RYR increased the a∗ (red/green) values of raw and cooked EFFS but decreased the L∗ (lightness) and b∗ (yellow/blue) values. These RYR levels significantly improved the sensory color, overall acceptability, and overall preference ranking of the raw and cooked EFFSs. However, no statistical differences were observed between the effects of 0.35 and 0.7% RYR. RYR levels did not affect the microbial analyses, TA, pH, weight loss, cooking loss, or TPA. Moreover, they had no effect on the odor and hand-feel texture of raw EFFS, or the odor, mouthfeel texture, or flavor of cooked EFFS. Therefore, RYR supplementation improved the color quality of the EFFSs without altering the other quality parameters, with 0.35% RYR deemed optimal. Moreover, the fermentation period significantly influenced most quality parameters, except CIE color values and sensory color perception of raw and cooked EFFSs. Most sensory parameters improved by day 2, remained unchanged until day 4, and then deteriorated on day 6.

Food Miles and Regional Logos: Investigating Consumer Preferences in the Midwestern United States

Regional food marketing initiatives in the United States include state-sponsored marketing programs, promotional efforts made by non-profit organizations, and retail-level supermarket campaigns. Some employ food miles, while others emphasize state boundaries or regions. Given that U.S. consumers are faced with these options, the objectives of this study are to (1) determine whether consumers have a clear preference ranking between three regional marketing logos currently seen in the marketplace, (2) estimate whether consumers are willing to pay a price premium for food mileage information, and if so, what mileage cutoffs are preferred, and (3) uncover whether displaying food mileage, regional marketing logos, or dual-displaying both sets of information is most preferred by consumers. To address these objectives, an artefactual field experiment featuring a series of non-hypothetical, random nth-priced auctions is conducted with 98 community participants in Wisconsin. The experimental auctions feature cheese displaying a regional marketing logo, a food mileage cutoff, or both simultaneously. A random-effects two-limit tobit model is used to fit the elicited bid data. Our results suggest regional logos referencing smaller geographic areas are preferred over state logos by U.S. consumers who are willing to pay a price premium. Consumers are not willing to pay a price premium for food mileage information unless it is within 50 miles. Our results also suggest larger distances do not meet consumers’ definition of local. Therefore, to appeal to consumers, federal and state agencies, retailers, and producers should consider marketing efforts targeting smaller regional areas. Such efforts could help shorten the food supply chain while providing consumers with an opportunity to make more sustainable food choices.

Preference Ranking Optimization for Human Alignment

Large language models (LLMs) often contain misleading content, emphasizing the need to align them with human values to ensure secure AI systems. Reinforcement learning from human feedback (RLHF) has been employed to achieve this alignment. However, it encompasses two main drawbacks: (1) RLHF exhibits complexity, instability, and sensitivity to hyperparameters in contrast to SFT. (2) Despite massive trial-and-error, multiple sampling is reduced to pair-wise contrast, thus lacking contrasts from a macro perspective. In this paper, we propose Preference Ranking Optimization (PRO) as an efficient SFT algorithm to directly fine-tune LLMs for human alignment. PRO extends the pair-wise contrast to accommodate preference rankings of any length. By iteratively contrasting candidates, PRO instructs the LLM to prioritize the best response while progressively ranking the rest responses. In this manner, PRO effectively transforms human alignment into aligning the probability ranking of n responses generated by LLM with the preference ranking of humans towards these responses. Experiments have shown that PRO outperforms baseline algorithms, achieving comparable results to ChatGPT and human responses through automatic-based, reward-based, GPT-4, and human evaluations.

Computing Multivalued Mathematical Morphology on Multiband Images Using Algorithms for Multicriteria Analysis

Mathematical morphology (MM) is a powerful tool for spatial multispectral and hyperspectral image analyses. However, MM was originally developed for single-band images in which each pixel is represented by a numerical value. The most commonly used method for extending MM to multiband images is to process each band independently without considering its correlations with other bands. This can lead to the creation of artificial false spectral signatures and result in object misidentification. Therefore, extending MM to multiband images requires the use of an adequate vector ordering strategy to fully exploit its potential. This work proposes new vector ordering algorithms for the computation of multivalued MM. A multicriteria analysis (MCA) system is used as a tool for establishing an ordering of vectors. Two MCA approaches, namely, an "analytic hierarchy process" and a "preference ranking organization method for enrichment evaluation," are developed to define ordering relations between vectors. To ensure the validity of the proposed vector ordering algorithms, the computed multivalued morphological profiles are compared using the proposed vector ordering approaches and conventional schemes. The results of applying the proposed vector ordering algorithms for computing morphological profiles show that good classification accuracies were achieved for urban structures in ROSIS hyperspectral images.

Optimizing Drug Delivery Vehicle with Multi-Criteria Decision Making (MCDM) - Based Excipient Selection

Excipients are used in drug delivery systems as a means of effectively delivering drugs to their target site. Multi-criteria decision-making (MCDM) methods are tools for decision-making that consider multidimensional factors. Such methods are a comparative technology used in medicine that combines individual criteria into the total assessment of selected alternatives. This study aims to enhance the solubility and bioavailability of drugs through the application of MCDM-based excipient selection. By incorporating the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), various excipients can be evaluated and ranked based on their suitability for specific applications, considering parameters related to drug solubility and bioavailability. The results highlight the potential of cyclodextrins (net flow: 0.0023) and Eudragit polymers (net flow: 0.0016) as preferred options for drug carriers, while Poloxamer 188 (P188) (net flow: -0.0030) is identified as the least preferred option. This study demonstrates the effectiveness of the PROMETHEE method in improving the performance of poorly soluble and bioavailable drugs, ultimately contributing to the development of new drug delivery systems. The findings have significant implications for therapeutic outcomes in the treatment of diseases.

Risk assessment of water inrush accident during tunnel construction based on FAHP-I-TOPSIS

This study introduces an innovative risk assessment method, namely FAHP-I-TOPSIS, for evaluating water inrush risks during tunnel construction. Its primary objective is to address the challenges posed by various fuzzy and uncertain factors in risk assessment, aiming to enhance the reliability of on-site risk evaluation. The method combines the Fuzzy Analytic Hierarchy Process for weight calculation and the Ideal Solution Similarity Ranking Preference Interval Technique for risk interval computation. Firstly, a comprehensive risk assessment framework is established, categorizing factors influencing tunnel water inrush into geological factors (Ni), construction technology and management (Mi), and the inherent vulnerability of the tunnel itself (Vi). These three indicator layers are further subdivided based on corresponding risk factors. The method is applied to the risk analysis of water inrush in the Shijingshan Tunnel in Zhuhai, analysing the risk weights of 16 influencing factors. It is observed that cracks and fault zones have the most significant impact on the occurrence of the incident. Through a comparison with traditional TOPSIS and VIKOR methods, which calculate a single risk value, the study demonstrates that FAHP-I-TOPSIS effectively integrates various fuzzy and uncertain factors, providing a reasonable level of risk assessment. It enhances adaptability to on-site errors, significantly improving the robustness of water inrush risk assessment during tunnel construction and sustainable management.

Double stochastic preference analysis on group decision making with interval additive preference relations

Interval additive preference relations (IAPRs) provide a valuable framework for representing uncertain pairwise comparisons among alternatives in group decision-making (GDM). Conventional GDM methods that incorporate IAPRs often treat preference values as uniform entities and establish dedicated conflict or consensus mechanisms for amalgamating individual opinions while overlooking the stochastic preferences and lacking the flexibility to address diverse decision requirements. Therefore, to account for the stochastic characteristics in both preference representation and group preference aggregation, this paper introduces an innovative double stochastic preference analysis for GDM with IAPRs. In the initial phase, stochastic additive preference relations are extracted from IAPRs using a specific density function. A best preference matrix is then constructed through a logarithmic consistency minimum deviation model. In the second phase, preference comparison intervals are synthesized to encapsulate the collective viewpoints of decision-makers. Subsequently, ranking acceptability indices and preference confidence degrees are computed through processing of the preference ranking judgment space, leading to the derivation of the final preference rankings. The experimental results, obtained through Monte Carlo simulations for a site selection case in Anhui Province, China, demonstrate the effectiveness and validity of the proposed method. The ranking results achieved a confidence degree exceeding 89%, offering insights into decision-makers’ weight distribution for various ranking scenarios.

Diversity and conservation status of herbal formulations used to treat different diseases in a semi-arid to sub-humid subtropical region

IntroductionThe current research aimed to discover the diversity and conservation status of medicinal plants used in herbal formulations to treat different diseases in Shakardara and adjacent areas, of Khyber Pakhtunkhwa, Pakistan. MethodsData were collected using semi-structured questionnaires about the preparation of monoherbal and polyherbal recipes that were used locally. Fidelity level, use value index, preference ranking method, and rapid vulnerability assessment were used for data interpretation. Relevant statistical tools (ANOVA and SPSS software) were used for data analysis. ResultsA total of 74 medicinal plants from 39 families were recorded that were used in the preparation of monoherbal and polyherbal formulation. In the study area, 146 recipes were used, of which 101 were monoherbal and 45 were polyherbal. Seeds were the most usable part of plants (28%), followed by fruits (23%) and leaves (21%). The highest informant consensus factor (1.00) was recorded for nail protection and growth, while the lowest (0.83) for wound healing. Withania coagulans Dunal was reported with the highest fidelity level and Peganum harmala L. was the most vulnerable species in the study area, with the highest threat score. ConclusionsConserving the knowledge and resources associated with the herbal remedies is essential for both human health and ecological balance. Monoherbal and polyherbal formulations of medicinal plants highlight the potential of traditional herbal remedies as valuable sources of alternative medicine. Further research in this field is crucial to explore the therapeutic potential and ensure the safe utilization of these natural remedies in modern healthcare.

Material Selection for Artificial Femur Bone Using PROMETHEE-GAIA Method

Abstract When replacing bones and implants, choosing the right materials for the artificial bone and orthopedic implants is crucial to the procedure’s success. In this work, a thorough assessment of the literature was followed by a thorough and rigorous evaluation of prospective materials for prosthetic femurs using a multicriteria decision-making process known as PROMETHEE-GAIA (Preference Ranking Organization METHod for Enrichment Evaluation and Geometric Analysis for Interactive Assistance). The proposed approach was validated using a total of 12 assessment parameters, including density, tensile strength, and ultimate tensile strength, and 17 candidate materials. The significance of the chosen criteria is well described. These 17 candidate implant materials and the 12 assessment criteria were used to develop a choice matrix. Rankings over the prepared matrix were produced using the PROMETHEE-GAIA program. Ti-6Al-7Nb, Ti-6Al-4V, and ASTM F1537, Standard Specification for Wrought Cobalt-28Chromium-6Molybdenum Alloys for Surgical Implants (UNS R31537, UNS R31538, and UNS R31539), Co-Cr-W emerged as the top contenders and were demonstrated as possible materials for effective artificial femur materials because of the assessment. With a large number of pertinent criteria and a wide range of materials, this study offers a framework for the selection of implant materials. It also emphasizes how choosing materials carefully may increase the durability and efficiency of orthopedic implants.

Medicinal Plants Used by Oromo Community in Kofale District, West-Arsi Zone, Oromia Regional State, Ethiopia.

The purpose of this study was to record the utilization of medicinal plants by the Oromo people in the Kofale District, Oromia Regional State, Ethiopia, to control human and animal health problems. Data regarding the use of medicinal plants were collected using ethnobotanical methods from 84 traditional medicine practitioners and 304 general informants sampled employing purposive and systematic random sampling methods, respectively, in the Kofale District. Data were analyzed using different indices, including a preference ranking exercise, informant consensus factor (ICF), fidelity level (FL) and relative popularity level (RPL). In the district, 106 medicinal plants were claimed to be used for the treatment of 43 human and 18 livestock illnesses, of which 75 (71%) were used to manage human health problems, 23 (21.5%) were used to treat both human and livestock ailments and eight (7.5%) were utilized to treat manage livestock health problems. Most (76.4%) plants were harvested from the wild. Leaves were the most commonly used plant part (55.6%) in remedy preparations. Skin diseases scored the highest ICF value (0.97), followed by gastrointestinal disorders (ICF = 0.95), cancer (ICF = 0.93), and hemorrhoids (ICF = 0.91). Medicinal plants that record the highest fidelity level (FL) (100%) and rank order priority (ROP) (100%) values included Justicia schimperiana, Embelia schimperi, Ekebergia capensis and Datura stramonium, which have been used to treat liver disorders, tapeworm infections, babesiosis, and rabies, respectively. There were significant differences (p<0.05) in the mean numbers of medicinal plants claimed by different social groups: older, illiterate, and traditional medicine practitioners reported higher mean numbers of medicinal plants than younger, literate, and general informants, respectively. This study indicated the richness of medicinal plant species in Kofale District. Medicinal plants with the highest FL and ROP values and those used to treat disease categories with the highest ICF values should be prioritized in future phytochemical and pharmacological investigations.

Feed preference in lactating dairy cows for different pellet formulations

Two taste preference experiments were conducted with the same 8 multiparous lactating Jersey cattle (100 ± 7.1 DIM, 30.5 ± 4 0.06 kg of milk yield, 18.8 ± 2.52 kg of DMI in experiment 1; and 215 ± 7.1 DIM, 27.6 ± 3.98 kg of milk yield, 19.6 ± 3.03 kg of DMI in experiment 2). In experiment 1, 4 pellets were formulated and manufactured into 4.0-mm pellets. These were as follows: 45.7% alfalfa meal, 45.7% corn grain, and 8.57% wheat middlings (ALFC); 72.3% corn grain, 18.5% wheat middlings, and 9.25% dried distillers grains and solubles (ENG); a pellet containing 100% dehydrated alfalfa meal (DALF); and a pellet containing a mixture of concentrate ingredients (GMIX; 43.1% corn grain, 26.3% dried distillers grains and solubles, 13.8% wheat middlings, 7.10% dry molasses, 3.18% soybean meal, 0.93% corn oil, and 5.6% minor constituents). Cows were offered 0.50 kg of pellets in a randomized arrangement within the feed bunk. Feed preference was ranked from 1 to 4 with 1 being the most preferred and 4 the least. The resulting preference rankings were averaged (± SE) resulting in a highest (closest to 1) to lowest (furthest from 1) ranking as follows of ALFC (1.38 ± 0.164), ENG (2.13 ± 0.327), GMIX (2.88 ± 0.375), and DALF (3.13 ± 0.350). The probabilities of first choice were 70.6 ± 0.55% ALFC, 16.5 ± 0.46% ENG, 5.50 ± 0.475% DALF, and 7.48 ± 0.455% GMIX. A Z-test was conducted to determine the percentage a treatment would be chosen first differed from the value of no preference at 25%; ALFC and DALF differed from the mean value, whereas no difference was observed for ENG and GMIX. The most preferred pellet (ALFC) was used in a second study and compared against 3 other treatments in which different flavoring agents were added. In this study, 4 pellets were manufactured with ALFC: 45.7% alfalfa meal, 45.7% corn grain, 6.76% wheat middlings, and 1.81% oregano leaf (ALFCO); 45.7% alfalfa meal, 45.7% corn grain, 8.22% wheat middlings, 0.10% melon flavoring, and 0.25% BitterOff (ALFCM); and 45.7% alfalfa meal, 45.7% corn grain, 8.47% wheat middlings, and 0.10% licorice flavoring (ALFCL). The resulting preference rankings were averaged resulting in a highest to lowest ranking as follows: ALFC (1.25 ± 0.164), ALFCO (2.38 ± 0.263), ALFCM (2.63 ± 0.375), and ALFCL (3.25 ± 0.164). The probabilities of first choice were 81.9 ± 0.65% ALFC, 8.49 ± 0.46% ALFCO, 6.50 ± 0.481% ALFCM, and 3.12 ± 0.491% ALFCL. Of the pellet choices, ALFC and ALFCL differed from the mean value of no choice, whereas no difference was observed for ALFCO and ALFCM. Mixtures of corn grain and dehydrated alfalfa meal bound by wheat middlings may serve as a feeding strategy that is preferred by the animals and may be an effective reward to cows entering an automated milk system, and we were unable to improve preference by adding flavoring agents.