Sequencing grey games

Ergün, Serap; Palanci, Osman; Gök, Sırma Zeynep Alparslan; Nizamoğlu, Şule; Weber, Gerhard

doi:10.3934/jdg.2020002

Cited by 4 publications

(4 citation statements)

References 18 publications

(53 reference statements)

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“…Application of uncertainty and forecasting techniques such as robust optimization (Golpîra and Tirkolaee, 2019; Kara et al, 2019; Khalilpourazari et al, 2020a; Lotfi et al, 2020; Özmen et al, 2017), fuzzy programming (Goli et al, 2021; Maity et al, 2019; Roy et al, 2019; Tirkolaee et al, 2021), stochastic optimal control (Kalaycı et al, 2020; Kropat et al, 2020; Savku and Weber, 2018), time series (Weber et al, 2011), regression models (Kuter et al, 2018), and grey systems (Ergün et al, 2020) to address the uncertain nature of the problem.…”

Section: Discussionmentioning

confidence: 99%

A sustainable medical waste collection and transportation model for pandemics

Tirkolaee

Aydın

2021

Waste Manag Res

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We are currently experiencing a critical period for the prevention and control of the COVID-19 pandemic. COVID-19 related waste is a threat to global public environmental health. Medical waste management during this pandemic is one of the major issues facing public service organizations such as municipalities, which is of great importance in terms of logistics, environment and social aspects. The discussion of logistics operations is related to the collection, transportation and disposal of waste, which imposes high expenses. Many methods have been applied to develop and improve waste management policies in the literature. Apart from these studies, very few researchers have improved vehicle operations in waste management considering environmental aspects and the possibility of outsourcing. In this paper, by examining the gaps in the field, we try to explain and formulate the sustainable medical waste management problem for pandemics. Finally, by designing several practical examples with different scales, we solve the problem using CPLEX solver, compare different conditions and discuss the practical implications using the sensitivity analysis of demand parameter.

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Section: Discussionmentioning

confidence: 99%

A sustainable medical waste collection and transportation model for pandemics

Tirkolaee

Aydın

2021

Waste Manag Res

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“…* https://www.microsoft.com/en-us/research/project/letor-learning-rank-information-retrieval/ Basically, MARS model formula is an approximation of recursive partitioning regression in the shape of augmented set of basis functions (BFs) as given in Equation (1).…”

Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

“…In IR, relevance denotes how well a retrieved document or a set of documents meets the information need of the user. Moreover, ranking, as a significant task for many applications in IR as well as other domains such as scheduling, 1 scores retrieved documents in terms of relevance order. There are two major approaches to challenge the ranking problem: traditional approach and learning to rank.…”

Section: Introductionmentioning

confidence: 99%

Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Altinok

Karagöz

Batmaz

2020

Computational Intelligence

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Learning to rank is a supervised learning problem that aims to construct a ranking model for the given data. The most common application of learning to rank is to rank a set of documents against a query. In this work, we focus on point-wise learning to rank, where the model learns the ranking values. Multivariate adaptive regression splines (MARS) and conic multivariate adaptive regression splines (CMARS) are supervised learning techniques that have been proven to provide successful results on various prediction problems. In this article, we investigate the effectiveness of MARS and CMARS for point-wise learning to rank problem. The prediction performance is analyzed in comparison to three well-known supervised learning methods, artificial neural network (ANN), support vector machine, and random forest for two datasets under a variety of metrics including accuracy, stability, and robustness. The experimental results show that MARS and ANN are effective methods for learning to rank problem and provide promising results.

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“…Li [22], in his work, solved matrix games with different uncertainties using Atanassov's intuitionistic fuzzy environment. Several monographs and articles have been published in game theory [1,3,4,5,11,13,15,17,18,19,21,33,34,35,36,37,38,39,50] having many real-life impacts. Linguistic term set in neutrosophic environment through game theory can be viewed as a new concept.…”

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confidence: 99%

Multi-objective linguistic-neutrosophic matrix game and its applications to tourism management

Bhaumik¹,

Roy²,

Weber³

2021

JDG

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Game theory plays an important role in numerous decision-oriented real-life problems. Nowadays, many such problems are basically characterized by various uncertainties. Uncertainties come to happen due to decision makers' collection of data, intuition, assumption, judgement, behaviour, evaluation and lastly, due to the problem itself. Fuzzy concept with membership degree made an initialization towards the treatment of uncertainty, but it was not enough. Intuitionistic fuzzy concept was evolved concerning with both membership and non-membership degrees but failed to express reality more accurately. Then, neutrosophy logic was developed with a new degree in uncertainty, say, indeterminacy degree besides membership and non-membership degrees. Multi-objective optimization is an area of multiple-criteria decision making related with mathematical optimization problems involving more than one objective function to be optimized at the same time. Game theory (matrix game) problems with imprecise, vague information, like neutrosophic, can be formed with multiple objective functions. We develop and analyse a matrix game with multiple objectives, and solve the problem under a single-valued neutrosophic environment in linguistic approach. The main achievement of our study is that we here introduce a problem-oriented example to justify our designed methodologies with a successful real-life implications using linguistic neutrosophic data rather than crisp data as used in previous researches.

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Sequencing grey games

Cited by 4 publications

References 18 publications

A sustainable medical waste collection and transportation model for pandemics

A sustainable medical waste collection and transportation model for pandemics

Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Multi-objective linguistic-neutrosophic matrix game and its applications to tourism management

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