complexity of decision-making makes multi-criteria analysis an invaluable tool in the engineering design and selection process. Thus, the main objective of this paper is set to reveal the computational easiness of the six preference ranking methods in dealing with FMS selection problems, involving both ordinal and cardinal attribute data. It mainly focuses on introducing these multi-beneficial MCDM methods that can make FMS selection easier and compatible with most of the situations. These preference ranking methods are applied to an existing problem, dealing with the selection of the best FMS alternative for a given manufacturing environment. In these methods, the decision makers’ preferences and preferences on alternatives’ performances are aggregated together to reach the final evaluation and selection decision. The past researchers have adopted different mathematical tools for evaluating, justifying and selecting FMS technologies, but all those methods are either very complicated or require lengthy computations. For decision-making problems with large number of attributes and small number of alternatives, those approaches may occasionally provide poor results. This paper takes the opportunity to explore the application viability and potentiality of six popular preference ranking methods to provide more precise and accurate ranking of the feasible FMS alternatives. According to the best knowledge of the authors, there have been very few applications of these preference ranking methods in manufacturing environment. Few successful implementations of these methods can be found in construction engineering, financial analysis and waste water management. Even till date, very less effort has been devoted to study the relative performance of these methods as employed in discrete manufacturing environment. Furthermore, no attempt has been made to map/match any FMS selection problem to these methods. All these methods are successfully applied and the results are compared for better visualization. Four performance analysis tests are also executed to assess the degree of agreement between the ranking orders as obtained by these methods, while keeping the performance measures in the evaluation matrix of the considered example constant. These six preference ranking methods are also qualitatively compared in terms of their suitability for solving different FMS selection problems, operational similarities and other model characteristics, like information type and criteria requirement, methodological aspect, operational approach, compensatory character and nature of the obtained results.
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Assigning meaningful scores for answer typing is a difficult task. For example, given the question “What city hosted the 1988 Winter Olympics?” and the candidates New York, Calgary, and the word blue, how can we identify New York and Calgary as appropriate and the word blue as inap- propriate? Scoring answer candidates is compli- cated by the fact that a gold standard for appropri- ateness scores does not exist. Therefore, we have no a priori notion that New York is better than the word blue by some amount v. Because of this, we approach the problem of answer typing as one of preference ranking in which the relative appropri- ateness is more important than the absolute scores. Preference ranking stands in contrast to classifi- cation, in which a candidate is classified as appro- priate or inappropriate depending on the values in its feature representation. Unfortunately, simple classification does not work well in the face of a large imbalance in positive and negative examples. In answer typing we typically have far more inap- propriate candidates than appropriate candidates, and this is especially true for the experiments de- scribed in Section 4. This is indeed a problem for our system, as neither re-weighting nor attempt- ing to balance the set of examples with the use of random negative examples were shown to give better performance on development data. This is not to say that some means of balancing the data would not provide comparable or superior perfor- mance, but rather that such a weighting or sam- pling scheme is not obvious.
Previous literatures have well studied on various smooth models for SP. However, they vary great- ly on the measure of preferences. It is still not clear how to do this best. Lapata et al. investigate the correlations between the co-occurrence counts (CT) c(q, a), or smoothed counts with the human plausibility judgements (Lapata et al., 1999; Lap- ata et al., 2001). Some introduce conditional prob- ability (CP) p(a | q) for the decision of preference judgements (Chambers and Jurafsky, 2010; Erk et al., 2010; S´eaghdha, 2010). Meanwhile, the point- wise mutual information (MI) is also employed by many researchers to filter out incorrect infer- ences (Pantel et al., 2007; Bergsma et al., 2008).
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Recent work has shown that Tree Ker- nels (TKs) and Convolutional Neural Net- works (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vec- tor Machines (SVMs) is promising as it can exploit both the syntactic patterns cap- tured by TKs and the embeddings learned by CNNs. However, the embeddings are constructed according to a classification function, which is not directly exploitable in the preference ranking algorithm of SVMs. In this work, we propose a new hy- brid approach combining preference rank- ing applied to TKs and pointwise rank- ing applied to CNNs. We show that our approach produces better results on two well-known and rather different datasets: WikiQA for answer sentence selection and SemEval cQA for comment selection in Community Question Answering.
I now examine the inviolability principle, which holds that a profane outcome cannot be compared with, nor chosen over, a sacred outcome. According to RCT, any choice features multiple options one must evaluate by weighing beneficial attributes against negative ones, enabling the comparison of outcomes and, eventually, an act. In contrast, then, the presence of inviolability implies that there is no preference ranking (better than, equal to, or worse than (Chang 1997)) between sacred and profane outcomes because comparing them is improper. Absent standard preferential relationships, people display unconditional commitment to objects, places, and ideas sacralized by the community. This treatment of privileged outcomes bears considerable resemblance to Joseph Raz’s “constitutive incomparability,” a relation that obtains when “the refusal to trade one option for the other is a condition of the agent's ability to successfully pursue one of his goals” (1986 p. 346). For example, only those who believe “that friendship is neither better nor worse than money, but is simply not comparable to money or other commodities, are capable of having friends” (1986 p. 352). Certain goals and roles, like prison reform or being a priest, require treating some outcomes as inviolable, blocking the sacred outcome from entering into any of the three traditional value relations with a non-sacred outcome.
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Glinus lotoides was the most widely known not only by the local practioniers but also among the large local community of the study area in the treatment of tape- worm infection, followed by Echinops kebericho and Al- lium sativum. In contrast,  have recently reported that among 9 medicinal plants Croton macrostachys was the most popular in the treatment of the same (tapeworm) in- fection followed by Cucumis sp. Various authors have also reported the existence of popular medicinal plants in dif- ferent regions of the country such as Cucumis pastulatus for Tuberculosis, Ocimum urtitolum, Rumex abyssinicus, Solanum incanum, Vernonia amygdalina to treat ‘’michi”, gonorrhea, toothache and urine retention, respectively [27-29]. In other study reports conducted by  and  using paired comparison, preference ranking and direct matrix Allium sativum was found to be the most preferred in the treatment of malaria in the northern part of the country. Of the 9 medicinal plants ranked in the study conducted by  in Nigeria Azadirachta indica was re- ported as a prime candidate for investigation, as it re- corded the highest rank by the informants. Over 1,200 plants belonging to 160 families were reported  to be used traditionally for the treatment of malaria. Since then the number of species has increased substantially due to the increasing worldwide interest in anti-malarial plants.
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DOI: 10.4236/jwarp.2018.106034 613 Journal of Water Resource and Protection diment supply (S1) solution as paramount. Similarly, combination of each crite- ria-weighting techniques with preference ranking method ELECTRE provides different preference ranking. A combination of criteria weighting method En- tropy and AHP with preference ranking method ELECTRE prioritizes two al- ternate solutions Koshi high dam (S2) and controlled flooding with deposition areas (S7) simultaneously as the highest ranks. Two alternate solutions narrow- ing river by recurrent measures (S4) and dredging (S5) are simultaneously pri- oritized the top ranks by combination of criteria weighting method Entropy with preference ranking method ELECTRE. Besides, combination of crite- ria-weighting technique AHP with preference ranking method ELECTRE ranks controlled flooding and sedimentation using old course (S6) in the first position over others. The results show deviations on preference ranking with different methods. This may be due to variations in criteria weighting index. Considering average value of results for all nine set of combinations of criteria weighting methods and preference ranking methods, an alternate solution prescribing re- duction on upstream sediment supply (S1) is recommended as top prioritized and removing embankments and Koshi barrage (S8) as the least prioritized measures for sediment control. The recommended top prioritized measure comprises bottom or bank protection; check dams and reforestation to decrease the supply of sediment at its origin. The processes which are responsible for the high sediment load of the river i.e. landslides, bank and bottom erosion and GLOFs, have to be reduced. However, its tedious job and takes long time to real- ize the results.
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Ken Arrow (1998) asks, “What has economics to say about racial discrimination?” He replies – entirely correctly – that racial “segregation within an industry – that is, firms with either all black or all white labor forces ” – may be explained by economic theory, but “the hypothesis of employer discrimination does not at all explain segregation by occupation, [and] discriminatory tastes of other employees … may explain segregation [by firms] within industries but not segregation by occupation[s]” (p. 95), that are filled by racially distinct persons within firms. Becker (1957) and Akerlof and Kranton (2000 and 2010) offer economic theories that deal with social identity differentiation, but these lack rational choice theory foundations, insofar as they impose a utility indicator function as a primitive concept via persuasion, rather than such a function being entailed by derivation from a preference ranking relation defined on a set of outcomes, with restrictions imposed both on the set and the relation. This is a methodological weakness of their work relative to that of Arrow and Debreu (1954).
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Ranking and comparing items is crucial for collecting information about preferences in many areas, from marketing to politics. The Mallows rank model is among the most successful approaches to analyze rank data, but its computational complexity has limited its use to a particular form based on Kendall distance. We develop new computationally tractable methods for Bayesian inference in Mallows models that work with any right-invariant dis- tance. Our method performs inference on the consensus ranking of the items, also when based on partial rankings, such as top-k items or pairwise comparisons. We prove that items that none of the assessors has ranked do not influence the maximum a posteriori con- sensus ranking, and can therefore be ignored. When assessors are many or heterogeneous, we propose a mixture model for clustering them in homogeneous subgroups, with cluster- specific consensus rankings. We develop approximate stochastic algorithms that allow a fully probabilistic analysis, leading to coherent quantifications of uncertainties. We make probabilistic predictions on the class membership of assessors based on their ranking of just some items, and predict missing individual preferences, as needed in recommendation systems. We test our approach using several experimental and benchmark data sets.
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MADM determines the best alternative from a set of alternatives (selection problems) using the alternative reference as attributes in its selection. Because the decision-making defense system model emerges a potential conflict and there is no attribute that dominates others, it can be inferred as the preliminary hypothesis that MADM method can be used in decision-making for sea defense areas ;  and . For example, the division by zones is beneficial from the perspective of sea-defense operation. However, it will require a lot of funds to support needed facilities in each sector. In addition, MADM Decision allows more than one character who has different preference on the existed alternative in their decision-making sea defense model.
The opinion of both urban area and rural area customers of the bank in the table depicted that the reason to prefer e-shopping was the same. Unanimously, customers of both urban area and rural area had given similar ranking for the reason behind the preference of e-shopping as the presence of e- banking services like internet banking, mobile banking or credit cards. This shows the trust over such services provided by their banks.
Spatial database systems manage large collections of geographic entities, which apart from spatial attributes contain non spatial information. Spatial objects in reality are associated with multiple quality attributes in addition to their spatial locations. Traditional spatial queries and joins focus on manipulating only spatial locations and distances, but they ignore the importance of quality attributes. The dominance comparison is suitable for comparing two objects with respect to multiple quality attributes. For the sake of simplicity, we assume that the domain of each quality attribute is fully ordered (e.g., integer domain). An object A is said to dominate another object B, if A is no worse than B for all quality attributes and A is better than B for at least one quality attribute. In this system, we study an interesting type of spatial queries, which select the best spatial location with respect to the quality of facilities in its spatial neighbourhood. Given a set D of interesting objects (e.g., candidate locations) and quality vector, a top-k s p a t i a l preference queries retrieves the k objects in D with the highest scores. The score of an object is defined by the quality of features (e.g., facilities or services) in its spatial neighbourhood. As a motivating example, consider a database containing all information of dams. Here “feature” refers to specific facilities or services. A customer may want to rank the contents of this database with respect to the quality of their locations, quantified by aggregating non spatial characteristics of other features (e.g., height of dam, reservoir capacity etc.,) .
Ranking fraud in the mobile App market insinuates fraudulent or deceptive activities which have a purpose behind thumping up the Apps in the popularity list. Certainly, it ends up being progressively visit for App architects to use darken implies, for instance, growing their Apps' arrangements or posting phony App assessments, to submit ranking fraud. While the hugeness of thwarting ranking fraud has been comprehensively seen, there is obliged appreciation and research around there. To this end, in this paper, we give a widely inclusive viewpoint of ranking fraud and propose a ranking fraud detection structure for mobile Apps. Specifically, we at first propose to accurately locate the ranking fraud by mining the dynamic time periods, particularly leading sessions, of mobile Apps. Such leading sessions can be used for recognizing the close-by peculiarity instead of overall inconsistency of App rankings. Furthermore, we investigate three sorts of affirmations, i.e., ranking based affirmations, rating based affirmations and review based affirmations, by showing Apps' ranking, rating and overview rehearses through statistical hypotheses tests. Besides, we propose a progression based accumulation procedure to organize all of the affirmations for fraud detection. Finally, we evaluate the proposed system with genuine App data accumulated from
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Prior to the elicitation of health state values using the TTO method, respondents were asked to rank a set of health states from best to worst. The ranking set contained 11 health states in total, the 9 health states which were subsequently valued using the TTO (including the PITS state), plus the best SQOL health state containing the most desirable levels on all dimensions and immediate death. The second stage of the interview involved obtaining TTO valuations of the health states defined by the classification. The main valuation survey was undertaken using the Measurement and Valuation of Health (MVH) group version of TTO with a visual prop (MVH Group, 1995).
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Increasing transport problems caused by high dependency on road transport, has brought us to investigate various policy scenarios to promote a greater use of rail and coastal transport for freight transport in New Zealand. For this, we examined how factors associated with freight transport (e.g. cost and reliability) inﬂuenced the transport decisions of shippers with various operation types. Online stated preference surveys were developed and mixed-logit models were estimated from the data provided by 233 shippers. These models were used to calculate the base mode shares, and subsequently, to test various hypothetical policy options for promoting greater use of rail and coastal transport. The results show that a substantial improvement in reliability of both the rail and coastal freight transport services will lead to a substantial decline in the share of road transport, especially for shippers with short-haul and long- haul operations, transporting either large or small shipment volume.
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Health state values are usually obtained using cardinal methods such as standard gamble (SG), time trade-off (TTO) or visual analogue scaling (VAS). However, there are a number of concerns with these techniques (Brazier et al, 1999). The direct and choice-less nature of the VAS task has been criticised (Bleichrodt and Johannesson 1997) and VAS data may be subject to end point and context bias (Torrance et al, 2001). Although SG and TTO are often identified as preferred over VAS due to their choice based theoretical underpinnings (Brazier et al, 2006), the values produced by these methods are influenced by factors beyond the respondents preference for the health state including time preference, risk attitude and loss aversion (Bleichrodt, 2002) . For these reasons, there is increasing interest in using ordinal methods to estimate cardinal values for health states to calculate quality adjusted life years
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Now-a-days, the importance of understanding farmers' technology preference becomes an important issue by the research and extension system. Many technologies disseminated to farmers are simply rejected by farmers due to inconsistence with preference criteria between technology generators and end users. The study results provided in Table 5 below clearly indicate that the farmers consider 19 attributes in deciding their preference criteria (evaluation) for adoption of upland rice variety (NERICA- 4).
In recent years, personalized search has attracted interest in the research community as a means to decrease search ambiguity and return results that are more likely to be interesting to a particular user and thus providing more effective and efficient information access. In this dissertation, we have proposed web user personalization and search with content, location and time preference of a user which helps user to get highly relevant information according his/her current interest.
There are also gray-area goods and services—for example, art and music. In the industries of art and music, unlike in professional sports, there is no explicit game at which participants regularly compete. Of course, there are many competitions (particularly for young artists), but these represent only a small part of the art and music industries as a whole. Witnessing or knowing who “wins” a contest is not the main goal of consumers who buy art and music products. Practical value is undeniably important when a consumer buys such products: people purchase art and music products because they enjoy them. Nevertheless, ranking value is also important in these fields. There are few explicit and reliable rankings for many art and music products except, for example, hit charts in the music industry. Even so, people will still generally feel a sense of ranking, possibly even unconsciously, because they usually try to determine which products people are paying attention to, and they want to buy the products that are the most popular and well known. Fame is valuable because it provides information about implicit rankings and generates a sense of ranking. Even if a consumer makes the evaluation that the practical values and prices are the same between two paintings, he or she will generally buy the painting done by the more famous artist because the utility derived from the ranking value is greater. Therefore, although ranking value may be less important in the art and music industries than in professional sports, it is still relatively more important than it is in many other industries. Therefore, some artists may have great monopolistic powers: that is, superstars can be also generated in the art and music industries.
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Furthermore, the ‘entropy’ concept used by the authors for deciding the relative importance of attributes does not give scope to designer’s preferences and it involves more computation. Rao  presented a material selection model using graph theory and matrix approach. However, the method does not have a provision for checking the consistency made in the judgments of relative importance of the attributes. Manshadi et al.  proposed a numerical method for materials selection combining non-linear normalization with a modified digital logic method. However, the method does not make a provision for considering the qualitative material selection attributes. Chan and Tong  proposed weighted average method using grey relational analysis to rank the materials with respect to certain quantitative attributes. Rao  proposed a compromise ranking method known as VIKOR and Chatterjee et al.  proposed VIKOR and ELECTRE methods for material selection.
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