Conference Agenda

In maritime logistics, container terminals serve as critical nodes for global trade but are facing intense pressure to mitigate their environmental footprint due to stringent climate policies in context of climate warming. Traditional transport and handling equipment heavily rely on diesel engines, making container terminal operations a primary source of localized greenhouse gas emissions and air pollutants. To address these challenges, transitioning toward electrified equipment has emerged as a key pathway to increasing energy efficiency while achieving localized zero-emission operations.

This paper provides a systematic literature review that outlines the basic framework for the electrification of container terminals. Given the recent breakthroughs in heavy-duty battery storage, smart grid integration, and automated charging systems, the academic and practical landscape has evolved rapidly. This study maps out newly emerged technological pathways, evaluates current operational challenges, and provides strategic recommendations for container terminal operators.

Following the PRISMA framework, a systematic review was conducted across major academic databases. The literature is classified based on key operational units (e.g., Rubber-Tyred Gantry cranes, Automated Guided Vehicles, and mobile yard equipment) and primary electrification dimensions, including grid-connected power supplies, hybrid powertrains, and fully battery-electric energy storage systems.

The analysis reveals a significant acceleration in research addressing fully battery-powered mobile equipment and dynamic charging strategies. While grid-connected solutions remain dominant for heavy cranes, recent publications increasingly focus on decentralized charging station placement within transport zones to maximize operational uptime. Furthermore, the review identifies key research streams concerning peak shaving via ultracapacitors, vehicle-to-grid (V2G) capabilities providing primary frequency control, and automated battery swapping.

The paper concludes with an integrated action framework that aligns equipment electrification with port automation and digitalization. Terminal operators are provided with strategic insights regarding infrastructure dimensioning, the utilization of off-peak electricity tariffs, and layout-dependent charging optimization. Ultimately, this study bridges current research gaps by providing a holistic, up-to-date guide for transitioning to sustainable, low-carbon container terminal operations.

Containerized maritime transport is a core pillar of global trade, yet persistent imbalances between import and export flows continuously generate spatial mismatches in container availability. To manage these imbalances, liner shipping companies rely on empty container repositioning (ECR), a complex and cost-intensive process that ensures equipment availability across global networks. In recent years, this task has become increasingly challenging due to recurring global disruptions, including pandemics, geopolitical conflicts, and supply chain volatility.

ECR decisions depend on accurate forecasts to estimate future container demand and support operational planning. Increased uncertainty during trade crises gives leverage to human planners during the forecasting process, as they review and adjust system-generated predictions using contextual and operational knowledge. Understanding how users interact with these forecasts, particularly under varying levels of uncertainty and across different forecast horizons, is therefore crucial for improving decision quality in operational logistics environments.

This study examines user adjustment behavior and its effectiveness in an operational empty container repositioning forecasting environment at a major liner shipping company through a large-scale field study. The analysis is based on a comprehensive dataset covering 2020–2024 with more than 1.3 million adjusted forecasts across multiple container types. It includes system-generated forecasts, predominantly produced by AI-based and statistical models, realized demand, and user-adjusted forecasts submitted by practitioners for empty container releases at depots and terminals serving customer export shipments.

The empirical setting distinguishes between a crisis period (2020–2022), marked by severe disruptions such as the COVID-19 pandemic and a more stable period (2023–2024). Methodologically, the study combines established Forecast Value Added metrics (e.g., RelAME, MAPE, bias) with regression-based models that examine the determinants of forecast adjustment behavior and its impact on forecast accuracy across forecast horizons and different levels of environmental uncertainty. The paper aims to answer the following question: (1) Do human planners improve forecast accuracy greater during times of crises (and if so, in which means) (2) does the forecast horizon influence adjustment behavior and performance, and (3) does the influence of the forecast horizon change between stable and unstable times?

This study contributes to research in several ways. First, it extends existing research by providing a large-scale empirical analysis of human adjustment behavior in maritime demand forecasting. Second, it builds on prior work by examining human responses to AI-generated forecasts in an ECR setting across multiple forecast horizons. Finally, the findings offer insights into the role of human judgment in expert-supported forecasting processes under conditions of operational disruption and uncertainty.

Results show a pronounced shift in user behavior during crisis conditions, particularly in the magnitude of forecast adjustments. While forecast value added is higher at shorter forecast horizons, overall human interventions tend to reduce forecast accuracy on average. During crisis periods, however, planners contribute value primarily through downward adjustments, indicating that human judgment can partially offset systematic over forecasting by system forecasts under uncertain conditions.

Housekeeping is a key process in terminals, where containers are rearranged during off-peak hours to minimize future reshuffles during retrieval. The classical Container Pre-Marshalling Problem (CPMP) assumes a deterministic retrieval sequence, an assumption that rarely holds in practice, particularly in rail-road terminals where retrieval times of import containers are largely unknown. This paper introduces the Stochastic Container Pre-Marshalling Problem (SCPMP), which explicitly models uncertainty in container retrieval times through pairwise retrieval probabilities.

Unlike existing robust approaches that require non-overlapping time windows or convert the problem back into a deterministic formulation, the SCPMP encodes uncertainty directly at the container level by delivering a more realistic representation of the operations. Pairwise probabilities P(i,j), representing the likelihood that container i is retrieved before container j, are derived from heterogeneous sources including Machine Learning-based dwell time forecasts, truck appointment systems, and train schedules. These probabilities naturally accommodate overlapping retrieval windows and serve as direct input parameters for both the optimization model and the solution algorithm.

We formulate the SCPMP as a binary integer program that minimizes the expected number of badly placed items under a fixed move budget, reflecting the limited crane capacity available during housekeeping periods. A threshold parameter τ controls which blocking probabilities are classified as critical, enabling operators to calibrate the trade-off between reshuffling effort and residual risk.

For the solution approach, we adapt the Iterative Deeping A* (IDA*) algorithm, established as state-of-the-art for the deterministic CPMP, to the stochastic setting. A central contribution is the development of improved heuristic bounds that tighten the search. Beyond the direct lower bound based on τ-classified badly placed items, we introduce a blocking bound (counting well-placed containers that physically obstruct access to badly placed ones) and a destination-deficit bound (identifying badly placed containers lacking any safe target stack). Both bounds are proven admissible and combined via a maximum operator, preserving optimality guarantees while significantly reducing the search space. Computational experiments show a median speedup factor of 2.6× in node expansions compared to the baseline heuristic.

A computational study on synthetic instances calibrated with real operational data from a German intermodal terminal demonstrates that the IDA* approach solves typical terminal configurations (up to 5 stacks, 4 tiers) within seconds. We further compare solution quality against the exact BIP formulation, analyze the impact of uncertainty levels on algorithmic performance, and evaluate final layouts using CV@R-based risk metrics. Results show that probabilistic information significantly reduces expected reshuffles and tail-risk compared to deterministic and robust reference approaches.