The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform

Amarnath Banerjee - One of the best experts on this subject based on the ideXlab platform.

  • Intelligent scheduling and motion control for household vacuum cleaning robot system using simulation based optimization
    Proceedings - Winter Simulation Conference, 2016
    Co-Authors: Hyunsoo Lee, Amarnath Banerjee
    Abstract:

    This research considers overall scheduling of a vacuum cleaning robot that includes multi cleaning cycles. Even though there are research studies for generating paths for a device, the paths in each cycle tend to be similar from the fact that the motion planning is based on one tour of a target space. This paper suggests a new and effective simulation based optimization (So) Framework for generating an overall schedule and an effective path for each cycle. In the simulation stage, a dust prediction model is generated using abSorbed dust data and floor information. This process uses a multi-modal Gaussian mixture model as a basic model. The generated prediction model provides the needed constraints for different mathematical programming models in the optimization stage. The proposed Framework is considered as an efficient scheduling method in terms of minimizing redundant paths while maintaining tolerable dust levels during multi cleaning cycles.

  • Winter Simulation Conference - Intelligent scheduling and motion control for household vacuum cleaning robot system using simulation based optimization
    2015 Winter Simulation Conference (WSC), 2015
    Co-Authors: Hyunsoo Lee, Amarnath Banerjee
    Abstract:

    This research considers overall scheduling of a vacuum cleaning robot that includes multi cleaning cycles. Even though there are research studies for generating paths for a device, the paths in each cycle tend to be similar from the fact that the motion planning is based on one tour of a target space. This paper suggests a new and effective simulation based optimization (So) Framework for generating an overall schedule and an effective path for each cycle. In the simulation stage, a dust prediction model is generated using abSorbed dust data and floor information. This process uses a multi-modal Gaussian mixture model as a basic model. The generated prediction model provides the needed constraints for different mathematical programming models in the optimization stage. The proposed Framework is considered as an efficient scheduling method in terms of minimizing redundant paths while maintaining tolerable dust levels during multi cleaning cycles.

Andrea Araldo - One of the best experts on this subject based on the ideXlab platform.

  • System-level optimization of multi-modal transportation networks for energy efficiency using perSonalized incentives: formulation, implementation, and performance
    Transportation Research Record, 2019
    Co-Authors: Andrea Araldo, Ravi Seshadri, Hossein Ghafourian, Sayeeda Ayaz, David Sukhin, Carlos Lima Azevedo, Moshe Ben-akiva
    Abstract:

    The paper presents the system optimization (So) Framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation system. From the user's perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives, consisting of a combination of departure time, mode, and route. Each alternative is rewarded with an amount of tokens which the user can later redeem for goods or services. The role of So is to compute the optimized set of tokens asSociated with the available alternatives to minimize the system-wide energy consumption under a limited token budget. To do So, the alternatives that guarantee the largest energy reduction must be rewarded with more tokens. So is multi-modal, in that it considers private cars, public transit , walking, car pooling, and So forth. Moreover, it is dynamic, predictive, and perSonalized: the same alternative is rewarded differently, depending on the current and the predicted future condition of the network and on the individual profile. The paper presents a method to Solve this complex optimization problem and describe the system architecture, the multi-modal simulation-based optimization model, and the heuristic method for the online computation of the optimized token allocation. Finally it showcases the Framework with simulation results.

  • system level optimization of multi modal transportation networks for energy efficiency using perSonalized incentives formulation implementation and performance
    Transportation Research Record, 2019
    Co-Authors: Andrea Araldo, Song Gao, Ravi Seshadri, Carlos Lima Azevedo, Hossein Ghafourian, Yihang Sui, Sayeeda Ayaz, David Sukhin, Moshe Benakiva
    Abstract:

    The paper presents the system optimization (So) Framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation sys...

Hyunsoo Lee - One of the best experts on this subject based on the ideXlab platform.

  • Intelligent scheduling and motion control for household vacuum cleaning robot system using simulation based optimization
    Proceedings - Winter Simulation Conference, 2016
    Co-Authors: Hyunsoo Lee, Amarnath Banerjee
    Abstract:

    This research considers overall scheduling of a vacuum cleaning robot that includes multi cleaning cycles. Even though there are research studies for generating paths for a device, the paths in each cycle tend to be similar from the fact that the motion planning is based on one tour of a target space. This paper suggests a new and effective simulation based optimization (So) Framework for generating an overall schedule and an effective path for each cycle. In the simulation stage, a dust prediction model is generated using abSorbed dust data and floor information. This process uses a multi-modal Gaussian mixture model as a basic model. The generated prediction model provides the needed constraints for different mathematical programming models in the optimization stage. The proposed Framework is considered as an efficient scheduling method in terms of minimizing redundant paths while maintaining tolerable dust levels during multi cleaning cycles.

  • Winter Simulation Conference - Intelligent scheduling and motion control for household vacuum cleaning robot system using simulation based optimization
    2015 Winter Simulation Conference (WSC), 2015
    Co-Authors: Hyunsoo Lee, Amarnath Banerjee
    Abstract:

    This research considers overall scheduling of a vacuum cleaning robot that includes multi cleaning cycles. Even though there are research studies for generating paths for a device, the paths in each cycle tend to be similar from the fact that the motion planning is based on one tour of a target space. This paper suggests a new and effective simulation based optimization (So) Framework for generating an overall schedule and an effective path for each cycle. In the simulation stage, a dust prediction model is generated using abSorbed dust data and floor information. This process uses a multi-modal Gaussian mixture model as a basic model. The generated prediction model provides the needed constraints for different mathematical programming models in the optimization stage. The proposed Framework is considered as an efficient scheduling method in terms of minimizing redundant paths while maintaining tolerable dust levels during multi cleaning cycles.

Moshe Benakiva - One of the best experts on this subject based on the ideXlab platform.

Moshe Ben-akiva - One of the best experts on this subject based on the ideXlab platform.

  • System-level optimization of multi-modal transportation networks for energy efficiency using perSonalized incentives: formulation, implementation, and performance
    Transportation Research Record, 2019
    Co-Authors: Andrea Araldo, Ravi Seshadri, Hossein Ghafourian, Sayeeda Ayaz, David Sukhin, Carlos Lima Azevedo, Moshe Ben-akiva
    Abstract:

    The paper presents the system optimization (So) Framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation system. From the user's perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives, consisting of a combination of departure time, mode, and route. Each alternative is rewarded with an amount of tokens which the user can later redeem for goods or services. The role of So is to compute the optimized set of tokens asSociated with the available alternatives to minimize the system-wide energy consumption under a limited token budget. To do So, the alternatives that guarantee the largest energy reduction must be rewarded with more tokens. So is multi-modal, in that it considers private cars, public transit , walking, car pooling, and So forth. Moreover, it is dynamic, predictive, and perSonalized: the same alternative is rewarded differently, depending on the current and the predicted future condition of the network and on the individual profile. The paper presents a method to Solve this complex optimization problem and describe the system architecture, the multi-modal simulation-based optimization model, and the heuristic method for the online computation of the optimized token allocation. Finally it showcases the Framework with simulation results.