Dynamic Control of Logistics Queueing Networks for Large-Scale Fleet Management

1998 ◽  
Vol 32 (2) ◽  
pp. 90-109 ◽  
Author(s):  
Warren B. Powell ◽  
Tassio A. Carvalho
Keyword(s):  
Queueing Networks ◽  
Large Scale ◽  
Dynamic Control ◽  
Fleet Management

scholarly journals Acoustoelectronic nanotweezers enable dynamic and large-scale control of nanomaterials

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peiran Zhang ◽  
Joseph Rufo ◽  
Chuyi Chen ◽  
Jianping Xia ◽  
Zhenhua Tian ◽  
...  
Keyword(s):  
Large Scale ◽  
Condensed Matter ◽  
Dynamic Control ◽  
Massively Parallel ◽  
Large Field ◽  
Graphene Flakes ◽  
Field Dynamic ◽  
Scale Control ◽  
Orientation Pattern ◽  
Resolution Single

AbstractThe ability to precisely manipulate nano-objects on a large scale can enable the fabrication of materials and devices with tunable optical, electromagnetic, and mechanical properties. However, the dynamic, parallel manipulation of nanoscale colloids and materials remains a significant challenge. Here, we demonstrate acoustoelectronic nanotweezers, which combine the precision and robustness afforded by electronic tweezers with versatility and large-field dynamic control granted by acoustic tweezing techniques, to enable the massively parallel manipulation of sub-100 nm objects with excellent versatility and controllability. Using this approach, we demonstrated the complex patterning of various nanoparticles (e.g., DNAs, exosomes, ~3 nm graphene flakes, ~6 nm quantum dots, ~3.5 nm proteins, and ~1.4 nm dextran), fabricated macroscopic materials with nano-textures, and performed high-resolution, single nanoparticle manipulation. Various nanomanipulation functions, including transportation, concentration, orientation, pattern-overlaying, and sorting, have also been achieved using a simple device configuration. Altogether, acoustoelectronic nanotweezers overcome existing limitations in nano-manipulation and hold great potential for a variety of applications in the fields of electronics, optics, condensed matter physics, metamaterials, and biomedicine.

scholarly journals A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1021
Author(s):  
Michael Wittmann ◽  
Lorenz Neuner ◽  
Markus Lienkamp
Keyword(s):  
Service Quality ◽  
Large Scale ◽  
Management Strategies ◽  
Global Market ◽  
Fleet Management ◽  
Bipartite Matching ◽  
Fleet Size ◽  
Original Size ◽  
Real Time Information

The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers. Rapid developments in available algorithms, data sources, and real-time information systems offer new possibilities of maximizing the efficiency of MoD services. In particular, the use of demand predictions is expected to contribute to a reduction in operational costs and an increase in overall service quality. This paper examines the potential of predictive fleet management strategies applied to a large-scale real-world taxi dataset for the city of Munich. A combination of state-of-the art dispatching algorithms and a predictive RHC optimization for idle vehicle rebalancing was developed to determine the scale by which a fleet size can be reduced without affecting service quality. A simulation study was conducted over a one-week period in Munich, which showed that predictive fleet strategies clearly outperform the present strategy in terms of both service quality and costs. Furthermore, the results showed that current taxi fleets could be reduced to 70% of their original size without any decrease in performance. In addition, the results indicated that the reduced fleet size of the predictive strategy was still 20% larger compared to the theoretical optimum resulting from a bipartite matching approach.

scholarly journals A Cloud-Based In-Field Fleet Coordination System for Multiple Operations

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 775 ◽  
Author(s):  
Caicong Wu ◽  
Zhibo Chen ◽  
Dongxu Wang ◽  
Bingbing Song ◽  
Yajie Liang ◽  
...  
Keyword(s):  
Operating System ◽  
Large Scale ◽  
Field Experiments ◽  
Flow Shop ◽  
Time Windows ◽  
Fleet Management ◽  
Prototype System ◽  
Arable Farming ◽  
Coordination System ◽  
Multiple Machines

In large-scale arable farming, multiple sequential operations involving multiple machines must be carried out simultaneously due to restrictions of short time windows. However, the coordination and planning of multiple sequential operations is a nontrivial task for farmers, since each operation may have its own set of operational features, e.g., operating width and turning radius. Taking the two sequential operations—hoeing cultivation and seeding—as an example, the seeder has double the width of the hoeing cultivator, and the seeder must remain idle while waiting for the hoeing cultivator to finish two rows before it can commence its seeding operation. A flow-shop working mode can coordinate multiple machines in multiple operations within a field when different operations have different implement widths. To this end, an auto-steering-based collaborative operating system for fleet management (FMCOS) was developed to realize an in-field flow-shop working mode, which is often adopted by the scaled agricultural machinery cooperatives. This paper proposes the structure and composition of the FMCOS, the method of operating strip segmenting, and a new algorithm for strip state updating between successive field operations under an optimal strategy for waiting time conditioning between sequential operations. A simulation model was developed to verify the state-updating algorithm. Then, the prototype system of FMCOS was combined with auto-steering systems on tractors, and the collaborative operating system for the server was integrated. Three field experiments of one operation, two operations, and three operations were carried out to verify the functionality and performance of FMCOS. The results of the experiment showed that the FMCOS could coordinate in-field fleet operations while improving both the job quality and the efficiency of fleet management by adopting the flow-shop working mode.

ERROR BOUNDS FOR OCCUPATION MEASURE OF SINGULARLY PERTURBED MARKOV CHAINS INCLUDING TRANSIENT STATES

2000 ◽  
Vol 14 (4) ◽  
pp. 511-531 ◽  
Author(s):  
G. Yin ◽  
Q. Zhang ◽  
Q. G. Liu
Keyword(s):  
Markov Chains ◽  
Error Bounds ◽  
Queueing Networks ◽  
Large Scale ◽  
Singularly Perturbed ◽  
Planning System ◽  
Nonstationary Processes ◽  
Transient States ◽  
Asymptotic Error ◽  
Occupation Measures

Motivated by many applications in production planning, system reliability, queueing networks, and wireless communication, this work is devoted to singularly perturbed Markov chains with finite states. Focusing on nonstationary processes with the inclusion of transient states, asymptotic error bounds of a sequence of suitably scaled occupation measures are derived. The main tools used include martingales and differential equations. The results are useful for analyzing structural properties of the underlying Markov chains and for designing nearly optimal and hierarchical controls of large-scale and complex systems.

COMBINED NEURAL-NET/KNOWLEDGE-BASED ADAPTIVE SYSTEMS FOR LARGE SCALE DYNAMIC CONTROL

1991 ◽  
Vol 05 (04) ◽  
pp. 503-522 ◽  
Author(s):  
ALISTAIR D. C. HOLDEN ◽  
STEVEN C. SUDDARTH
Keyword(s):  
Adaptive Systems ◽  
Large Scale ◽  
Dynamic Control ◽  
Back Propagation ◽  
Computation Time ◽  
Neural Nets ◽  
Small Scale ◽  
Neural Net ◽  
Training Time ◽  
Knowledge Based

The control of small-scale systems using either knowledge-based or neural net methods is quite feasible. Large scale systems, however, introduce complexities in modeling and excessive computation time. This paper attacks these difficulties by breaking down the problem into a hierarchy of control contexts. The lowest level of this hierarchy is implemented as rule sets and/or neural networks. A method using "hints" is shown to greatly reduce training time in back-propagation neural nets.

scholarly journals Dynamic control of hippocampal spatial coding resolution by local visual cues

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Romain Bourboulou ◽  
Geoffrey Marti ◽  
François-Xavier Michon ◽  
Elissa El Feghaly ◽  
Morgane Nouguier ◽  
...  
Keyword(s):  
Hippocampal Neurons ◽  
Visual Cues ◽  
Large Scale ◽  
Dynamic Control ◽  
Local Features ◽  
Temporal Coding ◽  
Spatial Coding ◽  
3D Objects ◽  
Phase Precession ◽  
Fast Adaptation

The ability to flexibly navigate an environment relies on a hippocampal-dependent cognitive map. External space can be internally mapped at different spatial resolutions. However, whether hippocampal spatial coding resolution can rapidly adapt to local features of an environment remains unclear. To explore this possibility, we recorded the firing of hippocampal neurons in mice navigating virtual reality environments, embedding or not local visual cues (virtual 3D objects) in specific locations. Virtual objects enhanced spatial coding resolution in their vicinity with a higher proportion of place cells, smaller place fields, increased spatial selectivity and stability. This effect was highly dynamic upon objects manipulations. Objects also improved temporal coding resolution through improved theta phase precession and theta timescale spike coordination. We propose that the fast adaptation of hippocampal spatial coding resolution to local features of an environment could be relevant for large-scale navigation.

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