Vessel delays and increased terminal call sizes negatively impact the ability to properly plan daily operations at seaport container terminals. Such traffic patterns lead to, among others, infrequent peak loads at the seaside of container terminals, complicating terminal operations. Thus, relying on annual or monthly statistics fails to account for these day-to-day fluctuations. When container terminals are planned, be it a greenfield or brownfield terminal, these variations in operations need to be accounted for. The traditional formula-based approach to design terminals uses annual statistics. In this study, it is first used to produce estimates for the required yard capacity for three existing exemplary container terminals. These are then compared to the results of numerical experiments using the synthetic container flow generator ConFlowGen. The findings reveal that yard capacity requirements fluctuate considerably depending on the timing of vessel arrivals and their call sizes. This dynamic modeling proved particularly beneficial for planning gateway traffic, offering more accurate storage capacity predictions. Suggestions are made for how to further develop ConFlowGen for handling transshipment traffic better in future versions.
In order to manage the demand and control the flow of cargo arriving at terminals, the port sector created a dynamic Truck Appointment System. However, disruptive events can cause a delay or an early arrival of trucks at the port terminal, leading to long waiting times, queues, and the need to reschedule trucks in other time windows when they arrive off the scheduled time. Smart technologies offer the potential to deal with uncertain scenarios and create a flexible context for the use of TAS. The main objective of this study is to compare regression and classification Machine Learning algorithms to predict truck arrival times. By comparing the predictions with the original appointment, a flexible Truck Appointment System is built. Four different ML approaches were evaluated, which have been implemented in Python: Linear Regression, Random Forest, Gradient Boosting Regression, and Decision Tree. Considering the disruptive arrivals, we identified that the classification algorithms performed better than the regression algorithms predicting the exact arrival time, but worse than the regression algorithms that predict the time window of truck arrivals.
The convergence of Value Stream Management with cutting-edge technologies represents a dynamic area of research, as underscored by recent studies. These studies reveal a growing emphasis on digitalization and share a common goal: proposing data-driven techniques to enhance and optimize conventional Value Stream Management struggling to adapt in rapidly changing environments. By the present paper, a digital Value Stream Map according to the Digital Twin (DT) concept is investigated. This Digital Value Stream Twin (DVST) is based on the orchestration of multiple DT, representing core elements of Value Stream Management such as material flowing through the value stream and related resources. Overcoming the fixed structure of the automation pyramid, business application systems and machine signals are merged as data sources into one model, verified by a business scenario, mainly carried out in an SAP S4/HANA (ERP - enterprise resource planning) test environment. In this context, the present study is built upon a validation using a digital value stream model according to the Digital Shadow (DS) approach. Conceptually, the expansion of the DS into a DT is described. From this, potentials regarding the value stream method are derived and investigated.
Despite the evident connections between Demand Forecasting and Inventory Control, both researchers and practitioners tend to perform and analyze these issues separately. Yet, the application of appropriate Demand Forecasting Methods promises meeting inventory related target values while reducing inventory costs. A significant challenge consists in identifying the appropriate Demand Forecasting Method. Thus, practitioners require a framework that supports the decision process of selecting said method. Depending on the chosen forecasting method, different configurations of an inventory control policy might be suitable. The aim of this work is to facilitate the complex task of connecting the forecasting method selection and inventory control policy configuration for a group of numerous and heterogeneous products. Thus, a simple framework that generates recommendations regarding the appropriate forecasting method and inventory policy will be devised and empirically tested. Two of the three suggested forecast methods will be investigated further. An example application shows that the concept enables a significant reduction of stockouts which translates to higher service levels. The proposed methods therefore contribute to efficient and economically sustainable warehouse operations and inventory control management.
Green hydrogen production, distribution and use is seen as a central element of a carbon-neutral economy. Specifically, the establishment of offshore green hydrogen production facilities amidst wind energy parks is seen as a promising concept for European countries like Germany, which documented the first offshore wind energy hydrogen production during 2023 and plans significant production volume extensions in this regard. Yet, such green hydrogen manufacturing and distribution concepts are not evaluated in a comprehensive sustainability perspective. In order to avoid unintended sustainability effects, an ex-ante evaluation regarding the three triple bottom line perspectives of environmental, economic and social sustainability is advisable. As especially offshore green hydrogen production and transportation concepts are completely new, even the evaluation concept to be used for such a required comprehensive sustainability check is largely missing. Although dedicated evaluation and decision support methods in the fields of LCA and SLCA are available for sustainability evaluation issues, the question of selecting matching method frameworks and relevant evaluation categories for a future offshore-based green hydrogen supply chain is yet to be answered. This contribution is provided by this paper in a conceptual approach based on existing method sets and analytical results for neighboring application fields like solar or biogas green hydrogen production and distribution.
