The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices

This paper contributes to the general consideration of whether a policy of incentivising improved forecasts for renewable energy outputs, and making them more available in the daily electricity market, would be beneficial. Using data from the German electricity market, we investigate the effect of wind and solar energy forecasts errors on imbalance volumes and intraday spot electricity prices. We use ordinary least square regression, quantile regression and autoregressive moving averages to identify these relationships using variables that have a quarter-hourly data granularity. The results show a positive relationship between wind forecast errors and imbalance volumes. We find that wind forecast errors impact spot prices more than solar forecasting errors. Policy incentives to improve the accuracy and availability of renewable energy forecasts should therefore be encouraged

Analyzing Sustainability Initiatives of the Airline Industry Through Random Forest Classification and K- Means Clustering Techniques

This analysis delves into sustainability within the aviation sector using machine learning and clustering. It uncovers distinct airline clusters based on sustainability focus. The study was conducted utilizing both the Random Forest algorithm and the K-means clustering algorithm. Despite uncovering trends, the analysis concentrates on 16 out of 17 United Nations sustainability goals, overlooking one aspect. Future research could benefit from better data collection and advanced models to improve sustainability analyses in aviation and similar industries

A Reverse Logistics Network Model for Handling E-commerce Returns

E-commerce supply chains are becoming more complex due to increasing global sales, and product returns from these sales are alarmingly high, highlighting the importance of effective return management. This paper proposes a reverse logistics network model to optimize return management. The proposed model applies ward-like hierarchical clustering with geographical constraints to detect return tendencies and utilizes mixed integer linear programming to optimize the network. The decision variables of the model include selection of Initial Collection Centers (ICCs), allocation of customer markets to ICCs, and optimal return volumes to be sent to each fulfillment center and recycling center from ICCs. The validity of the proposed model is established through a case study conducted in the consumer electrical and electronics sector of an e-commerce firm, providing 39.9% cost savings on average compared to the current Reverse Logistics (RL) network operation. This study contributes to the literature by integrating industry 4.0 technologies into the assessment of RL and facility planning with network optimization. The proposed RL network model serves as an operational planning tool, providing directions to e-commerce firms on optimizing RL networks and utilizing partner networks with integrated decision making for product returns.</p

Unmanned Aerial Vehicle Adaptation to Facilitate Healthcare Supply Chains in Low-Income Countries

Low-income countries are persistently suffering from last-mile logistics issues in healthcare supply chains. Therefore, it is high time to explore technological applications to overcome such inadequacies. The faster speed, low maintenance cost, and absence of road dependency in unmanned aerial vehicles (UAV) have popularized them as an alternative to road delivery. Hence, it is suggested as a solution to overcome the persisting distribution inefficiencies in healthcare logistics of low-income countries. According to the case study analysis conducted on the Sri Lankan vaccine cold chain, incorporating UAVs increases truck-space utilization and reduces the time consumed, cost incurred, and carbon dioxide emission in a delivery round. Moreover, the most suitable way to cover the initial setup cost of an unmanned aerial system (UAS) is by receiving aid from international donors. The capital cost also can be covered by government investments or via service outsourcing only if the number of flights per year is increased. Moreover, a homogenous (i.e., only UAV) solution was revealed to be more beneficial than a heterogeneous (i.e., truck and UAV) solution. However, due to the lack of technology literacy and willingness to change in low-income countries, it is recommended to initially execute a heterogeneous solution and expand to a homogeneous plan in the future years. However, it was evident that for a mixed-fleet solution to be advantageous, drone characteristics play a vital role. Hence, a UAV with specifications ideal for the use case must be utilized to garner the maximum benefits. Nevertheless, it was apparent that with the right implementation plan, UAVs possess the potential to overcome the shortcomings in the healthcare logistics of low-income countries