Publications by authors named "Ehsan Ardjmand"
In the aftermath of the COVID-19 pandemic, supply chains experienced an unprecedented challenge to fulfill consumers' demand. As a vital operational component, manual order picking operations are highly prone to infection spread among the workers, and thus, susceptible to interruption. This study revisits the well-known order batching problem by considering a new overlap objective that measures the time pickers work in close vicinity of each other and acts as a proxy of infection spread risk.
View Article and Find Full Text PDFWe discuss the design of interlayer edges in a multiplex network, under a limited budget, with the goal of improving its overall performance. We analyze the following three problems separately; first, we maximize the smallest nonzero eigenvalue, also known as the algebraic connectivity; second, we minimize the largest eigenvalue, also known as the spectral radius; and finally, we minimize the spectral width. Maximizing the algebraic connectivity requires identical weights on the interlayer edges for budgets less than a threshold value.
View Article and Find Full Text PDFArticle Synopsis
- Demand forecasting is essential for effective resource management, especially for water due to its scarcity, and involves using various soft computing techniques.
- This study focuses on methods like artificial neural networks (ANNs), fuzzy logic, and support vector machines for water demand forecasting published between 2005 and 2015, highlighting that ANNs often perform best in short-term scenarios.
- While multiple methods exist, there's potential for further advancements in water demand forecasting through more diverse soft computing approaches, including deep learning and ensemble methods.