Intelligence Amplification Framework for Enhancing Scheduling Processes

Dobrkovic, Andrej; Liu, Luyao; Iacob, Maria-Eugenia; Hillegersberg, Jos van

doi:10.1007/978-3-319-47955-2_8

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…The experimental use and potential impact of IA in the logistics is demonstrated using the serious game Synchromania by Dobrkovic et al (2016) and to support the process of literature reviewing (Dobrkovic et al, 2018). The work done in SynchromodalIT is extended by actually implementing IA in operational environments.…”

Section: Applying Iamentioning

confidence: 98%

Designing intelligence amplification: a design canvas for practitioners

Piest¹,

Iacob²,

Wouterse³

2022

AHFE International

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Designing, developing, and implementing applications based on the concepts of Intelligence Amplification (IA) is a complex process. Although some design theories are present in literature, to our best knowledge, no comprehensive IA design approach exists for practitioners. Based on action design research, an IA design canvas is developed and guiding design principles are derived in two iterations. The main contribution of this research is a comprehensive IA design approach, consisting of an IA design canvas and four guiding design principles. Evaluation of the IA design canvas in three concurrent design workshops with 25 participants representing, 14 organizations, provides empirical support that the proposed IA design approach can ease the design processes, especially during the emphasize, ideate, and conceptualize stages of design thinking. Generalization is however not possible. Future research can explore the broader use of the IA canvas for explanation, analysis, prediction, and quantification, and formalize the IA design approach in a design theory.

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Section: Applying Iamentioning

confidence: 98%

Designing intelligence amplification: a design canvas for practitioners

Piest¹,

Iacob²,

Wouterse³

2022

AHFE International

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“…The main aim of our system design is to automate the dynamic planning activities, but dynamic events may also change the stakeholders' preferences during the actual execution of the initial plan. Therefore, our system should include a symbiotic relationship where intelligent agents focus on task execution, while human stakeholders can modify objectives, constraints and decision parameters [25].…”

Section: Requirement Analysismentioning

confidence: 99%

A Reference Architecture for IoT-Enabled Dynamic Planning in Smart Logistics

Koot

Iacob

Mes

2021

Advanced Information Systems Engineering

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Increasing customer demands and variability in today's logistics networks force fleet operators to become more reliable and flexible in their operations. As modern-day fleets are well equipped with wireless sensing, processing, and communication devices, fleet operators could proactively respond to dynamic events. However, the use of real-time sensor data to achieve re-optimization is scarce. This observation raises the question of how logistics operators should incorporate the emerging track-and-trace services into their dynamic planning activities. In this paper, we propose a reference architecture that relies on both the Internet of Things and the Smart Logistics paradigms, and aims at enhancing the resilience of logistics networks. Since the decision of when to reschedule the network's configurations remains nontrivial, we propose a hierarchical set of disruption handling systems to facilitate the trade-off between decision quality and response time. In our design, autonomous logistics agents can quickly anticipate on minor changes in their surroundings, while more severe disruptions require both more data and computational power in higher-level processing nodes (e.g., fog/cloud computing, machine learning, optimization algorithms). We illustrate the need of our architecture in the context of the dynamic vehicle routing problem.

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“…The agent successfully learned how to allocate products according to the requirements prioritized by the company. Using intelligence amplification [26], warehouse employees were able to use the agent and increase their slotting performance. The model was tested in an experimental setting and validated using expert opinions.…”

Section: B Earlier and Related Workmentioning

confidence: 99%

A Reinforcement Learning Platform for Small and Medium-sized Enterprises in Logistics

Piest

Iacob

Sinderen

et al. 2021

2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW)

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While real-time data and sophisticated Reinforcement Learning (RL) approaches are emerging, logistic organizations, in particular Small and Medium-size d Enterprises (SMEs), lack the tools and expertise to effectively identify whether (parts of) their business processes are suitable for using RL and adopt these approaches in their daily practice. This paper presents the results of our efforts to design, develop, test and implement a RL-based decision support platform based on the Open Trip Model (OTM) for the logistics industry . The main contribution of this paper is a potentially generalizable platform architecture and instantiation of the following main functional components: 1) a graphical user interface to access platform services, 2) APIs to automate the data collection based on the OTM, 3) an OTM compliant data storage, 4) a repository and tool for testing multiple algorithms and perform (hyper)parameter tuning, and 5) infrastructure provisioning to run and monitor agents. The platform is complemented with RL guidelines to transfer the platform, knowledge, and practices to SMEs. The platform architecture is validated with a panel of experts and demonstrated in use in a case study at a logistics services provider. The platform demonstration and case study contribute to increasing awareness of potential applications of RL in the logistics industry. The platform provides a foundation for empirical research and experimental development of RL approaches in logistics. Future research will focus on alternative and hybrid approaches, federated learning, and incorporating data sharing concepts as part of the envisioned federated data sharing infrastructure for the Dutch logistics industry.

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Intelligence Amplification Framework for Enhancing Scheduling Processes

Cited by 8 publications

References 14 publications

Designing intelligence amplification: a design canvas for practitioners

Designing intelligence amplification: a design canvas for practitioners

A Reference Architecture for IoT-Enabled Dynamic Planning in Smart Logistics

A Reinforcement Learning Platform for Small and Medium-sized Enterprises in Logistics

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