Investigating Challenges in Decision Support Systems for Energy-Efficient Ship Operation: A Transdisciplinary Design Research Approach

Benjamin Schwarz, Mourad Zoubir, Jan Heidinger, Marthe Gruner, Hans-christian Jetter, Thomas Franke
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Abstract

To increase energy-efficiency and reduce CO2e emissions in maritime shipping, Decision-Support Systems (DSS) can be leveraged. Specifically, in regard to reducing the greatest contributor to consumption, propulsion (IMO, 2021), by assisting seafarers in route planning, and timely and efficient re-planning, as well as general monitoring of ship’s energy dynamics. However, the successful integration and acceptance of these systems into the seafarer’s workflow pose significant challenges, such as goal conflicts, e.g. with safety or with the financial interests of different stakeholders, which require a deep understanding of interactions onboard and onshore.This paper reflects on our implementation of a transdisciplinary design research approach for developing novel, human-centered AI-based tools for energy-efficient ship operations. Of our concurrent studies, we describe selected forms of inquiry that together resulted in a holistic understanding of the application domain, target audience, and typical tasks as well as an interactive prototype of a decision support system for energy-efficient ship navigation.The research activities reported are based on human factors research concerning energy-efficient ship operations and focus on research through design in the sense of Jonas (2015) in the field of DSS for CO2e emission mitigation in navigation and ship operation, and the formative evaluation of a DSS prototype in a ship simulator environment (N = 22). By viewing these research activities through the lens of design research, more specifically the theoretical foundation of MAPS (Jonas et al., 2010), we systematically describe and discuss their individual contributions. MAPS specifically operationalized design research as “Matching Analysis, Projection and Synthesis”, enabling integrative, systematic research processes across boundaries of disciplinary bodies of knowledge, domains and actors.As a primary contribution, we reflect on our lessons learned to identify generalizable challenges for similar future projects of the maritime ergonomics community. These include (1) context-sensitive integration of navigational and operational data; (2) calibration of users’ expectations of the system’s capabilities; and related to this (3) increasing transparency of how the DSS retrieves and processes data, and of how confident it is in its suggestions. By considering key human factors, such as workload, autonomy and biases (e.g., automation bias) on the basis of our system, we demonstrate how these challenges can be addressed. As a secondary contribution, we also share our resulting designs as examples of how AI-based decision support for optimizing energy efficiency can be visually and functionally integrated into onboard ship operation and navigation.REFERENCESIMO, 2021. Fourth IMO GHG Study 2020. International Maritime Organization, London, UK.Jonas, W., 2015. Research through design is more than just a new form of disseminating design outcomes. Constructivist Foundations 11, 32–36.Jonas, W., Chow, R., Bredies, K., Vent, K., 2010. Far beyond dualisms in methodology – an integrative design research medium “MAPS.”
研究船舶节能运行决策支持系统的挑战:跨学科设计研究方法
为了提高能源效率并减少海运中的二氧化碳排放,可以利用决策支持系统(DSS)。具体而言,通过协助海员进行航线规划、及时有效的重新规划以及对船舶能量动态的全面监测,减少最大的消耗因素——推进力(IMO, 2021年)。然而,将这些系统成功整合和接受到海员的工作流程中会带来重大挑战,例如目标冲突,例如与安全或与不同利益相关者的经济利益相冲突,这需要深入了解船上和陆上的相互作用。本文反映了我们实施的跨学科设计研究方法,用于开发新型的、以人为本的基于人工智能的节能船舶操作工具。在我们的并行研究中,我们描述了选定的调查形式,这些形式共同导致对应用领域、目标受众和典型任务的整体理解,以及节能船舶导航决策支持系统的交互式原型。报告的研究活动基于船舶节能运行的人为因素研究,并侧重于Jonas(2015)在船舶运行中减少二氧化碳排放的DSS领域的设计研究,以及船舶模拟器环境中DSS原型的形成性评估(N = 22)。通过从设计研究的角度来看待这些研究活动,更具体地说是MAPS的理论基础(Jonas et al., 2010),我们系统地描述和讨论了他们的个人贡献。MAPS将设计研究具体化为“匹配分析、投影和综合”,实现跨学科知识、领域和参与者边界的综合、系统的研究过程。作为主要的贡献,我们反思了我们所学到的经验教训,以确定海事人体工程学社区未来类似项目的可概括的挑战。这些包括:(1)导航和操作数据的上下文敏感集成;(2)校准用户对系统能力的期望;与此相关的是(3)提高DSS如何检索和处理数据的透明度,以及它对其建议的信心。通过在我们系统的基础上考虑关键的人为因素,如工作量、自主性和偏见(例如,自动化偏见),我们展示了如何解决这些挑战。作为第二个贡献,我们还分享了我们的最终设计,作为基于人工智能的决策支持如何优化能源效率的例子,如何在视觉上和功能上集成到船上的操作和导航中。REFERENCESIMO, 2021年。2020年第四次国际海事组织温室气体研究报告。国际海事组织,英国伦敦。乔纳斯,W., 2015。通过设计进行研究不仅仅是传播设计成果的一种新形式。建构主义基础11,32-36。Jonas, W, Chow, R., Bredies, K., Vent, K., 2010。超越方法论上的二元论——一种综合设计研究媒介“MAPS”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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