[HTML][HTML] Supply chain risk management with machine learning technology: A literature review and future research directions
Abstract Coronavirus disease 2019 (COVID-19) has placed tremendous pressure on supply
chain risk management (SCRM) worldwide. Recent technological advances, especially …
chain risk management (SCRM) worldwide. Recent technological advances, especially …
Deep learning applications in manufacturing operations: a review of trends and ways forward
Purpose Deep learning (DL) technologies assist manufacturers to manage their business
operations. This research aims to present state-of-the-art insights on the trends and ways …
operations. This research aims to present state-of-the-art insights on the trends and ways …
Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions
Policymakers during COVID-19 operate in uncharted territory and must make tough
decisions. Operational Research–the ubiquitous 'science of better'–plays a vital role in …
decisions. Operational Research–the ubiquitous 'science of better'–plays a vital role in …
Predictive analytics for demand forecasting: A deep learning-based decision support system
The demand is often forecasted using econometric (regression) or statistical forecasting
methods. However, most of these methods lack the ability to model both temporal (linear and …
methods. However, most of these methods lack the ability to model both temporal (linear and …
A cross-temporal hierarchical framework and deep learning for supply chain forecasting
Organizations require short-term up to long-run aggregated forecasts for making strategic,
tactical, and operational decisions for their supply chain management. In supply chain …
tactical, and operational decisions for their supply chain management. In supply chain …
[HTML][HTML] Predicting demand for new products in fashion retailing using censored data
In today's highly competitive fashion retail market, it is crucial to have accurate demand
forecasting systems, namely for new products. Many experts have used machine learning …
forecasting systems, namely for new products. Many experts have used machine learning …
Deep learning for information systems research
Modern artificial intelligence (AI) is heavily reliant on deep learning (DL), an emerging class
of algorithms that can automatically detect non-trivial patterns from petabytes of rapidly …
of algorithms that can automatically detect non-trivial patterns from petabytes of rapidly …
[HTML][HTML] Constructing decision rules for multiproduct newsvendors: An integrated estimation-and-optimization framework
AV Olivares-Nadal - European Journal of Operational Research, 2024 - Elsevier
In this paper, we develop an integrated estimation-and-optimization framework for
constructing decisions rules for the order quantities of multiple perishable products. The …
constructing decisions rules for the order quantities of multiple perishable products. The …
Container terminal daily gate in and gate out forecasting using machine learning methods
Container throughput is an essential indicator for measuring the container terminal's
efficiency. Gate in and gate out containers are the containers that are transported to and …
efficiency. Gate in and gate out containers are the containers that are transported to and …
Deep learning-based container throughput forecasting: A triple bottom line approach
Purpose Container throughput forecasting plays a pivotal role in strategic, tactical and
operational level decision-making. The determination and analysis of the influencing factors …
operational level decision-making. The determination and analysis of the influencing factors …