Agent-Based Decision Support and Simulation for Wood Products Manufacturing

Elghoneimy, E.; Gruver, W.A.

doi:10.1109/tsmcc.2012.2213809

Cited by 12 publications

(4 citation statements)

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“…In AI, the term “intelligent agent” refers to an autonomous entity having goal-directed behavior in an environment using observation through sensors and execution actions through actuators (Russell & Norvig, 2022). Examples of the application of agents can be seen in the automotive industry (Society of Automotive Engineers, 2021), healthcare (Coronato et al, 2020; Loftus et al, 2020), unmanned aerial vehicles (UAV) (Hocraffer & Nam, 2017), manufacturing (Elghoneimy & Gruver, 2012), and recent development towards maritime autonomous surface ships (IMO, 2018). Even though agents can be very sophisticated and can perform certain task with a high degree of independence, they often require some form of human supervision in case of failures or unforeseen situations.…”

Section: Introductionmentioning

confidence: 99%

Agent Transparency, Situation Awareness, Mental Workload, and Operator Performance: A Systematic Literature Review

2022

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Objective In this review, we investigate the relationship between agent transparency, Situation Awareness, mental workload, and operator performance for safety critical domains. Background The advancement of highly sophisticated automation across safety critical domains poses a challenge for effective human oversight. Automation transparency is a design principle that could support humans by making the automation’s inner workings observable (i.e., “seeing-into”). However, experimental support for this has not been systematically documented to date. Method Based on the PRISMA method, a broad and systematic search of the literature was performed focusing on identifying empirical research investigating the effect of transparency on central Human Factors variables. Results Our final sample consisted of 17 experimental studies that investigated transparency in a controlled setting. The studies typically employed three human-automation interaction types: responding to agent-generated proposals, supervisory control of agents, and monitoring only. There is an overall trend in the data pointing towards a beneficial effect of transparency. However, the data reveals variations in Situation Awareness, mental workload, and operator performance for specific tasks, agent-types, and level of integration of transparency information in primary task displays. Conclusion Our data suggests a promising effect of automation transparency on Situation Awareness and operator performance, without the cost of added mental workload, for instances where humans respond to agent-generated proposals and where humans have a supervisory role. Application Strategies to improve human performance when interacting with intelligent agents should focus on allowing humans to see into its information processing stages, considering the integration of information in existing Human Machine Interface solutions.

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Section: Introductionmentioning

confidence: 99%

Agent Transparency, Situation Awareness, Mental Workload, and Operator Performance: A Systematic Literature Review

2022

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“…Respective DSSs can be broadly classified in data-or model-based. Data-based DSSs have been applied successfully for manufacturing systems [31], service development of business models [32] and product development in wind energy [33]. As the complex physical relationships in automotive systems are hard to be integrated into pure data analytical models, model-based DSSs have been widely used in the automotive industry.…”

Section: Related Workmentioning

confidence: 99%

Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering

Ling

2023

IEEE Open J. Intell. Transp. Syst.

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Towards customer-centric automotive systems engineering, it is essential to incorporate physical models and vehicle usage behavior into decision support systems (DSSs). Such DSSs tend to apply digital twin concepts, where simulations are parameterized with fine-grained time-series data acquired from customer fleets. However, logging vast amounts of data from customer fleets is costly and raises privacy concerns. Alternatively, these time-series data can be aggregated into vehicle usage statistics. The feasibility of creating digital twins from these vehicle usage statistics and the corresponding DSSs for systems engineering is yet to be established. This paper aims to demonstrate this feasibility by proposing a DSS framework that integrates four key elements of digital twinning: aggregate usage statistics from customer fleets, logging data from testing fleets, physical models for vehicle simulation, and evaluation models to derive decision support metrics. The digital twinning involves a four-step process: pre-processing, profiling, simulation, and post-processing. Based on a real-world fleet of 57110 vehicles and four evaluation metrics, a proof of concept is conducted. Results show that the digital twin covers the evaluation metrics of 99% of the vehicles and reaches an average fleet twinning accuracy of 91.09%, which indicates the feasibility and plausibility of the proposed DSS framework.

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“…Therefore, an independent intermediary in a distribution channel, which carries out tasks by itself and interacts with other companies, is fit to be modeled as an agent using computer programs to simulate its behavior and gain insight into supply chain management. Recently, the multiagent system (MAS) approach, a subdomain of ABM that comes from the discipline of distributed artificial intelligence (DAI), has been widely adopted as an intelligent IT support tool to study various SCM issues such as decision making [11], [33], supply chain coordination [34], product design engineering [35], planning and scheduling optimization problems in manufacturing processes [36], [37] etc. In fact, only a few studies have been conducted on the spatial market using ABM.…”

Section: Agent-based Modelingmentioning

confidence: 99%

“…Therefore, some researchers propose treating a supply chain as a complex adaptive system (CAS) in order to understand how the supply chain adapts to and co-evolves with the dynamic environment in which it exists [5]. All relevant aspects involved in a specific problem are considered and integrated into a systematic model, and suitable methodologies and tools from the systems engineering discipline are applied to deal with the complexity directly, such as systems architecture analysis [6], system dynamics [7], [8], and system simulation [9]- [11]. Agent-based modeling (ABM), one of simulation-based modeling methods, captures many of the challenges facing contemporary supply chain practices by dynamic modeling of the behaviors of firms and other entities in a supply chain [12].…”

mentioning

confidence: 99%

Evolutionary Location and Pricing Strategies in Competitive Hierarchical Distribution Systems: A Spatial Agent-Based Model

Cheng

Dong

et al. 2014

IEEE Trans. Syst. Man Cybern, Syst.

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Facing horizontal channel competition in a hierarchical distribution system, independent intermediaries such as wholesalers and retailers are keen to find the optimal location and pricing strategies that enable them to adapt to the increasingly competitive business environment. To help market intermediaries to address their challenges, we propose in this paper a spatial agent-based model (SAM), grounded in complex adaptive systems, which comprises four types of agents, namely the world, the manufacturer, firms, and consumers. We derive the firms' optimal behaviors in response to competition by evaluating the evolutionary location and pricing strategies using a genetic algorithm. We observe that a pyramid structure and the bullwhip effect in demand emerge from the evolutionary behavior of the SAM. We also find that buyers' searching ability enhanced by information technology has a significant effect on the degree of competition in a hierarchical distribution system. In addition, we find that firms that distribute elastic goods are likely to lower their prices to attract more buyers and move closer to their suppliers to save transport costs. In the case that the product demand is inelastic, intermediaries will move as close to their buyers as possible because they can maximize their profits in the SAM.Index Terms-Agent-based modeling, competitive location problem, complex adaptive system, hierarchical distribution system, location and pricing.

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Agent-Based Decision Support and Simulation for Wood Products Manufacturing

Cited by 12 publications

References 45 publications

Agent Transparency, Situation Awareness, Mental Workload, and Operator Performance: A Systematic Literature Review

Agent Transparency, Situation Awareness, Mental Workload, and Operator Performance: A Systematic Literature Review

Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering

Evolutionary Location and Pricing Strategies in Competitive Hierarchical Distribution Systems: A Spatial Agent-Based Model

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