Model-based design of supervisory controllers for baggage handling systems

Swartjes, L.; Beek, D.A. van; Fokkink, Wan; Eekelen, J.A.W.M. van

doi:10.1016/j.simpat.2017.08.005

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…Supervisory control synthesis benefits from having (almost) a product system, see Sect. 3 and the work of [5,9,12,26,35]. In software engineering, this modeling method is called component-based modeling, see [13].…”

Section: Modeling With the Input-output Perspectivementioning

confidence: 99%

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Modeling Guidelines for Component-Based Supervisory Control Synthesis

Goorden

Mortel-Fronczak

Reniers

et al. 2020

Formal Aspects of Component Software

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Section: Modeling With the Input-output Perspectivementioning

confidence: 99%

“…In [29], a method for modeling cyber-physical systems is presented utilizing templatebased modeling of [7,14]. Finally, several modeling guidelines are proposed in [35] based on experience with modeling bagage-handling systems, which is described and modeled in [34].…”

Section: Introductionmentioning

confidence: 99%

Modeling Guidelines for Component-Based Supervisory Control Synthesis

Goorden

Mortel-Fronczak

Reniers

et al. 2020

Formal Aspects of Component Software

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“…4 The ESCET project, an Eclipse Foundation open-source project since 2020, builds upon decades of research and tool development at Eindhoven University of Technology. Vital for the evolvement from an academic into an industrially applicable toolkit are the years-long ongoing research collaborations with industry, including Rijkswaterstaat [7], ASML [27], and Vanderlande [29]. Rijkswaterstaat, part of the Dutch Ministry of Infrastructure and Water Management, is responsible for infrastructure in the Netherlands, including roads, bridges, tunnels, and waterway locks.…”

Section: Introductionmentioning

confidence: 99%

Eclipse ESCET™: The Eclipse Supervisory Control Engineering Toolkit

Fokkink

Goorden

Hendriks

et al. 2023

Lecture Notes in Computer Science

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The Eclipse Supervisory Control Engineering Toolkit (ESCET™) is an open-source project to provide a model-based approach and toolkit for developing supervisory controllers, targeting their entire engineering process. It supports synthesis-based engineering of supervisory controllers for discrete-event systems, combining model-based engineering with computer-aided design to automatically generate correct-by-construction controllers. At its heart is supervisory controller synthesis, a formal technique for the automatic derivation of supervisory controllers from the unrestricted system behavior and system requirements. Vital for the future development of these techniques and tools is the ESCET project’s open environment, allowing industry and academia to collaborate on creating an industrial-strength toolkit. We report on some crucial developments of the toolkit in the context of research projects with Rijkswaterstaat and ASML that have considerably improved its capability to deal with the complexity of real-life systems as well as its usability.

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“…The suggested algorithm reduces the imbalances for the airlines sharing the BHTS collection conveyor, while maintaining overall BHTS performance at an acceptable level. [12] shows how a stateof-the-art model-based design framework has been successfully used for model-based design of supervisory controllers for an actual industrial baggage handling system, and for a real-time emulation model of an actual international airport. The high-level modeling elements of the applied model-based design framework allow the modeler to concentrate on implementing the BHTS design requirements.…”

Section: Introductionmentioning

confidence: 99%

Reducing CO₂ Emissions of an Airport Baggage Handling Transport System Using a Particle Swarm Optimization Algorithm

Lodewijks

Cao²,

Zhao

et al. 2021

IEEE Access

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Optimizing the design of an airport baggage handling transport system (BHTS) with respect to the minimization of the total costs and energy consumption is essential to reduce costs and Carbon dioxide (chemical formula CO2) emissions in airport operations. This paper introduces a mathematical model that comprehensively considers relevant costs regarding the operation of belt conveyors in a BHTS. Specifically, the Capital Expenditure (CapEx) and Operational Expenditure (OpEx) are considered in the airport BHTS cost function. Furthermore, to include the impact of CO2 emissions, the offsetting costs of CO2 emissions are included in the airport BHTS cost function. This function forms the basis of an objective function that can be used to optimize the airport BHTS's design by metaheuristic algorithms. Three state-of-the-art particle swarm optimization (PSO) algorithms are utilized to solve the airport BHTS optimization problem. The results of experiments show that the three PSO variants can solve the optimization problem effectively and efficiently. The self-regulation PSO algorithm performed the best in terms of CPU time and has been used for the case studies. Extensive tests of the impact of key parameters, e.g., capacity and system length, on the optimized solutions have been conducted. Experiments show that a system with several belt conveyors of shorter lengths performs better than a system with one long conveyor. In reality however, more parameters play a role like the varying baggage throughput per hour and therefore the BHTS problem needs to be optimized case-bycase. Optimizing an airport BHTS design leads to a significant reduction in CO2 emission and thus costs.INDEX TERMS Baggage handling transport system, airport operations, belt conveyors, CO2 emission, particle swarm optimization.

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Model-based design of supervisory controllers for baggage handling systems

Cited by 7 publications

References 34 publications

Modeling Guidelines for Component-Based Supervisory Control Synthesis

Modeling Guidelines for Component-Based Supervisory Control Synthesis

Eclipse ESCET™: The Eclipse Supervisory Control Engineering Toolkit

Reducing CO₂ Emissions of an Airport Baggage Handling Transport System Using a Particle Swarm Optimization Algorithm

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