Optimization in Item Delivery as Risk Management: Multinomial Case Using the New Method of Statistical Inference for Online Decision

Risks

Mokhammad Ridwan Yudhanegara¹  Kurnia Novita Sari²  Sapto Wahyu Indratno³ 

[1] Mathematics Education Department, Faculty of Teacher Training and Education, Universitas Singaperbangsa Karawang, Karawang 41361, West Java, Indonesia;Statistics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, West Java, Indonesia;University Center of Excellence on Artificial Intelligence for Vision, Institut Teknologi Bandung, Natural Language Processing & Big Data Analytics (U-CoE AI-VLB), Bandung 40132, West Java, Indonesia;

关键词: statistics test;    optimization in risk management;    online decision;    dynamic network;   

DOI  :  10.3390/risks10060122

来源: DOAJ

【 摘 要 】

Online activity increasing spreads with the power of technological development. Many studies reported the impact of online activities on decision making. From the statistical perspective, decision making is related to statistical inference. In this regard, it is interesting to propose a new method of statistical inference for online decisions. This method is built by the logarithm distribution of the likelihood function, which allows us to determine statistics using the normal statistical test approach iteratively. It means that the inference can be made in an online way every time new data arrive. Compared to classical methods (commonly, chi-squared), the advantage of this method is that it allows us to make decisions without storing large data. In particular, the novelty of this research is expressed in the algorithm, theorem, and corollary for the statistical inference procedure. In detail, this paper’s simulation discusses online statistical tests for multinomial cases and applies them to transportation data for item delivery, namely traffic density. Changes in traffic density resulted in changes to the strategy of item delivery. The goal is to obtain a minimum delivery time for the risk of losses.

【 授权许可】

Unknown