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Automation Science and Engineering, IEEE Transactions on

Issue 4 • Date Oct. 2011

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  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Automation Science and Engineering publication information

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  • An Integrated Pricing and Deteriorating Model and a Hybrid Algorithm for a VMI (Vendor-Managed-Inventory) Supply Chain

    Page(s): 673 - 682
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (679 KB) |  | HTML iconHTML  

    This paper studies a vendor-managed-inventory (VMI) supply chain where a manufacturer, as a vendor, procures a type of nondeteriorating raw material to produce a deteriorating product, and distribute it to multiple retailers. The price of the product offered by one retailer is also influenced by the prices offered by other retailers because consumers can choose the product from any of the retailers. This paper is one of the first papers that propose an integrated model to study the influence of pricing and deterioration on the profit of such a VMI system. A hybrid approach combining genetic algorithms and an analytical method is developed for efficiently determining the optimal price of the product of each retailer, the inventory policies of the product and the raw material. Our results of a detailed numerical study show that parameters related to the market and deterioration have significant influences on the profit of the VMI system. However, different from common intuition, we find that an increase in the substitution elasticity of the product among different retailers can bring an increase in the retail prices of the product, while the increase of the market scale can reduce the retail prices. View full abstract»

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  • Optimization Based Method for Supply Location Selection and Routing in Large-Scale Emergency Material Delivery

    Page(s): 683 - 693
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (560 KB) |  | HTML iconHTML  

    Timely supply of vital materials to disaster hit areas plays a critical role in emergency relief. The problem involves warehouse selection, fleet routing, and scheduling so as to meet demand in the strict time window. The problem is NP-hard, in general, and extremely difficult to solve. The congestion caused by heavy traffic further aggravates the problem. To obtain a scalable solution, a new method based on successive subproblem solving in Lagrangian Relaxation (LR) framework is developed. The route capacity and location selection constraints are relaxed by Lagrange multipliers, and the problem is converted into a two-level optimization problem. The subproblems at the lower level are solved successively in dual iterations with convergence assurance so that the indecomposable location constraints can be incorporated. A systematic method is developed to obtain a feasible solution by adding the once relaxed constraints back into the dual problem successively in feasibility iterations. Convergence proof of the new method and its properties are presented. Numerical results show that the new method is effective and efficient, and can be applied to large-scale problems. View full abstract»

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  • The Linehaul-Feeder Vehicle Routing Problem With Virtual Depots

    Page(s): 694 - 704
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (896 KB) |  | HTML iconHTML  

    The problem addressed in this paper-the linehaul-feeder vehicle routing problem with virtual depots (LFVRP-VD)-can be regarded as an extension of the vehicle routing problem. During delivery operation, one large vehicle departs from the physical depot (PD) and services all virtual depots (VDs). A set of small vehicles delivers to customers and, if necessary, reloads either from the PD or from the large vehicle at a VD before continuing work. The objective of the operation is to minimize the total travel and waiting costs for all vehicles. Two heuristics that embed the cost-sharing method and the threshold method are proposed for initial solution construction. Seventeen test problems are extensively examined. The results show that the cost-sharing method outperforms the threshold method in terms of several selected performance measures. In addition, the more candidates available a VD can choose, the better the obtained objective value. View full abstract»

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  • Self Assessment-Based Decision Making for Multiagent Cooperative Search

    Page(s): 705 - 719
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (902 KB) |  | HTML iconHTML  

    This paper addresses a search problem with multiple limited capability search agents in a partially connected dynamical networked environment under different information structures. A self assessment-based decision-making scheme for multiple agents is proposed that uses a modified negotiation scheme with low communication overheads. The scheme has attractive features of fast decision-making and scalability to large number of agents without increasing the complexity of the algorithm. Two models of the self assessment schemes are developed to study the effect of increase in information exchange during decision-making. Some analytical results on the maximum number of self assessment cycles, effect of increasing communication range, completeness of the algorithm, lower bound and upper bound on the search time are also obtained. The performance of the various self assessment schemes in terms of total uncertainty reduction in the search region, using different information structures is studied. It is shown that the communication requirement for self assessment scheme is almost half of the negotiation schemes and its performance is close to the optimal solution. Comparisons with different sequential search schemes are also carried out. View full abstract»

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  • An Adaptive Sampling Algorithm for Simulation-Based Optimization With Descriptive Complexity Preference

    Page(s): 720 - 731
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (813 KB) |  | HTML iconHTML  

    Many systems nowadays follow not only physical laws but also manmade rules. These systems are known as discrete-event dynamic systems (DEDSs), where simulation is the only faithful way for performance evaluation. Due to various advantages in practice, designs (or solution candidates) with low descriptive complexity (called simple designs) are usually preferred over complex ones when their performances are close. However, the descriptive complexity (DC) is usually nonlinear and takes discrete value, which makes traditional methods such as linear programming and gradient-based local search not applicable. Existing methods for simulation-based optimization (SBO) do not explore the preference on descriptive complexity and thus cannot solve the problem efficiently. The major contributions of this paper are to point out the importance of considering SBO problems with DC preference, and to develop an adaptive sampling algorithm (ASA) to find the simplest good design. It is shown that ASA terminates within finite iterations and with controllable probability of making mistake. The computational complexity of ASA and its dependence on various parameters are discussed. ASA is then applied to three parameter optimization problems and a node activation policy optimization problem in a wireless sensor network. Numerical results show that ASA is more efficient than blind picking and Levin search in most cases. We hope this work can shed some insight to how to find simple and good designs in general. View full abstract»

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  • Allocating Resources in Multiagent Flowshops With Adaptive Auctions

    Page(s): 732 - 743
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1228 KB) |  | HTML iconHTML  

    In this paper, we consider the problem of allocating machine resources among multiple agents, each of which is responsible to solve a flowshop scheduling problem. We present an iterated combinatorial auction mechanism in which bid generation is performed within each agent, while a price adjustment procedure is performed by a centralized auctioneer. While this approach is fairly well-studied in the literature, our primary innovation is in an adaptive price adjustment procedure, utilizing variable step-size inspired by adaptive PID-control theory coupled with utility pricing inspired by classical microeconomics. We compare with the conventional price adjustment scheme proposed in Fisher (1985), and show better convergence properties. Our secondary contribution is in a fast bid-generation procedure executed by the agents based on local search. Putting both these innovations together, we compare our approach against a classical integer programming model as well as conventional price adjustment schemes, and show drastic run time improvement with insignificant loss of global optimality. View full abstract»

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  • Nonparametric and Semi-Parametric Sensor Recovery in Multichannel Condition Monitoring Systems

    Page(s): 744 - 753
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1250 KB) |  | HTML iconHTML  

    Condition monitoring (CM) has been recognized as a more effective failure prevention paradigm than the time-based counterpart. CM can be performed via an array of sensors providing multiple, real-time equipment degradation information with broad coverage. However, loss of sensor readings due to sensor abnormalities and/or malfunction of connectors has long been a hurdle to reliable fault diagnosis and prognosis in multichannel CM systems. The problem becomes more challenging when the sensor channels are not synchronized because of different sampling rates used and/or time-varying operational schemes. This paper provides a nonparametric sensor recovery technique and a semi-parametric alternative to enhance the robustness of multichannel CM systems. Based on historical data, models for all the sensor signals are constructed using functional principal component analysis (FPCA), and functional regression (FR) models are developed for those correlated signals. These models with parameters updated in online implementation can be used to recover the lost sensor signals. A case study of aircraft engines is used to demonstrate the capability of the proposed approaches. In addition to recovering asynchronous sensor signals, the proposed approaches are also compared with the Elman neural network as a popular alternative in recovering synchronous sensor signals. View full abstract»

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  • Symbolic Computation of Reduced Guards in Supervisory Control

    Page(s): 754 - 765
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (676 KB) |  | HTML iconHTML  

    In the supervisory control theory, a supervisor is generated based on given plant and specification models. The supervisor restricts the plant in order to fulfill the specifications. A problem that is typically encountered in industrial applications is that the resulting supervisor is not easily comprehensible for the users. To tackle this problem, we introduce an efficient method to characterize a supervisor by tractable logic conditions, referred to as guards, generated from the models. The guards express under which conditions an event is allowed to occur to fulfill the specifications. To obtain tractable guard expressions, we reduce them by exploiting the structure of the given models. In order to be able to handle complex systems efficiently, the models are symbolically represented by binary decision diagrams and all computations are performed on these data structures. The algorithms have been implemented in a supervisory control tool and applied to an industrially relevant example. View full abstract»

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  • A Practical Approach for Maximally Permissive Liveness-Enforcing Supervision of Complex Resource Allocation Systems

    Page(s): 766 - 779
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (762 KB) |  | HTML iconHTML  

    Past works towards the effective deployment of the maximally permissive liveness-enforcing supervisor (LES) for sequential resource allocation systems (RAS) have been stalled by: (i) the NP-Hardness of the computation of this policy for the majority of the considered RAS classes and (ii) the inability of the adopted more compact representations of the underlying RAS dynamics to provide an effective representation of the target policy for all RAS instantiations. This paper proposes a novel approach to the aforementioned problem, that can be perceived as a two-stage process: The first stage computes the maximally permissive LES by employing an automaton-based representation of the RAS behavior and techniques borrowed from the Ramadge & Wonham (R&W) Supervisory Control framework. The second stage seeks the development of a more compact representation for the dichotomy into admissible and inadmissible-or “safe” and “unsafe”-subspaces of the RAS state space, that is effected by the LES developed in the first stage. This compact representation is obtained by: (i) taking advantage of certain properties of the underlying subspaces and (ii) the employment of pertinent data structures. The resulting approach is also “complete,” i.e., it will return an effectively implementable LES for any given RAS instantiation. Numerical experimentation demonstrates the efficacy of the approach and establishes its ability to support the deployment of maximally permissive LES for RAS with very large structure and state spaces. View full abstract»

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  • Stochastic Algorithms for Discrete Parameter Simulation Optimization

    Page(s): 780 - 793
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB) |  | HTML iconHTML  

    We present two efficient discrete parameter simulation optimization (DPSO) algorithms for the long-run average cost objective. One of these algorithms uses the smoothed functional approximation (SFA) procedure, while the other is based on simultaneous perturbation stochastic approximation (SPSA). The use of SFA for DPSO had not been proposed previously in the literature. Further, both algorithms adopt an interesting technique of random projections that we present here for the first time. We give a proof of convergence of our algorithms. Next, we present detailed numerical experiments on a problem of admission control with dependent service times. We consider two different settings involving parameter sets that have moderate and large sizes, respectively. On the first setting, we also show performance comparisons with the well-studied optimal computing budget allocation (OCBA) algorithm and also the equal allocation algorithm. View full abstract»

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  • Supervisor Optimization for Deadlock Resolution in Automated Manufacturing Systems With Petri Nets

    Page(s): 794 - 804
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (622 KB) |  | HTML iconHTML  

    For automated manufacturing systems (AMSs), deadlock resolution in terms of Petri nets remains an attractive topic to which many approaches are dedicated. However, few of them can quantitatively optimize certain indices during their supervisor synthesis process. This causes unnecessary control limitations and often leads to high implementation cost. In the framework of Petri nets, this paper proposes a method to synthesize a cost-effective supervisor with the aid of a set of mathematical programming formulations. Along the same vein, we also show some results by investigating timed Petri nets, which can be utilized to make a good tradeoff between implementation cost and system cycle time. Examples are used to validate the effectiveness of our result. View full abstract»

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  • Bending-Invariant Correspondence Matching on 3-D Human Bodies for Feature Point Extraction

    Page(s): 805 - 814
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1757 KB) |  | HTML iconHTML  

    In this paper, we present an automatic approach to match correspondences on 3-D human bodies in various postures so that feature points can be automatically extracted. The feature points are very important to the establishment of volumetric parameterization around human bodies for the human-centered customization of soft-products (Trans. Autom. Sci. Eng., vol. 4, issue no. 1, pp. 11-21, 2007). For a given template human model with a set of predefined feature points, we first down-sample the input model into a set of sample points. Then, the corresponding points of these samples on the human model are identified by minimizing the distortion with the help of a series of transformations regardless of their differences in postures, scales or positions. The basic idea of our algorithm is to transform the template human body to the shape of the input model iteratively. To generate a bending invariant mapping, the initial correspondence/transformation is computed in a multidimensional scaling (MDS) embedding domain of 3-D human models, where the Euclidean distance between two samples on a 3-D model in the MDS domain corresponds to the geodesic distance between them in ℜ3 . As the posture change (i.e., the body bending) of a human model can be considered as approximately isometric in the intrinsic 3-D shape, the initial correspondences established in the MDS domain can greatly enhance the robustness of our approach in body bending. Once the correspondences between the surface samples on the template model and the input model are determined after iterative transformations, we have essentially found the corresponding feature points on the input model. Finally, the locations of the based local matching step. View full abstract»

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  • Optimal Image-Based Euclidean Calibration of Structured Light Systems in General Scenes

    Page(s): 815 - 823
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (580 KB) |  | HTML iconHTML  

    This paper presents a method to perform Euclidean calibration on camera and projector-based structured light systems, without assuming specific scene structure. The vast majority of the methods in the literature rely on prior knowledge of the 3D scene geometry in order to perform calibration, i.e., the nature of the occluding bodies in the scene needs to be known beforehand in order to calibrate structured light systems. Examples of prior knowledge used include using known stationary occluding bodies, precisely maneuvering known occluding bodies, knowing the exact world location of projected points or lines, or ensuring the entire scene obeys some other specific setup. By using multiple cameras, the method presented in this paper is able to calibrate camera and projector systems without requiring any of these constraints on occluding bodies in the scene. The method presented optimizes the calibration of the scene in terms of image-based reprojection error. Simulations are shown which characterize the effect noise has on the system, and experimental verification is performed on complex and cluttered scenes. The main contribution of this paper is the elimination of the requirement of using known occluding bodies in the scene for camera and projector-based structured light system calibration, which has not been extensively studied. View full abstract»

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  • Development and Force/Position Control of a New Hybrid Thermo-Piezoelectric MicroGripper Dedicated to Micromanipulation Tasks

    Page(s): 824 - 834
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1546 KB) |  | HTML iconHTML  

    A new microgripper dedicated to micromanipulation and microassembly tasks is presented in this paper. Based on a new actuator, called thermo-piezoelectric actuator, the microgripper presents both a high range and a high positioning resolution. The principle of the microgripper is based on the combination of the thermal actuation (for the coarse positioning) and the piezoelectric actuation (for the fine positioning). In order to improve the performances of the microgripper, its actuators are modeled and a control law for both the position and the manipulation force is synthesized afterwards. A new control scheme adapted for the actuators of the hybrid thermo-piezoelectric microgripper is therefore proposed. To prove the interest of the developed microgripper and of the proposed control scheme, the control of a pick-and-release task using this microgripper is carried out. The experimental results confirm their efficiency and demonstrate that the new microgripper and the control law are well suited for micromanipulation and microassembly applications. View full abstract»

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  • Model-Assisted Stochastic Learning for Robotic Applications

    Page(s): 835 - 845
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1176 KB) |  | HTML iconHTML  

    We present here a framework for the generation, application, and assessment of assistive models for the purpose of aiding automated robotic parameter optimization methods. Our approach represents an expansion of traditional machine learning implementations by employing models to predict the performances of input parameter sequences and then filter a potential population of inputs prior to evaluation on a physical system. We further provide a basis for numerically qualifying these models to determine whether or not they are of sufficient quality to be capable of fulfilling their predictive responsibilities. We demonstrate the effectiveness of this approach using an industrial robotic testbed on a variety of mechanical assemblies, each requiring a different strategy for completion. View full abstract»

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  • Linear Programming SVM-ARMA _{\rm 2K} With Application in Engine System Identification

    Page(s): 846 - 854
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (537 KB) |  | HTML iconHTML  

    As an emerging non-parametric modeling technique, the methodology of support vector regression blazed a new trail in identifying complex nonlinear systems with superior generalization capability and sparsity. Nevertheless, the conventional quadratic programming support vector regression can easily lead to representation redundancy and expensive computational cost. In this paper, by using the l1 norm minimization and taking account of the different characteristics of autoregression (AR) and the moving average (MA), an innovative nonlinear dynamical system identification approach, linear programming SVM-ARMA2K, is developed to enhance flexibility and secure model sparsity in identifying nonlinear dynamical systems. To demonstrate the potential and practicality of the proposed approach, the proposed strategy is applied to identify a representative dynamical engine model. View full abstract»

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  • Optimization of Train Regulation and Energy Usage of Metro Lines Using an Adaptive-Optimal-Control Algorithm

    Page(s): 855 - 864
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1003 KB) |  | HTML iconHTML  

    Automatic train regulation (ATR) dominates the service quality, transport capacity, and energy usage of a metro-line operation. The train regulator aims to maximize the schedule/headway adherence while minimizing the energy consumption. This paper presents a traffic-energy model to characterize the complicated dynamics with regard to the traffic and the energy consumption of a metro line, and devises an adaptive-optimal-control (AOC) algorithm to optimize the train regulator through reinforcement learning. The updating rules for reinforcement learning are deduced from the discrete minimum principle. Testing with field traffic data, the AOC algorithm succeeds in the optimization of the train regulator; no matter the system is disturbed by passenger-flow fluctuations or by frequently minor delays. The results also show that better train regulation with less energy consumption is attainable through the running-time and dwell-time controls. View full abstract»

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  • On the Flexible Demand Assignment Problems: Case of Unmanned Aerial Vehicles

    Page(s): 865 - 868
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (112 KB) |  | HTML iconHTML  

    Multiresource generalized assignment problem (MRGAP) has enormous applications in solving real problems of industries. In recent years, several generalizations of MRGAP have been proposed to tackle very difficult problems. An important generalization is called flexible demand assignment (FDA) problem. In this paper, a generalization of FDA is proposed that has many applications. Two features of our formulation are inclusion of: 1) acceptance of orders from a large set of available orders and 2) consideration of setup time between operations of two consecutive of tasks. We show an interesting application of generalized FDA is unmanned aerial vehicle (UAV) assignment problem. For the UAV assignment problem, we show our formulation considerably reduces the size of the problem compared to some recent results. To test effectiveness of the proposed model, computational experiment with CPLEX for the UAV assignment problem is presented. View full abstract»

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  • T-ASE Reviewers for 2010/2011

    Page(s): 869 - 873
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  • Why we joined ... [advertisement]

    Page(s): 874
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  • 2011 Index IEEE Transactions on Automation Science and Engineering Vol. 8

    Page(s): 875 - 888
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  • IEEE Robotics and Automation Society Information

    Page(s): C3
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  • IEEE Transactions on Automation Science and Engineering Information for authors

    Page(s): C4
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T-ASE will publish foundational research on Automation: scientific methods and technologies that improve efficiency, productivity, quality, and reliability, specifically for methods, machines, and systems operating in structured environments over long periods, and the explicit structuring of environments.

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Ken Goldberg
University of California, Berkeley