The increasing rate of vehicle population leads to traffic congestion and gas emission. Frequent traffic congestion increases commuting time and affects efficiency in work. Raising the proportion of public transportation is a solution to the issue. Compared with conventional transportation, Individualized Public Transportation System (IPTS) is more flexible and can be customized for every passenger. This paper proposes a four-stage system design method. Scheduling, dispatching and order responding algorithm are design to meet the demand of IPTS. We define the concept of Available Area for each bus, in order to recognize whether a new passenger order can be added to the bus without detour. We present the results in two scenarios: uniform distributed passengers and commuting passengers. IPTS reduces total travel time and waiting time compared to conventional transportation.
{"title":"A Multi-Stage Dispatching and Scheduling Algorithm for Individualized Public Transportation System","authors":"Zhenming Yang, Xuetao Wang, Chenghao Li, Qianchuan Zhao","doi":"10.1109/COASE.2019.8843019","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843019","url":null,"abstract":"The increasing rate of vehicle population leads to traffic congestion and gas emission. Frequent traffic congestion increases commuting time and affects efficiency in work. Raising the proportion of public transportation is a solution to the issue. Compared with conventional transportation, Individualized Public Transportation System (IPTS) is more flexible and can be customized for every passenger. This paper proposes a four-stage system design method. Scheduling, dispatching and order responding algorithm are design to meet the demand of IPTS. We define the concept of Available Area for each bus, in order to recognize whether a new passenger order can be added to the bus without detour. We present the results in two scenarios: uniform distributed passengers and commuting passengers. IPTS reduces total travel time and waiting time compared to conventional transportation.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"13 1","pages":"745-750"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90040797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8842869
Lijie Zhou, Chengran Lin, Biao Hu, Zhengcai Cao
The scheduling problem of a semiconductor production line with a constrained waiting time is studied in this paper. This problem can be regarded as an expanded flexible job-shop scheduling problem, which can often be described by a mixed integer nonlinear programming model. An improved cuckoo search algorithm is proposed to minimize the weighted completion time including the penalty of constrained waiting time violation. In the algorithm, we propose using an onedimensional chaotic search strategy to make full use of the local space information. In addition, we introduce backtracking search into our algorithm to ensure a desired diversification of population. Experimental results demonstrate that our proposed approaches outperform several other state-of-the-art meta-heuristics.
{"title":"A Cuckoo Search-Based Scheduling Algorithm for a Semiconductor Production Line with Constrained Waiting Time","authors":"Lijie Zhou, Chengran Lin, Biao Hu, Zhengcai Cao","doi":"10.1109/COASE.2019.8842869","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842869","url":null,"abstract":"The scheduling problem of a semiconductor production line with a constrained waiting time is studied in this paper. This problem can be regarded as an expanded flexible job-shop scheduling problem, which can often be described by a mixed integer nonlinear programming model. An improved cuckoo search algorithm is proposed to minimize the weighted completion time including the penalty of constrained waiting time violation. In the algorithm, we propose using an onedimensional chaotic search strategy to make full use of the local space information. In addition, we introduce backtracking search into our algorithm to ensure a desired diversification of population. Experimental results demonstrate that our proposed approaches outperform several other state-of-the-art meta-heuristics.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"32 1","pages":"338-343"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77832769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8843205
Peng Peng, Jiaxin Zhao, Yi Zhang, Heming Zhang
Data-driven techniques become increasingly popular in the field of industrial fault detection. Regarding the complex nonlinear industrial process accompanied by multiple operational monitoring modes, conventional multivariate monitoring techniques such as kernel principal component analysis (KPCA) are not suitable. In this paper, a novel hidden Markov model (HMM) combined with kernel principal component analysis is proposed for nonlinear multimode process fault detection. Firstly, the HMM is built from the measurement data of different modes so as to estimate the dynamic mode sequence. Furthermore, a local KPCA model is developed to detect the fault of each mode. The effectiveness of the proposed method is shown through a numerical nonlinear multimode simulation example and Tennessee Eastman (TE) Chemical benchmark process. The comparison results demonstrate that the proposed HMM-KPCA method precedes the conventional KPCA method due to the high fault detection rate (FDR) and low false alarm rate (FAR).
{"title":"Hidden Markov Model Combined with Kernel Principal Component Analysis for Nonlinear Multimode Process Fault Detection","authors":"Peng Peng, Jiaxin Zhao, Yi Zhang, Heming Zhang","doi":"10.1109/COASE.2019.8843205","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843205","url":null,"abstract":"Data-driven techniques become increasingly popular in the field of industrial fault detection. Regarding the complex nonlinear industrial process accompanied by multiple operational monitoring modes, conventional multivariate monitoring techniques such as kernel principal component analysis (KPCA) are not suitable. In this paper, a novel hidden Markov model (HMM) combined with kernel principal component analysis is proposed for nonlinear multimode process fault detection. Firstly, the HMM is built from the measurement data of different modes so as to estimate the dynamic mode sequence. Furthermore, a local KPCA model is developed to detect the fault of each mode. The effectiveness of the proposed method is shown through a numerical nonlinear multimode simulation example and Tennessee Eastman (TE) Chemical benchmark process. The comparison results demonstrate that the proposed HMM-KPCA method precedes the conventional KPCA method due to the high fault detection rate (FDR) and low false alarm rate (FAR).","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"42 1","pages":"1586-1591"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82517476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8842871
V. Ortenzi, Naresh Marturi, Vijaykumar Rajasekaran, Maxime Adjigble, R. Stolkin
This paper investigates the effect of inverse kinematics (IK) on operator performance during the telemanipulation of an industrial robot. Robotic teleoperation is often preferred when manipulating objects in extreme conditions. In many applications, e.g., hazardous and high-consequence environments, operators cannot directly perceive the robot motions and have to rely only on CCTV views of the scene for situational awareness while teleoperating the heavy-duty industrial robots. Making best guesses for the IK plays a significant role on the task success rate and increases the operator cognitive load significantly. In this context, we develop a new optimisation-based IK solver that is robust with respect to the robot’s singularities and assists the operator in generating smooth trajectories. Inspired by a successful algorithm used in computer graphics to solve the IK problem and devise smooth movements (FABRIK), our algorithm takes advantage also of the kinematic structure of the robot in order to decouple the notoriously difficult IK problem of orientation and position. To evaluate the effectiveness of the proposed method, we have compared its performance to that of the commonly used Jacobian pseudo inverse-based method in terms of positional accuracy and task-space reachability. We also report the results of telemanipulation experiments with human test-subjects. Our proposed IK algorithm outperforms classical IK methods on both objective metrics of task success, and subjective metrics of operator preference.
{"title":"Singularity-Robust Inverse Kinematics Solver for Tele-manipulation","authors":"V. Ortenzi, Naresh Marturi, Vijaykumar Rajasekaran, Maxime Adjigble, R. Stolkin","doi":"10.1109/COASE.2019.8842871","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842871","url":null,"abstract":"This paper investigates the effect of inverse kinematics (IK) on operator performance during the telemanipulation of an industrial robot. Robotic teleoperation is often preferred when manipulating objects in extreme conditions. In many applications, e.g., hazardous and high-consequence environments, operators cannot directly perceive the robot motions and have to rely only on CCTV views of the scene for situational awareness while teleoperating the heavy-duty industrial robots. Making best guesses for the IK plays a significant role on the task success rate and increases the operator cognitive load significantly. In this context, we develop a new optimisation-based IK solver that is robust with respect to the robot’s singularities and assists the operator in generating smooth trajectories. Inspired by a successful algorithm used in computer graphics to solve the IK problem and devise smooth movements (FABRIK), our algorithm takes advantage also of the kinematic structure of the robot in order to decouple the notoriously difficult IK problem of orientation and position. To evaluate the effectiveness of the proposed method, we have compared its performance to that of the commonly used Jacobian pseudo inverse-based method in terms of positional accuracy and task-space reachability. We also report the results of telemanipulation experiments with human test-subjects. Our proposed IK algorithm outperforms classical IK methods on both objective metrics of task success, and subjective metrics of operator preference.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"88 1","pages":"1821-1828"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76389738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8842965
Janindu Arukgoda, Ravindra Ranasinghe, G. Dissanayake
This paper presents a novel method for representing an uncertain occupancy map using a “feature vector” and an associated covariance matrix. Input required is a point cloud generated using observations from a sensor captured at different locations in the environment. Both the sensor locations and the measurements themselves may have an associated uncertainty. The output is a set of coefficients and their uncertainties of a cubic spline approximation to the distance function of the environment, thereby resulting in a compact parametric representation of the environment geometry. Cubic spline coefficients are computed by solving a non-linear least squares problem that enforces the Eikonal equation over the space in which the environment geometry is defined, and zero boundary condition at each observation in the point cloud. It is argued that a feature based representation of point cloud maps acquired from uncertain locations using noisy sensors has the potential to open up a new direction in robot mapping, localisation and SLAM. Numerical examples are presented to illustrate the proposed technique.
{"title":"Representation of Uncertain Occupancy Maps with High Level Feature Vectors","authors":"Janindu Arukgoda, Ravindra Ranasinghe, G. Dissanayake","doi":"10.1109/COASE.2019.8842965","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842965","url":null,"abstract":"This paper presents a novel method for representing an uncertain occupancy map using a “feature vector” and an associated covariance matrix. Input required is a point cloud generated using observations from a sensor captured at different locations in the environment. Both the sensor locations and the measurements themselves may have an associated uncertainty. The output is a set of coefficients and their uncertainties of a cubic spline approximation to the distance function of the environment, thereby resulting in a compact parametric representation of the environment geometry. Cubic spline coefficients are computed by solving a non-linear least squares problem that enforces the Eikonal equation over the space in which the environment geometry is defined, and zero boundary condition at each observation in the point cloud. It is argued that a feature based representation of point cloud maps acquired from uncertain locations using noisy sensors has the potential to open up a new direction in robot mapping, localisation and SLAM. Numerical examples are presented to illustrate the proposed technique.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"20 1","pages":"1035-1041"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78750946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8843291
Zhuo Yang, Yan Lu, H. Yeung, S. Krishnamurty
Consistent melt pool geometry is an indicator of a stable laser powder bed fusion (L-PBF) additive manufacturing process. Melt pool size and shape reflect the impact of process parameters and scanning path on the interaction between the laser and the powder material, the phase change and the flow dynamics of the material during the process. Current L-PBF processes are operated based on predetermined toolpaths and processing parameters and consequently lack the ability to make reactions to unexpected melt pool changes. This paper investigated how melt pool can be characterized in real-time for feedback control. A deep learning-based melt pool classification method is developed to analyze melt pool size both fast and accurately. The classifier, based on a convolutional neural network, was trained with 2763 melt pool images captured from a laser melting powder fusion build using a serpentine scan strategy. The model is validated through 2926 new images collected from a different part in the same build using ‘island’ serpentine strategy with predictive accuracy of 91%. Compared to a traditional image analysis method, the processing time of the validation images is reduced by 90 %, from 9.72 s to 0.99 s, which gives the feedback control a reaction time window of 0.34 ms/image. Results show the feasibility of the proposed method for a real-time closed loop control of L-PBF process.
{"title":"Investigation of Deep Learning for Real-Time Melt Pool Classification in Additive Manufacturing","authors":"Zhuo Yang, Yan Lu, H. Yeung, S. Krishnamurty","doi":"10.1109/COASE.2019.8843291","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843291","url":null,"abstract":"Consistent melt pool geometry is an indicator of a stable laser powder bed fusion (L-PBF) additive manufacturing process. Melt pool size and shape reflect the impact of process parameters and scanning path on the interaction between the laser and the powder material, the phase change and the flow dynamics of the material during the process. Current L-PBF processes are operated based on predetermined toolpaths and processing parameters and consequently lack the ability to make reactions to unexpected melt pool changes. This paper investigated how melt pool can be characterized in real-time for feedback control. A deep learning-based melt pool classification method is developed to analyze melt pool size both fast and accurately. The classifier, based on a convolutional neural network, was trained with 2763 melt pool images captured from a laser melting powder fusion build using a serpentine scan strategy. The model is validated through 2926 new images collected from a different part in the same build using ‘island’ serpentine strategy with predictive accuracy of 91%. Compared to a traditional image analysis method, the processing time of the validation images is reduced by 90 %, from 9.72 s to 0.99 s, which gives the feedback control a reaction time window of 0.34 ms/image. Results show the feasibility of the proposed method for a real-time closed loop control of L-PBF process.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"97 1","pages":"640-647"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76736842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8843092
S. Das, M. Saadatzi, Shamsudeen Abubakar, D. Popa
To detect forces during physical Human-Robot Interaction (pHRI), a force-torque sensor (FTS) is generally attached at the wrist of a robot manipulator. Alternatively, collaborative robots can measure interaction forces via torque sensing at their joints. Yet another direction toward safe and interactive robots is to cover them in smart skins with embedded tactile sensors. In this paper, we explore another idea to facilitate pHRI using an FTS placed at the base of a robot arm. The resulting base force-torque sensor (BFTS) is able to sense external forces and torques applied anywhere along the robot body. We formulate a model-free, on-line learning controller to estimate the interaction forces on the robot from the BFTS data. The controller does not require a robot dynamic model to operate, and has Lyapunov stability guarantees. We conduct experiments to validate the mean-square estimation error of our scheme using a custom 6-DOF robotic arm under real-time control. Results show that the measured torques at individual joints closely follow the estimated values. In the future, this controller can be used for adaptive pHRI with non-collaborative robots or robot manipulators.
{"title":"Joint Torque Estimation using Base Force-Torque Sensor to Facilitate Physical Human-Robot Interaction (pHRI)","authors":"S. Das, M. Saadatzi, Shamsudeen Abubakar, D. Popa","doi":"10.1109/COASE.2019.8843092","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843092","url":null,"abstract":"To detect forces during physical Human-Robot Interaction (pHRI), a force-torque sensor (FTS) is generally attached at the wrist of a robot manipulator. Alternatively, collaborative robots can measure interaction forces via torque sensing at their joints. Yet another direction toward safe and interactive robots is to cover them in smart skins with embedded tactile sensors. In this paper, we explore another idea to facilitate pHRI using an FTS placed at the base of a robot arm. The resulting base force-torque sensor (BFTS) is able to sense external forces and torques applied anywhere along the robot body. We formulate a model-free, on-line learning controller to estimate the interaction forces on the robot from the BFTS data. The controller does not require a robot dynamic model to operate, and has Lyapunov stability guarantees. We conduct experiments to validate the mean-square estimation error of our scheme using a custom 6-DOF robotic arm under real-time control. Results show that the measured torques at individual joints closely follow the estimated values. In the future, this controller can be used for adaptive pHRI with non-collaborative robots or robot manipulators.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"75 5 1","pages":"1367-1372"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77311281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8842864
Félicien Barhebwa-Mushamuka, S. Dauzére-Pérés, C. Yugma
This paper presents a multi-objective optimization approach for global fab scheduling, based on a mathematical model that determines production targets, i.e. product quantities to complete in each operation and each period on a scheduling horizon. The multi-objective approach balances product mix variability minimization and throughput maximization using an $epsilon$-constraint approach. For evaluation purposes, the global fab scheduling model is coupled with a generic multi-method simulation model. Numerical experiments conducted on industrial data illustrate the effectiveness of the approach.
{"title":"Multi-objective optimization for Work-In-Process balancing and throughput maximization in global fab scheduling","authors":"Félicien Barhebwa-Mushamuka, S. Dauzére-Pérés, C. Yugma","doi":"10.1109/COASE.2019.8842864","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842864","url":null,"abstract":"This paper presents a multi-objective optimization approach for global fab scheduling, based on a mathematical model that determines production targets, i.e. product quantities to complete in each operation and each period on a scheduling horizon. The multi-objective approach balances product mix variability minimization and throughput maximization using an $epsilon$-constraint approach. For evaluation purposes, the global fab scheduling model is coupled with a generic multi-method simulation model. Numerical experiments conducted on industrial data illustrate the effectiveness of the approach.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"301 1","pages":"697-702"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74963274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8842863
Amir Zakerimanesh, Ali Reza Torabi, F. Hashemzadeh, M. Tavakoli
This note presents a novel approach for task-space tracking control of redundant manipulators with bounded actuation. Inspired by the leader-follower containment problem in multi-agent systems, the proposed controller is utilized to address the containment control of a single follower manipulator led by multiple manipulators. In the controller design, the redundancy of the robots is exploited for achieving sub-task control such as singularity avoidance, and joint limit avoidance. The asymptotic stability condition for the closed-loop dynamics is obtained using Lyapunov functional. For the containment, the proposed controller makes sure that the leaders track their desired positions and the follower robot’s end-effector asymptotically converges to the convex hull formed by the leaders’ traversed trajectories. The efficiency of the proposed control algorithm is verified through numerical simulations and experimental results.
{"title":"Task-Space Position and Containment Control of Redundant Manipulators with Bounded Inputs","authors":"Amir Zakerimanesh, Ali Reza Torabi, F. Hashemzadeh, M. Tavakoli","doi":"10.1109/COASE.2019.8842863","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842863","url":null,"abstract":"This note presents a novel approach for task-space tracking control of redundant manipulators with bounded actuation. Inspired by the leader-follower containment problem in multi-agent systems, the proposed controller is utilized to address the containment control of a single follower manipulator led by multiple manipulators. In the controller design, the redundancy of the robots is exploited for achieving sub-task control such as singularity avoidance, and joint limit avoidance. The asymptotic stability condition for the closed-loop dynamics is obtained using Lyapunov functional. For the containment, the proposed controller makes sure that the leaders track their desired positions and the follower robot’s end-effector asymptotically converges to the convex hull formed by the leaders’ traversed trajectories. The efficiency of the proposed control algorithm is verified through numerical simulations and experimental results.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"4 1","pages":"431-436"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73236961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8842982
Z. Zabinsky, Ting-Yu Ho, Hao Huang
Algorithms for solving large-scale optimization problems often use heuristics and approximations to achieve a solution quickly, however there is often little or no information as to the quality of the solution. We integrate heuristics and approximations into a branch and bound framework to take advantage of obtaining a solution quickly, while using the framework to prune regions that do not contain an optimal solution, and provide an optimality gap. Three examples are cast into this framework. First, we describe a Rollout Algorithm with Branch-and-Bound (RA-BnB) that embeds an approximate dynamic program into a branch and bound framework to address a challenging resource allocation problem in population disease management. Second, we describe a Vehicle Routing and Scheduling Algorithm (VeRSA) that embeds an easily calculated index, as is commonly used in scheduling, to dynamically search and prune a branch and bound tree. Third, we describe a Probabilistic Branch and Bound algorithm (PBnB) that uses a statistical sampling method to obtain confidence interval bounds that are embedded into a tree to probabilistically prune regions of the tree. These three, apparently different, methods share commonalities that make use of heuristics and approximations to generate a “near-optimal” solution quickly, and also provide information on the quality of the solution by providing an optimality gap. Lessons learned on implementation decisions and how to balance computation in the context of these three problems are discussed.
{"title":"Integrating Heuristics and Approximations into a Branch and Bound Framework*","authors":"Z. Zabinsky, Ting-Yu Ho, Hao Huang","doi":"10.1109/COASE.2019.8842982","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842982","url":null,"abstract":"Algorithms for solving large-scale optimization problems often use heuristics and approximations to achieve a solution quickly, however there is often little or no information as to the quality of the solution. We integrate heuristics and approximations into a branch and bound framework to take advantage of obtaining a solution quickly, while using the framework to prune regions that do not contain an optimal solution, and provide an optimality gap. Three examples are cast into this framework. First, we describe a Rollout Algorithm with Branch-and-Bound (RA-BnB) that embeds an approximate dynamic program into a branch and bound framework to address a challenging resource allocation problem in population disease management. Second, we describe a Vehicle Routing and Scheduling Algorithm (VeRSA) that embeds an easily calculated index, as is commonly used in scheduling, to dynamically search and prune a branch and bound tree. Third, we describe a Probabilistic Branch and Bound algorithm (PBnB) that uses a statistical sampling method to obtain confidence interval bounds that are embedded into a tree to probabilistically prune regions of the tree. These three, apparently different, methods share commonalities that make use of heuristics and approximations to generate a “near-optimal” solution quickly, and also provide information on the quality of the solution by providing an optimality gap. Lessons learned on implementation decisions and how to balance computation in the context of these three problems are discussed.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"8 1","pages":"774-779"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75895179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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