Effective barge scheduling in the logistic domain requires advanced information on the availability of the port terminals and the maritime traffic in their vicinity. To enable a long-term prediction of vessel arrival times, we investigate how to use the publicly available automatic identification system (AIS) data to identify maritime patterns and transform them into a directed graph that can be used to estimate the potential trajectories and destination points. To tackle this problem, we use a genetic algorithm (GA) to cluster vessel position data. Then, we show how to enhance the process to allow fast computation of incremental data coming from the sensors, including the importance of adding a quad tree structure for data preprocessing. Focusing on a real case implementation, characterized by partially incomplete and noisy AIS data, we show how the algorithm can handle routes intersecting the regions with missing data and the repercussions this has on the route graph. Finally, postprocessing is explained that handles graph pruning and filtering. We validate the results produced by the GA by comparing resulting patterns with known inland water routes for two Dutch provinces followed by the simulation using synthetic data to highlight the strengths and weaknesses of this approach.

Estimating the future position of a deep sea vessel more than 24 hours in advance is a major challenge for Dutch logistics service providers (LSPs). Their unscheduled arrival in ports directly impacts scheduling and waiting times of barges, propagating throughout the entire supply chain network. To help LSPs' planners improve planning operations, we intend to capture the characteristics of maritime routes for a specific region (the North Sea connecting the Netherlands and United Kingdom) in the form of a directed graph, which can be used as a foundation for predicting destination and arrival time of each associated vessel.To create such graph we need an efficient way to extract waypoints for traffic data and this is the problem we will address in this paper.Since LSPs only use publicly available data for arrival estimation, our solution is entirely based on Automatic Identification System (AIS) data. Extracting positional information from AIS, we explore various machine learning approaches to identify clusters. We apply DBSCAN algorithm and show its advantages and disadvantages when used on AIS data. The same process is repeated using meta-heuristics, comparing clustering results generated by a genetic algorithm and by modified ant-colony optimization to those produced by DBSCAN. Finally, we present a hybrid approach and its ability to discover waypoints, highlighting the achieved improvements.To extend the problem, two constraints are added. The first is the requirement to handle large volumes of streaming AIS data on standard PC-based hardware. The second introduces the common situation of "dark areas" in a map due to problems with receiving and transmitting AIS data. The algorithm discovers route waypoints in efficient and effective ways under these constraints.

The scheduling process in a typical business environment consists of predominantly repetitive tasks that have to be completed in limited time and often containing some form of uncertainty. The intelligence amplification is a symbiotic relationship between a human and an intelligent agent. This partnership is organized to emphasize the strength of both entities, with the human taking the central role of the objective setter and supervisor, and the machine focusing on executing the repetitive tasks. The output efficiency and effectiveness increase as each partner can focus on its native tasks. We propose the intelligence amplification framework that is applicable in typical scheduling problems encountered in the business domain. Using this framework we build an artifact to enhance scheduling processes in synchromodal logistics, showing that a symbiotic decision maker performs better in terms of efficiency and effectiveness.

The long term prediction of maritime vessels' destinations and arrival times is essential for making an effective logistics planning. As ships are influenced by various factors over a long period of time, the solution cannot be achieved by analyzing sailing patterns of each entity separately. Instead, an approach is required, that can extract maritime patterns for the area in question and represent it in a form suitable for querying all possible routes any vessel in that region can take. To tackle this problem we use a genetic algorithm (GA) to cluster vessel position data obtained from the publicly available Automatic Identification System (AIS). The resulting clusters are treated as route waypoints (WP), and by connecting them we get nodes and edges of a directed graph depicting maritime patterns. Since standard clustering algorithms have difficulties in handling data with varying density, and genetic algorithms are slow when handling large data volumes, in this paper we investigate how to enhance the genetic algorithm to allow fast and accurate waypoint identification. We also include a quad tree structure to preprocess data and reduce the input for the GA. When the route graph is created, we add post processing to remove inconsistencies caused by noise in the AIS data. Finally, we validate the results produced by the GA by comparing resulting patterns with known inland water routes for two Dutch provinces.

In this paper, we present a method for systematic literature search based on the symbiotic partnership between the human researcher and intelligent agents. Using intelligence amplification, we leverage the calculation power of computers to quickly and thoroughly extract data, calculate measures, and visualize relationships between scientific documents with the ability of domain experts to perform qualitative analysis and creative reasoning. Thus, we create a foundation for a collaborative literature search system (CLSS) intended to aid researches in performing literature reviews, especially for interdisciplinary and evolving fields of science for which keyword-based literature searches result in large collections of documents beyond humans' ability to process or the extensive use of filters to narrow the search output risks omitting relevant works. Within this article, we propose a method for CLSS and demonstrate its use on a concrete example of a literature search for a review of the literature on human-machine symbiosis.