AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

磁性流体を用いたバックドライブ可能な油圧アクチュエータの開発

早大学位記番号:新7478早稲田大

Sensors Data Analysis in Supervisory Control and Data Acquisition (SCADA) Systems to Foresee Failures with an Undetermined Origin

This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.This work was supported partially by the Basque Government through project IT1207-19, and by the MCIU/MINECO through RTI2018-094902-B-C21/RTI2018-094902-B-C22 (MCIU/AEI/FEDER, UE). The authors would like to thank Intenance Company for its collaboration and help

Flight crew aiding for recovery from subsystem failures

Some of the conceptual issues associated with pilot aiding systems are discussed and an implementation of one component of such an aiding system is described. It is essential that the format and content of the information the aiding system presents to the crew be compatible with the crew's mental models of the task. It is proposed that in order to cooperate effectively, both the aiding system and the flight crew should have consistent information processing models, especially at the point of interface. A general information processing strategy, developed by Rasmussen, was selected to serve as the bridge between the human and aiding system's information processes. The development and implementation of a model-based situation assessment and response generation system for commercial transport aircraft are described. The current implementation is a prototype which concentrates on engine and control surface failure situations and consequent flight emergencies. The aiding system, termed Recovery Recommendation System (RECORS), uses a causal model of the relevant subset of the flight domain to simulate the effects of these failures and to generate appropriate responses, given the current aircraft state and the constraints of the current flight phase. Since detailed information about the aircraft state may not always be available, the model represents the domain at varying levels of abstraction and uses the less detailed abstraction levels to make inferences when exact information is not available. The structure of this model is described in detail

Improving the skills of forest harvester operators

Forestry suffers from a shortage of trained machine operators, which jeopardises efficient and productive operations. Extensive training is required to skilfully master the complex tasks of operators of forest harvesters and forest forwarders. Therefore, the digitisation of the industry envisages training and support systems on machines that provide real-time support to operators, both on-site and remotely. The aim of this thesis was to improve training methods and pave the way for the development of future operator support systems, therefore a detailed analysis of harvester operators' work tasks, focussing on motor control skills and cognitive (work)load, was conducted. The work was guided by the following two general research questions, which were systematically answered throughout the studies presented in this thesis. (1) How can training methods for robotic arm operators be improved by analysing performance limiting factors in the bimanual control of the robotic cranes and (2) How can the machine operators be effectively supported with different sensorimotor support systems to ensure high level performance? To this end, a multi-pronged approach using qualitative and quantitative methods was adopted and five scientific studies were carried out. For three quantitative laboratory studies, a multi-joint robotic manipulator was designed and programmed as a simulation environment, which in its basic layout resembles the crane of real forestry machines. To identify the challenges in learning the motor control of such robotic cranes, this work focussed on the joystick control of the individual joints (joint control) or the movement of the tip (end-effector) of the robotic crane. Two experimental studies on the acquisition of operating skills with the two different control schemes, showed that in spite of a gain in mental workload reduction with end-effector control, movement accuracy remains difficult with both control schemes. This refers with joint control to the challenging use of the joints involved in the fine control of the robotic crane and with end-effector control to a general lack of accuracy. In a third study, visual and auditory (sonification) support systems were implemented in the simulation environment and compared for increasing accuracy. Auditory support systems showed higher effectiveness, which depends on initial operator performance level. In summary, this thesis has shown that behavioural analysis at the level of joystick movements and the analysis of crane movements can be very fruitful for studying the development of human control skills and deriving new performance indicators that can be used in operator training and the design of different operator support systems. The development of machines with increasing technical operator support will potentially lead to new challenges in real-world operation, where the management of cognitive workload and the detrimental effects, specifically of cognitive underload conditions, will require a rethinking and design of the operators’ work

Advanced Control Strategies for Mobile Hydraulic Applications

Mobile hydraulic machines are affected by numerous undesired dynamics, mainly discontinuous motion and vibrations. Over the years, many methods have been developed to limit the extent of those undesired dynamics and improve controllability and safety of operation of the machine. However, in most of the cases, today\u27s methods do not significantly differ from those developed in a time when electronic controllers were slower and less reliable than they are today. This dissertation addresses this aspect and presents a unique control method designed to be applicable to all mobile hydraulic machines controlled by proportional directional valves. In particular, the proposed control method is targeted to hydraulic machines such as those used in the field including construction (wheel loaders, excavators, and backhoes, etc.), load handling (cranes, reach-stackers, and aerial lift, etc.), agriculture (harvesters, etc.), forestry, and aerospace. For these applications the proposed control method is designed to achieve the following goals: A. Improvement of the machine dynamics by reducing mechanical vibrations of mechanical arms, load, as well as operator seat; B. Reduction of the energy dissipation introduced by current vibration damping methods; C. Reduction of system slowdowns introduced by current vibration damping methods. Goal A is generally intended for all machines; goal B refers to those applications in which the damping is introduced by means of energy losses on the main hydraulic transmission line; goal C is related to those applications in which the vibration attenuation is introduced by slowing down the main transmission line dynamics. Two case studies are discussed in this work: 1. Hydraulic crane: the focus is on the vibrations of the mechanical arms and load (goals A and B). 2. Wheel loader: the focus is on the vibrations of the driver\u27s seat and bucket (goals A and C). The controller structure is basically unvaried for different machines. However, what differs in each application are the controller parameters, whose adaptation and tuning method represent the main innovations of this work. The proposed controller structure is organized so that the control parameters are adapted with respect to the instantaneous operating point which is identified by means of feedback sensors. The Gain Scheduling technique is used to implement the controller whose set of parameters are function of the specific identified operating point. The optimal set of control parameters for each operating point is determined through the non-model-based controller tuning. The technique determines the optimal set of controller parameters through the optimization of the experimental machine dynamics. The optimization is based on an innovative application of the Extremum Seeking algorithm. The optimal controller parameters are then indexed into the Gain Scheduler. The proposed method does not require the modification of the standard valve controlled machine layout since it only needs for the addition of feedback sensors. The feedback signals are used by the control unit to modify the electric currents to the proportional directional valves and cancel the undesired dynamics of the machine by controlling the actuator motion. In order for the proposed method to be effective, the proportional valve bandwidth must be significantly higher than the frequency of the undesired dynamics. This condition, which is typically true for heavy machineries, is further investigated in the research. The research mostly focuses on the use of pressure feedback. In fact, although the use of position, velocity, or acceleration sensors on the vibrating bodies of the machine would provide a more straightforward measurement of the vibration, they are extremely rare on mobile hydraulic machines where mechanical and environmental stress harm their integrity. A comparison between pressure feedback and acceleration feedback alternatives of the proposed method is investigated with the aim to outline the conditions making one alternative preferable over the other one (for those applications were both alternatives are technically viable in terms of sensors and wiring reliability). A mid-sized hydraulic crane (case study 1) was instrumented at Maha Fluid Power Research Center to study the effectiveness of the proposed control method, its stability and its experimental validation. Up to 30% vibration damping and 40% energy savings were observed for a specific cycle over the standard vibration damping method for this application. The proposed control method was also applied to a wheel loader (case study 2), and up to 70% vibrations attenuation on the bucket and 30% on the driver\u27s cab were found in simulations. These results also served to demonstrate the applicability of the control method to different hydraulic machines. Improved system response and a straightforward controller parameters tuning methodology are the features which give to the proposed method the potential to become a widespread technology for fluid power machines. The proposed method also has potential for improving several current vibration damping methods in terms of energy efficiency as well as simplification of both the hydraulic system layout and tuning process

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Topological analysis of powertrains for refusecollecting vehicles based on real routes – Part I: Hybrid hydraulic powertrain

In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on the simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this first part, a characterization of a standard route is performed, analyzing the average power consumption and the most frequent working points of an internal combustion engine (ICE) in real routes. This information is used to define alternative powertrain architectures. A hybrid hydraulic powertrain architecture is proposed and modelled. The proposed powertrain model is executed using two different control algorithms, with and without predictive strategies, with data obtained from real routes. A calculation engine (an algorithm which runs the vehicle models on real routes), is presented and used for simulations. This calculation engine has been specifically designed to analyze if the different alternative powertrain delivers the same performance of the original ICE. Finally, the overall performance of the different architectures and control strategies are summarized into a fuel and energy consumption table, which will be used in the second part of this paper to compare with the different architectures based on hybrid electric powertrain. The overall performance of the different architectures indicates that the use of a hybrid hydraulic powertrain with simple control laws can reduce the fuel consumption up to a 14 %.Postprint (author's final draft

Enabling Resilience in Cyber-Physical-Human Water Infrastructures

Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments