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References BAINBRIDGE, L. (1983), Ironies of Automation, Automatica 19, pp.775-779 CREECH, J.A.; RYAN, J.F. (2003), AIS: The Cornerstone of National Security?, J. Navigation (U.K.), 56, pp.31-44. DARCE, K. (2004), Tracking River Traffic, The New Orleans Times-Picayune, 13 March 2004 GRABOWSKI, M.; ROBERTS, K.H. (1996), Human and organizational error in large-scale systems, IEEE Transactions on Systems, Man, and Cybernetics 26, pp.2-16 GRABOWSKI, M.; SANBORN, S.D. (2001), Evaluation of Embedded Intelligent Real-Time Systems, Decision Sciences, 32:1, pp.95-123. HART, S.; STAVELAND, L. (1998), Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research, in Hancock, P., Meshkati, N., Human Mental Workload. Elsevier Science Publishers B.V. NATIONAL RESEARCH COUNCIL (2003), Shipboard Automatic Identification System Displays: Meeting the Needs of Mariners, National Academy Press http://books.nap.edu/html/SR273/SR273.pdf NORMAN, D.A. (1990), The “Problem” with Automation: Inappropriate Feedback and Interaction, not “Over-Automation”, Proc. Royal Society B237 ROCHLIN, G. (1997), Trapped in the Net: The Unanticipated Consequences of Computerization, Princeton University Press SARTER, N.; WOODS, D.D. (1995), How in the World Did We Ever Get Into That Mode?, Human Factors 36 TENNER, E. (1996), Why Things Bite Back: Technology and the Revenge of Unintended Consequences, New York: Alfred A. Knopf WICKENS, C.D.; MAVOR, A.S.; McGEE, J.P. (editors) (1997), Flight to the Future: Human Factors in Air Traffic Control. Washington, D.C.: National Academy Press WIENER, E. L. (1988), Cockpit Automation, In Human Factors in Aviation, E.L. Wiener & D.C. Nagel (editors). San Diego: Academic Press WIENER, E.L.; CURRY, R.E. (1980), Flight Deck Automation: Promises and Problems, Ergonomics 23, pp.995-1011 16
Robust Pareto – Optimal Routing of Ships utilizing Ensemble Weather Forecasts Jörn Hinnenthal, Technical University Berlin, Berlin/Germany, hinnenthal@ism.tu-berlin.de Øyvind Saetra, The Norwegian Meteorological Institute, Blindern/Norway, oyvinds@met.no Abstract Sophisticated routing of ships establishes a multi-objective, non-linear and constrained optimization problem. The paper proposes an optimization approach to ship routing on the basis of hydrodynamic simulation and advanced weather forecast. Response amplitude operators are employed to describe a ship in waves. The probabilistic ensemble forecast is applied to account for the stochastic behavior of weather. An elaborated example is given for a container service between Europe (Le Havre) and North America (New York). A severe weather situation for the North Atlantic is taken into account. The robustness of the optimization result against weather changes is assessed. A perturbation approach with respect to a parent route and a B-spline technique to describe the route increase computational efficiency. The implementations uses a multi-objective genetic algorithm within the commercial software modeFRONTIER. 1. Introduction Ship monitoring, routing assistance and decision support became important topics for save and reliable sea transport. Types of Route optimization regarding weather and sea state can become an important tool to improve (i) the reliability of sea transport within a transport chain, (ii) comfort and safety of crew and passengers. In order to provide a meaningful database for a route decision support, a route optimization tool has to take different opposing objectives into account (e.g. estimated time of arrival and fuel consumption). Probable weather changes can affect optimized routes, abolish their advantages or even make them infeasible to sail. The approach presented here applies a stochastic, multi-objective optimization strategy on ship routing with regard to a deterministic wave forecast. This serves to provide a database of advantageous Pareto optimum routes. Subsequently the robustness of the optimization result regarding assumable weather changes is assessed. Therefore, an ensemble wave forecast generated by the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF) is utilized. The optimization presented here belongs to a westbound Atlantic crossing of the Hapag Lloyd panmax container ship ‘CMS Hannover Express’. 2. Deterministic and ensemble wave forecast Deterministic and ensemble forecast were provided by the ECMWF. They are wave forecasts of a severe sea condition in the North Atlantic from 20 th to 30 th of January 2002. Data for the significant wave height H 1/3 , the peak period T P and the wave angle β are given in time steps of 12 hours on a grid with 1.5° mesh size. While estimated time of arrival (ETA) and fuel consumption (FUEL) depend on all environmental conditions, accelerations and slamming are primarily influenced by swell. Swell - i.e., long-crested waves - being the crucial factor, it was decided to focus on this influence, leaving out wind-sea, current, wind, drift-ice, shallow-water etc. for later and more refined work. The deterministic forecast represents one likely development of the sea state but no information about the probability of its occurrence. In contrast, the operational ensemble prediction system (EPS) generates a set of 50 forecasts. These ensemble members have an almost equal probability of occurrence. They are generated by superposing small perturbations to the operational analysis before launching the forecast calculation. The ensemble spread measures the “differences” between the ensemble members. It is related to the forecast uncertainty. Small spread indicates low forecast uncertainty, and vice versa. Generally the uncertainty increases while going further into the forecast, ECMWF (2002). This characteristic of the ensemble forecast is used to assess the robustness of ship 17
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