scholarly journals Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ride-Sharing Platforms

2021 ◽  
Vol 51 (5) ◽  
pp. 373-390
Author(s):  
Hao Yi Ong ◽  
Daniel Freund ◽  
Davide Crapis
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Optimal Solution ◽  
Monetary Incentives ◽  
Convex Programs ◽  
Personal Power ◽  
Lack Of Information ◽  
Short Run ◽  
Main Components ◽  
Levels Of Service

Drivers on the Lyft ride-share platform do not always know where the areas of supply shortage are in real time. This lack of information hurts both riders trying to find a ride and drivers trying to determine how to maximize their earnings opportunities. Lyft’s Personal Power Zone (PPZ) product helps the company to maintain high levels of service on the platform by influencing the spatial distribution of drivers in real time via monetary incentives that encourage them to reposition their vehicles. The underlying system that powers the product has two main components: (1) a novel “escrow mechanism” that tracks available incentive budgets tied to locations within a city in real time, and (2) an algorithm that solves the stochastic driver-positioning problem to maximize short-run revenue from riders’ fares. The optimization problem is a multiagent dynamic program that is too complicated to solve optimally for our large-scale application. Our approach is to decompose it into two subproblems. The first determines the set of drivers to incentivize and where to incentivize them to position themselves. The second determines how to fund each incentive using the escrow budget. By formulating it as two convex programs, we are able to use commercial solvers that find the optimal solution in a matter of seconds. Rolled out to all 320 cities in which Lyft operates in a little more than a year, the system now generates millions of bonuses that incentivize hundreds of thousands of active drivers to optimally position themselves in anticipation of ride requests every week. Together, the PPZ product and its underlying algorithms represent a paradigm shift in how Lyft drivers drive and generate earnings on the platform. Its direct business impact has been a 0.5% increase in incremental bookings, amounting to tens of millions of dollars per year. In addition, the product has brought about significant improvements to the driver and rider experience on the platform. These include statistically significant reductions in pick-up times and ride cancellations. Finally, internal surveys reveal that the vast majority of drivers prefer PPZs over the legacy system.

scholarly journals Synthesis of reversible circuits for large reversible functions

2010 ◽  
Vol 23 (3) ◽  
pp. 273-286 ◽  
Author(s):  
Nouraddin Alhagi ◽  
Maher Hawash ◽  
Marek Perkowski
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Large Scale ◽  
Optimal Solution ◽  
Truth Table ◽  
Synthesis Process ◽  
Quantum Cost ◽  
Reversible Circuits ◽  
Gate Count ◽  
Reversible Circuit

This paper presents a new algorithm MP (multiple pass) to synthesize large reversible binary circuits without ancilla bits. The well-known MMD algorithm for synthesis of reversible circuits requires to store a truth table (or a Reed-Muller - RM transform) as a 2n vector to represent a reversible function of n variables. This representation prohibits synthesis of large functions. However, in MP we do not store such an exponentially growing data structure. The values of minterms are calculated in MP dynamically, one-by-one, from a set of logic equations that specify the reversible circuit to be designed. This allows for synthesis of large scale reversible circuits (30-bits), which is not possible with any existing algorithm. In addition, our unique multi-pass approach where the circuit is synthesized with various, yet specific, minterm orders yields quasi-optimal solution. The algorithm returns a description of the quasi-optimal circuit with respect to gate count or to its 'quantum cost'. Although the synthesis process in MP is relatively slower, the solution is found in real-time for smaller circuits of 8 bits or less.

scholarly journals Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4320 ◽  
Author(s):  
Michael Short ◽  
Sergio Rodriguez ◽  
Richard Charlesworth ◽  
Tracey Crosbie ◽  
Nashwan Dawood
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Industry 4.0 ◽  
Energy Demand ◽  
Large Scale ◽  
Optimal Solution ◽  
Hvac Systems ◽  
Economic Incentives ◽  
Building Stock ◽  
The Real

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.

Effective Service Composition in Large Scale Service Market

2012 ◽  
Vol 9 (1) ◽  
pp. 74-94 ◽  
Author(s):  
Xianzhi Wang ◽  
Zhongjie Wang ◽  
Xiaofei Xu
Keyword(s):  
Real Time ◽  
Service Composition ◽  
Large Scale ◽  
Effective Means ◽  
Optimal Solution ◽  
Solution Space ◽  
Coarse Grained ◽  
Service Market ◽  
Time Service ◽  
Special Cases

The web has undergone a tremendous shift from information repository to the provisioning capacity of services. As an effective means of constructing coarse-grained solutions by dynamically aggregating a set of services to satisfy complex requirements, traditional service composition suffers from dramatic decrease on the efficiency of determining the optimal solution when large scale services are available in the Internet based service market. Most current approaches look for the optimal composition solution by real-time computation, and the composition efficiency greatly depends on the adopted algorithms. To eliminate such deficiency, this paper proposes a semi-empirical composition approach which incorporates the extraction of empirical evidence from historical experiences to provide guidance to solution space reduction to real-time service selection. Service communities and historical requirements are further organized into clusters based on similarity measurement, and then the probabilistic correspondences between the two types of clusters are identified by statistical analysis. For each new request, its hosting requirement cluster would be identified and corresponding service clusters would be determined by leveraging Bayesian inference. Concrete services would be selected from the reduced solution space to constitute the final composition. Timing strategies for re-clustering and consideration to special cases in clustering ensures continual adaption of the approach to changing environment. Instead of relying solely on pure real-time computation, the approach distinguishes from traditional methods by combining the two perspectives together.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

Models, Practices and Methods of Reading: Evolution in Time and Space

2009 ◽  
pp. 59-64
Author(s):  
Yulia P. Melentyeva
Keyword(s):  
Large Scale ◽  
Social Groups ◽  
National Program ◽  
Time And Space ◽  
Lack Of Information ◽  
The Individual

In recent years as public in general and specialist have been showing big interest to the matters of reading. According to discussion and launch of the “Support and Development of Reading National Program”, many Russian libraries are organizing the large-scale events like marathons, lecture cycles, bibliographic trainings etc. which should draw attention of different social groups to reading. The individual forms of attraction to reading are used much rare. To author’s mind the main reason of such an issue has to be the lack of information about forms and methods of attraction to reading.

SOVIET SCIENTIFIC PERSONAL REPRESSIVE STATE APPARATUS AS AN OBJECT OF HISTORICAL SCIENCE OF SCIENCE STUDIES

2020 ◽  
pp. 27-35
Author(s):  
Alexander Allakhverdyan
Keyword(s):  
Science Policy ◽  
Large Scale ◽  
Science Studies ◽  
Soviet Science ◽  
State Repression ◽  
Developed Country ◽  
Systemic Analysis ◽  
State Apparatus ◽  
Dramatic Practice ◽  
Main Components

Numerous studies by Russian scientists and historians of science are devoted to the state science policy in the USSR and its well-known achievements, but not enough attention is paid to the negative, socially repressed aspects of the Soviet science policy. Repressions became one of the main components of the state's scientific and personnel policy in the Stalinist era. The systemic analysis of the development of Soviet science declared in the scientific literature, limited only by its indisputably outstanding achievements, without under-standing the origins, causes and mechanisms of the repressed state apparatus that operated in the same period, sharply reduces the overall picture of the reliability of the study of Soviet science. The purpose of the study is to comprehend the diverse and dramatic practice of state repression in the system of Soviet science, because in the world history of science no other developed country has experienced such large-scale and tragic events in the functioning of the scientific society.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

Reuse of Wastewaters in Agriculture by Means of Sprinkler Irrigation on a Farmland Area of 3000 ha – Large Scale Experience in Germany

1986 ◽  
Vol 18 (9) ◽  
pp. 163-173
Author(s):  
R. Boll ◽  
R. Kayser
Keyword(s):  
Large Scale ◽  
Treatment Process ◽  
Treatment Plant ◽  
Sprinkler Irrigation ◽  
Paper Briefly ◽  
Treatment Results ◽  
Sewage Farm ◽  
Western Germany ◽  
Pre Treatment ◽  
Main Components

The Braunschweig wastewater land treatment system as the largest in Western Germany serves a population of about 270.000 and has an annual flow of around 22 Mio m3. The whole treatment process consists of three main components : a pre-treatment plant as an activated sludge process, a sprinkler irrigation area of 3.000 ha of farmland and an old sewage farm of 200 ha with surface flooding. This paper briefly summarizes the experiences with management and operation of the system, the treatment results with reference to environmental impact, development of agriculture and some financial aspects.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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