Elham Ahmadi started her journey as Research Associate and PhD candidate at the Kühne Logistics University under the Supervision of Prof. Dr. André Ludwig in May 2022. Her research focuses on the application of Data Science and Machine Learning in Logistics and Supply Chain Management. With her research Elham contributes to the "CargoSurfer" project funded by the Federal Ministry of Transport and Digital Infrastructure, whose main goal is to make a digital platform based on the prediction by Machine Learning for a multi modal logistics system.
Prior to KLU, she received her second Master of Science degree in "Supply Chain Engineering and Management" from Jacobs University Bremen in June 2019. For her master's thesis, she applied her skills and knowledge in data analytics for Twitter sentiment analysis to improve a vehicle routing problem with drones. Elham received another Master of Science degree in Industrial Engineering from Yazd University of Iran in 2013. Her Bachelor of Science degree is also in industrial engineering from the same university. In pursuing her bachelor’s degree, she was a member of the Brilliant Talents Association, for this reason she was invited to study for an entrance exam-free Master's degree. During her studies, she was able to develop her skills in the application of machine learning algorithms in her field of study, which developed three models based on artificial intelligence to predict the stock market for her master's thesis. In addition to her studies, Elham has five years of experience as an Industrial Engineering Manager in the steel pipe industry in Iran.
Contact
Networks
Education
| Since 2022 | PhD candidate at Kühne Logistics University, Hamburg, Germany |
| 2019 - 2021 | Master of Science in Supply Chain Engineering and Management, Jacobs University Bremen, Bremen, Germany |
| 2011 - 2013 | Master of Science in Industrial Engineering, Yazd University, Yazd, Iran |
| 2007 - 2011 | Bachelor of Science in Industrial Engineering, Yazd University, Yazd, Iran |
| 2004 - 2006 | Diploma in Physics and Mathematics, Zandizadeh High School, Isfahan, Iran |
Professional Experience
| 2014 - 2019 | Industrial Engineering Manager (Supply Chain Manager), Iran Spiral Co., Isfahan, Iran |
| 2013 - 2014 | Software Expert (Production and Inventory Management Modules), Rayvarz Software Engineering Co., Isfahan, Iran |
| 2011 | Internship, Isfahan's Mobarakeh Steel Company, Isfahan, Iran |
| 2009 | Internship, Isfahan's Mobarakeh Steel Company, Isfahan, Iran |
Publications
Extreme weather is often perceived as a major threat to the reliability of rail transportation. This study investigates regional rail operations in central Germany and examines whether severe weather conditions causally affect train arrival delays. We combine several years of operational data (2017–2022) with weather observations from the German Weather Service and define four component-specific treatments for adverse conditions: extreme temperature, strong wind, heavy rainfall, and snow presence. To estimate causal effects, we employ a Double Machine Learning framework based on partial linear regression (PLR–DML) and Causal Forest–DML. By reframing the weather–delay relationship as a causal question rather than a purely predictive one, the study provides evidence on whether extreme weather constitutes a materially relevant source of arrival delays. Across all four weather components, estimated average treatment effects are small in magnitude; analyses of individualized and group-level effects reveal no consistent or robust patterns of heterogeneity across lines, seasons, weekdays, or hours of the day. Robustness checks indicate that these operationally negligible estimates are not sensitive to outliers, rare-event imbalance, or to reasonable perturbations consistent with plausible unobserved confounding. Exploratory mediation analyses are consistent with the interpretation that passenger-flow variables do not materially amplify the already negligible estimated effects of severe weather on delays. Overall, the results suggest that, in this regional network, severe weather does not materially increase arrival delays. The findings underscore the importance of rigorous causal diagnostics for distinguishing perceived from materially relevant sources of delay in transportation reliability studies.
Last mile delivery is of important stages through supply chains and logistics, which covers the final stage of delivering products to the end customers. With the growth of e-commerce and the increasing desire of people to shopping online, logistics and consequently last mile delivery became very important. One of the biggest challenges is the issue of sustainability and reducing pollution when delivering products to customers, especially in urban surroundings. For this reason, this study presents a new model that considers the issue of sustainability in optimising the problem of vehicle routing problem with drones. For this purpose, a sentiment analysis is used considering Twitter to determine the alignment of customer sentiments on environmental protection and to calculate the net promoter score index. This index is considered as a coefficient in the penalty function added to the base model. According to the results of this study, this new model will be applied to logistics companies that are responsible for the issue of sustainability and can help customers who are willing to participate for improving the sustainability of the supply chain.
Last mile delivery is of important stages through supply chains and logistics, which covers the final stage of delivering products to the end customers. With the growth of e-commerce and the increasing desire of people to shopping online, logistics and consequently last mile delivery became very important. One of the biggest challenges is the issue of sustainability and reducing pollution when delivering products to customers, especially in urban surroundings. For this reason, this study presents a new model that considers the issue of sustainability in optimising the problem of vehicle routing problem with drones. For this purpose, a sentiment analysis is used considering Twitter to determine the alignment of customer sentiments on environmental protection and to calculate the net promoter score index. This index is considered as a coefficient in the penalty function added to the base model. According to the results of this study, this new model will be applied to logistics companies that are responsible for the issue of sustainability and can help customers who are willing to participate for improving the sustainability of the supply chain.
Nowadays, Information Technology (IT) is changing the way traditional enterprise management
concepts work. One of the most dominant IT achievements is the Blockchain Technology. This
technology enables the distributed collaboration of stakeholders for their interactions while fulfilling
the security and consensus rules among them. This paper has focused on the application of Blockchain
technology to enhance one of traditional inventory management models. The Vendor Managed
Inventory (VMI) has been considered one of the most efficient mechanisms for vendor inventory
planning by the suppliers. While VMI has brought competitive advantages for many industries, however
its centralized mechanism limits the collaboration of a pool of suppliers and vendors simultaneously.
This paper has studied the recent research for VMI application in industries and also has investigated
the applications of Blockchain technology for decentralized collaboration of stakeholders. Focusing on
sustainability issue for total supply chain consisting suppliers and vendors, it has proposed a Blockchain
based VMI conceptual model. The different capabilities of this model for enabling the collaboration of
stakeholders while maintaining the competitive advantages and sustainability issues have been
discussed.
In this paper, two hybrid models are used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine (SVM) and Heuristic Algorithms of Imperialist Competition and Genetic. In the first model, SVM and Imperialist Competition Algorithm (ICA) are developed for stock market timing in which ICA is used to optimize the SVM parameters. In the second model, SVM is used with Genetic Algorithm (GA) where GA is used for feature selection in addition to SVM parameters optimization. Here the two approaches, Raw-based and Signal-based are devised on the basis of the literature to generate the input data of the model. For a comparison, the Hit Rate is considered as the percentage of correct predictions for periods of 1–6 day. The results show that SVM-ICA performance is better than SVM-GA and most importantly the feed-forward static neural network of the literature as the standard one.
In today competitive environment, qualified human resources are considered as one of the major keys to the organizations’ success. So an efficient solution to the problem of personnel selection is more necessary than any time in the past. Besides many of the works in the literature of the field, this paper presents a novel fuzzy ELECTRE approach which is categorized as a multiple criteria decision making (MCDM) technique. In the approach, the weights and ranks are determined by linguistic variables while both quantitative and qualitative criteria are considered simultaneously. At last with a case, the implementation of the model is illustrated and the results are compared with TOPSIS.
In this paper, the nonlinear autoregressive model with exogenous variables as a new neural network is used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick. In this model, the “nonlinear autoregressive model with exogenous variables” is an analyzer. For a more reliable comparison, here (like the literature) two approaches of Raw-based and Signal-based are devised to generate the input data of the model. The correct predictions percentages for periods of 1- 6 days with the total number of buy and sell signals are considered. The result proves that to some extent the approaches have similar performances while apparently, they are superior to a feed-forward static neural network. The created network is evaluated by the measure of Mean of Squared Error and the proposed model accuracy is calculated to be extremely high.
This paper investigates a single-stage and two-stage production systems where specification limits are
designed for inspection. When quality characteristics fall below a lower specification limit (LSL) or
above an upper specification limit (USL), a decision is made to scrap or rework the item. The purpose
is to determine the optimum mean for a process based on rework or scrap costs. In contrast to previous
studies, costs are not assumed to be constant. In addition, this paper provides a Markovian model for
multivariate Normal process. Numerical examples are performed to illustrate the application of the
proposed method.
