On-Demand Bus is a Demand Responsive Transit (DRT) service that passengers will be transported by the vehicles after they reserve a seat. The vehicle won't move if there is no reservation and the efficiency is expected as a new transportation service. It allows potential passengers to request service via the Internet or mobile phone, with requests for ride being processed by a server computer. The requests compose of pick-up location, delivery location and desired delivery time (or pick-up time). The computer executes two main algorithms which are vehicle-choosing algorithm and routing algorithm. Using the vehicle-choosing algorithm was used for decision about which vehicle will accept the new request. And the routing algorithm was used to design the new route and schedule for the vehicle chosen to serve the new request. After calculation, the system will report to the customer whether the request is accepted or not. If it is accepted, the vehicle will pick up and deliver him to his destination within a guaranteed time -not later than the desired delivery time (or not earlier than pick-up time). We also carried out an experiment to evaluate the developed algorithm worked as we designed.
We seek to accelerate the adoption of multi-objective decision making (MODM) methods within transdisciplinary engineering. To this end, we specify a generic user interface that makes computational systems models more accessible to non-technical decision makers. The collection of user stories presented in this paper allude to minimum viable features to include in the future development and testing of a generic user interface for multi-objective decision making.
To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.
Various industries are undergoing transformation given recently available pervasive sensors, low-cost and low-latency digital communication, and distributed control technologies. The objective of this paper is to support the introduction of Internet of things (IoT) technologies in the maritime industry. The maritime industry is analyzed as a system of systems to define performance criteria and functions to be modeled and analyzed through simulation. In this case, the simulation of a shipping system includes models of operation, cargo loading, fuel loading, and docking for maintenance. In the simulation, various kinds of IoT technologies are defined by several input parameters. By changing the parameters, the simulator evaluates the impact of those technologies quantitatively. As a case study, 11 IoT technologies are evaluated and compared. The result reveals several insights that weight of the ship is the most impactful for the profit, controlling damage of the ship's hull by operation is the most important for safety, and improvement in efficiency at ports is the key to reducing delay time in operation. Moreover, this paper shows that the sensitivity analysis by changing the input parameters can support the decision making of how much investment will be effective in considering the technologies' levels.
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