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YILMAZ SOYDAN, NACİYE TUBA

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YILMAZ SOYDAN

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NACİYE TUBA

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End date: 2023 × Subject: Econometrics ×

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    Simulation-based optimisation approach to multi-choice transportation problem
    (2022-01-01) YILMAZ SOYDAN, NACİYE TUBA; ÇİLİNGİRTÜRK, AHMET METE; YILMAZ SOYDAN N. T. , ÇİLİNGİRTÜRK A. M.
    Copyright © 2022 Inderscience Enterprises Ltd.The classical transportation problem minimises the total costs of transportation of a unique product from various supply points (or warehouses) to demand points. The problem assumes that freight costs from source to destination are constant and that the supply and demand quantities are equal and strictly known, so the market for the product is well-balanced. It thus involves a special type of linear integer programming, which becomes stochastic since the constraints or parameters are random variables from a known or unknown distribution. Several studies have formulated well-known deterministic models under probabilistic restrictions. The transformed models mostly keep the confidence level at a given minimum constant or else minimise the error level. Also, there is a multi-choice stochastic transportation problem, which introduces several unit costs. In this study, we try to simulate Roy\"s (2014) multi-choice stochastic transport model with random supply and demand quantities from a given Weibull distribution and compare the results of distribution and total costs. As a result of the simulation, total cost value was estimated lower than the result of the problem.
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    Bibliometric analysis of the published literature on machine learning in economics and econometrics
    (2022-12-01) ÇAĞLAYAN AKAY, EBRU; YILMAZ SOYDAN, NACİYE TUBA; ÇAĞLAYAN AKAY E., YILMAZ SOYDAN N. T., KOCARIK GACAR B.
    An extensive literature providing information on published materials in machine learning exists. However, machine learning is still a rather new concept in the fields of economics and econometrics. This study aims to identify different properties of published documents about machine learning in economics and econometrics and therefore to draw a detailed picture of recent publications from bibliometric analysis perspectives. For the aim of the study, the data are collected from the publications indexed by Web of Science and Scopus databases from the period 1991 to 2020. Inthe study, the data have been illustrated by VOSviewer for science mapping. The analysis of variance has also been used to identify the links between the number of citations of articles and years. The findings obtained provides information about the studies on machine learning in the relevant field conducted in the past, as well as providing an opportunity to gain knowledge about the researched area by shedding light on what the future research areas would be. There is no doubt that it attracts attention has increased significantly on machine learning in the field of economics and econometrics and academic publications on machine learning in the relevant field have increased over the last decade.