3,313 research outputs found
Application of automatic vehicle location in law enforcement: An introductory planning guide
A set of planning guidelines for the application of automatic vehicle location (AVL) to law enforcement is presented. Some essential characteristics and applications of AVL are outlined; systems in the operational or planning phases are discussed. Requirements analysis, system concept design, implementation planning, and performance and cost modeling are described and demonstrated with numerous examples. A detailed description of a typical law enforcement AVL system, and a list of vendor sources are given in appendixes
Exploring the drive-by sensing power of bus fleet through active scheduling
Vehicle-based mobile sensing (a.k.a drive-by sensing) is an important means
of surveying urban environment by leveraging the mobility of public or private
transport vehicles. Buses, for their extensive spatial coverage and reliable
operations, have received much attention in drive-by sensing. Existing studies
have focused on the assignment of sensors to a set of lines or buses with no
operational intervention, which is typically formulated as set covering or
subset selection problems. This paper aims to boost the sensing power of bus
fleets through active scheduling, by allowing instrumented buses to circulate
across multiple lines to deliver optimal sensing outcome. We consider a fleet
consisting of instrumented and normal buses, and jointly optimize sensor
assignment, bus dispatch, and intra- or inter-line relocations, with the
objectives of maximizing sensing quality and minimizing operational costs,
while serving all timetabled trips. By making general assumptions on the
sensing utility function, we formulate the problem as a nonlinear integer
program based on a time-expanded network. A batch scheduling algorithm is
developed following linearization techniques to solve the problem efficiently,
which is tested in a real-world case study in Chengdu, China. The results show
that the proposed scheme can improve the sensing objective by 12.0%-20.5%
(single-line scheduling) and 16.3%-32.1% (multi-line scheduling), respectively,
while managing to save operational costs by 1.0%. Importantly, to achieve the
same level of sensing quality, we found that the sensor investment can be
reduced by over 33% when considering active bus scheduling. Comprehensive
comparative and sensitivity analyses are presented to generate managerial
insights and recommendations for practice.Comment: 32 pages, 13 figures, 8 table
Simulation in Automated Guided Vehicle System Design
The intense global competition that manufacturing companies face today results in an
increase of product variety and shorter product life cycles. One response to this threat is
agile manufacturing concepts. This requires materials handling systems that are agile
and capable of reconfiguration. As competition in the world marketplace becomes
increasingly customer-driven, manufacturing environments must be highly
reconfigurable and responsive to accommodate product and process changes, with rigid,
static automation systems giving way to more flexible types.
Automated Guided Vehicle Systems (AGVS) have such capabilities and AGV
functionality has been developed to improve flexibility and diminish the traditional
disadvantages of AGV-systems. The AGV-system design is however a multi-faceted
problem with a large number of design factors of which many are correlating and
interdependent. Available methods and techniques exhibit problems in supporting the
whole design process. A research review of the work reported on AGVS development in
combination with simulation revealed that of 39 papers only four were industrially
related. Most work was on the conceptual design phase, but little has been reported on
the detailed simulation of AGVS.
Semi-autonomous vehicles (SA V) are an innovative concept to overcome the problems
of inflexible -systems and to improve materials handling functionality. The SA V
concept introduces a higher degree of autonomy in industrial AGV -systems with the
man-in-the-Ioop. The introduction of autonomy in industrial applications is approached
by explicitly controlling the level of autonomy at different occasions. The SA V s are
easy to program and easily reconfigurable regarding navigation systems and material
handling equipment. Novel approaches to materials handling like the SA V -concept
place new requirements on the AGVS development and the use of simulation as a part
of the process. Traditional AGV -system simulation approaches do not fully meet these
requirements and the improved functionality of AGVs is not used to its full power.
There is a considerflble potential in shortening the AGV -system design-cycle, and thus
the manufacturing system design-cycle, and still achieve more accurate solutions well
suited for MRS tasks.
Recent developments in simulation tools for manufacturing have improved production
engineering development and the tools are being adopted more widely in industry. For
the development of AGV -systems this has not fully been exploited. Previous research
has focused on the conceptual part of the design process and many simulation
approaches to AGV -system design lack in validity. In this thesis a methodology is
proposed for the structured development of AGV -systems using simulation. Elements of
this methodology address the development of novel functionality.
The objective of the first research case of this research study was to identify factors for
industrial AGV -system simulation. The second research case focuses on simulation in
the design of Semi-autonomous vehicles, and the third case evaluates a simulation based
design framework. This research study has advanced development by offering a
framework for developing testing and evaluating AGV -systems, based on concurrent
development using a virtual environment. The ability to exploit unique or novel features
of AGVs based on a virtual environment improves the potential of AGV-systems
considerably.University of Skovde. European Commission for funding the INCO/COPERNICUS Projec
Computational tasks in robotics and factory automation
The design of Manufacturing Planning and Control Systems (MPCSs) — systems that negotiate with Customers and Suppliers to exchange products in return for money in order to generate profit, is discussed.\ud
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The computational task of MPCS components are systematically specified as a starting point for the development of computational engines, as computer systems and programs, that execute the specified computation. Key issues are the overwhelming complexity and frequently changing application of MPCSs
Incorporating traffic patterns to improve delivery performance
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 63-64).Traffic, construction and other road hazards impact the on-time performance of companies that operate delivery fleets. This study examines how incorporating traffic patterns in vehicle route development compares with standard, deterministic methods. We seek to understand how using historical data improves both planning and overall delivery efficiency. Our analysis contrasts manifests that were developed by an industry standard routing software tool with projections that use traffic data by benchmarking them against actual routes run by drivers. In addition to evaluating the differences between route planning tools, we explore why those differences exist, including how uncertainty is incorporated. Evidence suggests that incorporating traffic patterns into vehicle routing does produce improved solutions. Needless to say, the delivery process needs to be evaluated holistically. Our recommendations involve the various steps for creating and executing a route. Operational considerations, the potential for improving customer service, and areas for further exploration are discussed. This thesis is being conducted with sponsorship from a leading consumer products company and in coordination with the CarTel mobile sensing data project at Massachusetts Institute of Technology (MIT).by Melody J. Dickinson and Jillian Leifer.M.Eng.in Logistic
Dynamic Behavior Sequencing in a Hybrid Robot Architecture
Hybrid robot control architectures separate plans, coordination, and actions into separate processing layers to provide deliberative and reactive functionality. This approach promotes more complex systems that perform well in goal-oriented and dynamic environments. In various architectures, the connections and contents of the functional layers are tightly coupled so system updates and changes require major changes throughout the system. This work proposes an abstract behavior representation, a dynamic behavior hierarchy generation algorithm, and an architecture design to reduce this major change incorporation process. The behavior representation provides an abstract interface for loose coupling of behavior planning and execution components. The hierarchy generation algorithm utilizes the interface allowing dynamic sequencing of behaviors based on behavior descriptions and system objectives without knowledge of the low-level implementation or the high-level goals the behaviors achieve. This is accomplished within the proposed architecture design, which is based on the Three Layer Architecture (TLA) paradigm. The design provides functional decomposition of system components with respect to levels of abstraction and temporal complexity. The layers and components within this architecture are independent of surrounding components and are coupled only by the linking mechanisms that the individual components and layers allow. The experiments in this thesis demonstrate that the: 1) behavior representation provides an interface for describing a behavior’s functionality without restricting or dictating its actual implementation; 2) hierarchy generation algorithm utilizes the representation interface for accomplishing high-level tasks through dynamic behavior sequencing; 3) representation, control logic, and architecture design create a loose coupling, but defined link, between the planning and behavior execution layer of the hybrid architecture, which creates a system-of-systems implementation that requires minimal reprogramming for system modifications
Data-Driven Dynamic Robust Resource Allocation: Application to Efficient Transportation
The transformation to smarter cities brings an array of emerging urbanization challenges. With the development of technologies such as sensor networks, storage devices, and cloud computing, we are able to collect, store, and analyze a large amount of data in real time. Modern cities have brought to life unprecedented opportunities and challenges for allocating limited resources in a data-driven way. Intelligent transportation system is one emerging research area, in which sensing data provides us opportunities for understanding spatial-temporal patterns of demand human and mobility. However, greedy or matching algorithms that only deal with known requests are far from efficient in the long run without considering demand information predicted based on data.
In this dissertation, we develop a data-driven robust resource allocation framework to consider spatial-temporally correlated demand and demand uncertainties, motivated by the problem of efficient dispatching of taxi or autonomous vehicles. We first present a receding horizon control (RHC) framework to dispatch taxis towards predicted demand; this framework incorporates both information from historical record data and real-time GPS location and occupancy status data. It also allows us to allocate resource from a globally optimal perspective in a longer time period, besides the local level greedy or matching algorithm for assigning a passenger pick-up location of each vacant vehicle. The objectives include reducing both current and anticipated future total idle driving distance and matching spatial-temporal ratio between demand and supply for service quality. We then present a robust optimization method to consider spatial-temporally correlated demand model uncertainties that can be expressed in closed convex sets. Uncertainty sets of demand vectors are constructed from data based on theories in hypothesis testing, and the sets provide a desired probabilistic guarantee level for the performance of dispatch solutions. To minimize the average resource allocation cost under demand uncertainties, we develop a general data-driven dynamic distributionally robust resource allocation model. An efficient algorithm for building demand uncertainty sets that compatible with various demand prediction methods is developed. We prove equivalent computationally tractable forms of the robust and distributionally robust resource allocation problems using strong duality. The resource allocation problem aims to balance the demand-supply ratio at different nodes of the network with minimum balancing and re-balancing cost, with decision variables on the denominator that has not been covered by previous work.
Trace-driven analysis with real taxi operational record data of San Francisco shows that the RHC framework reduces the average total idle distance of taxis by 52%, and evaluations with over 100GB of New York City taxi trip data show that robust and distributionally robust dispatch methods reduce the average total idle distance by 10% more compared with non-robust solutions. Besides increasing the service efficiency by reducing total idle driving distance, the resource allocation methods in this dissertation also reduce the demand-supply ratio mismatch error across the city
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