Duygu analizi ve yapay sinir ağı kullanılarak envanter rotalama problemi için talep tahmini

Year 2021, Volume: 11 Issue: 1, 126 - 134, 15.01.2021

Aslı Boru İpek Ayse Tugba Dosdogru Rızvan Erol

https://doi.org/10.17714/gumusfenbil.691428

Abstract

Sosyal medya kullanıcılarının sayısı her geçen gün katlanarak artmaktadır. Bu gelişme, tedarik zincirinde iş zekasının ilerletilmesinde önemli fırsatlar sunduğu için araştırmacıları ve yöneticileri sosyal medya ve müşteri duygularını analiz etmeye teşvik etmektedir. Ancak, tedarik zinciri üyeleri günümüz iş dünyasında genel duyguları anlamakta zorlanmaktadır. Bu nedenle, tedarik zincirine değerli bilgiler sağlamak için çeşitli yöntemler kullanılmaktadır. Bu çalışmada, SentiStrength tek bir ürün ile ilgili müşteri yorumlarını analiz etmek için kullanılmaktadır. Daha sonra, SentiStrength'in çıktısı ve ürün talepleri, müşteri taleplerini tahmin etmek için Yapay Sinir Ağına beslenmiştir. Ardından, tahmin edilen müşteri talepleri kullanılarak envanter rotalama problemini çözmek için Baskılanamayan Sıralamalı Genetik Algoritma II (NSGA-II) tabanlı simülasyon optimizasyonu kullanılmıştır. Çalışmanın sonuçları, duygu analizi içeren melez metodolojinin kullanımının envanter rotalama problemini başarılı bir şekilde analiz edebileceğini göstermiştir.

Keywords

Duygu analizi, Envanter rotalama problemi, Simülasyon optimizasyonu

Thanks

Aslı Boru Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) 2211-C Yurt İçi Öncelikli Alanlar Doktora Burs Programına teşekkür etmektedir.

Demand forecasting for inventory routing problem using artificial neural network and sentiment analysis

Abstract

Social media users have been growing exponentially in recent years. This growth has evoked researchers and manager to analyze the social media and customer sentiment because it offers significant opportunities to advance business intelligence in supply chain. However, supply chain members are struggling in understanding the general sentiments in today’s business world. Therefore, various methods are used to provide valuable insights in supply chain. In this paper, SentiStrength is used to analyze customer reviews related to one type of product. The output of SentiStrength and demands of the product are then fed into Artificial Neural Network to forecast the customer demands. After, nondominated sorting genetic algorithm II (NSGA-II) based simulation optimization is employed to solve the inventory routing problem using forecasted customer demands. The results of the study demonstrated that the use of hybrid methodology containing sentiment analysis can successfully analyze the inventory routing problem.

Keywords

Sentiment analysis, Inventory routing problem, Simulation optimization

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