Abstract
Since requirements of service demands are becoming increasingly complex and diversified, one of the success factors of a multimodal service system is its capability to design a specific service instance satisfying a specific set of requirements. This capability is further highlighted in Ad Hoc Multimodal Service Systems (AHMSSs), where service instances rarely follow a standard form of service delivery and exist only for a limited time. However, due to the increasing scale and frequency of services in many business and public sectors, meeting the desired level of capability has become troublesome. A well-designed Artificial Intelligence (AI) approach can be a solution to the difficulty by addressing the underlying complexity and uncertainty of the AHMSS design process. To conceptualize and foster AI applications to an AHMSS, this study identifies key decision-making problems in the AHMSS design process and discusses the role of AI in the process. The results will form the basis for AI development and implementation for an AHMSS and relevant service systems.
Highlights
Multimodal service systems deliver services to demands by using a combination of multiple means or resources of the systems
While there has been a lot of research on disaster relief logistics using multimodal service systems [3], where an Ad Hoc Multimodal Service Systems (AHMSSs) is suitable for application, the current state is rather focused on optimization problems with well-defined networks during a disaster response phase
The same is applied to the AHMSS design process where the decision-making capability for service configuration and resource allocation in limited time is essential
Summary
Multimodal service systems deliver services to demands by using a combination of multiple means or resources of the systems. We aim to further conceptualize the decision support system for AHMSS and to leverage the use of AI as the key solution for the challenges To achieve this goal, we design a five-step AHMSS design process. Note that existing AI/ML studies in literature focus on specific parts of a decisionmaking process (e.g., optimization problem solving by AI and situational awareness creation by ML), neglecting a systematic and holistic perspective on the process To avoid such a fragmented application of AI/ML to a decision-making process, the outcome of the systematic approach for the AHMSS design will be transformed into a contextual solution architecture that describes the AI functions and relevant system elements required to perform the functions for a decision-making process.
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