Browsing by Author "Li, Guo"

Filter results by typing the first few letters
Now showing 1 - 4 of 4
  • Thumbnail Image
    Editorial
    Editorial for the special issue on circular economy and sustainable business performance management in the era of digitalization
    (EMERALD GROUP PUBLISHING LTD, 2022) Yigit Kazancoglu; Sachin Kumar Mangla; Malin Song; Guo Li; Flavio Hourneaux Junior; Song, Malin; Li, Guo; Kumar Mangla, Sachin; Hourneaux Junior, Flavio; Kazancoglu, Yigit
  • Thumbnail Image
    Editorial
    Citation - WoS: 1
    Citation - Scopus: 1
    Editorial note for special issue on “Carbon neutrality through Industry 4.0 based smart manufacturing”
    (Elsevier Ltd, 2025) Guo Li; Sachin Kumar Kumar Mangla; Malin Song; Yigit Kazancoglu; Ray Runyang Zhong; Song, Malin; Zhong, Ray Y.; Li, Guo; Mangla, Sachin Kumar; Kazancoglu, Yigit
    [No abstract available]
  • Thumbnail Image
    Article
    Citation - WoS: 56
    Citation - Scopus: 66
    Lateral inventory share-based models for IoT-enabled E-commerce sustainable food supply networks
    (Elsevier Ltd, 2021) Banu Yetkin Yetkin Ekren; Sachin Kumar Kumar Mangla; Ecem Eroğlu Turhanlar; Yigit Kazancoglu; Guo Li; Turhanlar, Ecem Eroglu; Li, Guo; Mangla, Sachin Kumar; Kazancoglu, Yigit; Ekren, Banu Yetkin
    This research investigates lateral inventory share-based business models for e-grocery networks where online groceries are inter-connected in an Internet of Things (IoT) environment. Recently managing food supplies has become a very important issue due to the onset of unexpected conditions such as natural disasters (earthquakes tsunamis floods droughts etc.) and pandemics. In this paper we aim to design sustainable food supply chain networks for e-commerce food companies (e.g. e-groceries) by applying lateral inventory share policies after the consideration of the existence of strategic alliances between organizations. We aim to minimize food waste as well as back orders resulting in more sustainable networks. Further we explore how a business-to-business (B2B) policy (i.e. lateral-inventory share policy) should be designed to optimize business (i.e. e-groceries) profitability. We optimize the re-order and up-to (sS) inventory levels of e-groceries for the pre-defined sharing policies by using a simulation optimization approach. The optimal results show that having a lateral inventory share poliIndiacy in food networks is more efficient compared to a non-lateral policy. Also at the optimal points of the considered policies lateral inventory share ratio is usually observed to be larger than 50% on average meaning that more than half of customer orders are met by lateral inventory share. © 2021 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Article
    Citation - WoS: 11
    Citation - Scopus: 11
    Leveraging explainable artificial intelligence in understanding public transportation usage rates for sustainable development
    (PERGAMON-ELSEVIER SCIENCE LTD, 2024) Gorkem Sariyer; Sachin Kumar Mangla; Mert Erkan Sozen; Guo Li; Yigit Kazancoglu; Sariyer, Gorkem; Sozen, Mert Erkan; Li, Guo; Mangla, Sachin Kumar; Kazancoglu, Yigit
    Public transportation usage prediction is valuable for the sustainable development of transportation systems particularly in crowded megacities. Machine learning technologies are of great interest for predicting public transportation usage. While these technologies outperform many other techniques they suffer from limited interpretability. Explainable artificial intelligence (XAI) tools and techniques that offer post -hoc explanations of the obtained predictions are gaining popularity. This paper proposes an advanced tree-based ensemble algorithm for public transportation usage rate prediction. We aim to explain the predictions both with the most widely used technique of XAI Shapley additive explanation (SHAP) and in the light of the rules presented. To predict the total public transportation usage the proposed model combines all types of public transportation categorized as ferry railway and bus unlike most existing studies focusing on a single kind of public transport. Besides the sort of transportation the day of the week whether the day is special and the daily ratio of passenger types were identified as model features for predicting the daily usage of each type of public transportation. We tested the proposed model using an open data set from Izmir City Turkey. While the model had superior prediction performance the explanations showed that the type of public transportation weekday and the ratio of full-fare passengers have the highest SHAP values and the model features have many interactions. We also validated our results using an online data set showing Google search trends.