1,529 research outputs found
Fluid and Diffusion Limits for Bike Sharing Systems
Bike sharing systems have rapidly developed around the world, and they are
served as a promising strategy to improve urban traffic congestion and to
decrease polluting gas emissions. So far performance analysis of bike sharing
systems always exists many difficulties and challenges under some more general
factors. In this paper, a more general large-scale bike sharing system is
discussed by means of heavy traffic approximation of multiclass closed queueing
networks with non-exponential factors. Based on this, the fluid scaled
equations and the diffusion scaled equations are established by means of the
numbers of bikes both at the stations and on the roads, respectively.
Furthermore, the scaling processes for the numbers of bikes both at the
stations and on the roads are proved to converge in distribution to a
semimartingale reflecting Brownian motion (SRBM) in a -dimensional box,
and also the fluid and diffusion limit theorems are obtained. Furthermore,
performance analysis of the bike sharing system is provided. Thus the results
and methodology of this paper provide new highlight in the study of more
general large-scale bike sharing systems.Comment: 34 pages, 1 figure
Hybrid dynamics in large-scale logistics networks
We study stability properties of interconnected hybrid systems with application to large-scale logistics networks. Hybrid systems are dynamical systems that combine two types of dynamics: continuous and discrete. Such behaviour occurs in wide range of applications. Logistics networks are one of such applications, where the continuous dynamics occurs in the production and processing of material and the discrete one in the picking up and delivering of material. Stability of logistics networks characterizes their robustness to the changes occurring in the network. However, the hybrid dynamics and the large size of the network lead to complexity of the stability analysis. In this thesis we show how the behaviour of a logistics networks can be described by interconnected hybrid systems. Then we recall the small gain conditions used in the stability analysis of continuous and discrete systems and extend them to establish input-to-state stability (ISS) of interconnected hybrid systems. We give the mixed small gain condition in a matrix form, where one matrix describes the interconnection structure of the system and the other diagonal matrix takes into account whether ISS condition for a subsystem is formulated in the maximization or the summation sense. The small gain condition is sufficient for ISS of an interconnected hybrid system and can be applied to an interconnection of an arbitrary finite number of ISS subsystems. We also show an application of this condition to particular subclasses of hybrid systems: impulsive systems, comparison systems and the systems with stability of only a part of the state. Furthermore, we introduce an approach for structure-preserving model reduction for large-scale logistics networks. This approach supposes to aggregate typical interconnection patterns (motifs) of the network graph. Such reduction allows to decrease the number of computations needed to verify the small gain condition
Plant supply logistics: balancing delivery and stockout costs
A manufacturer leases rail cars to transport raw material from the supplier to the factory. The manufacturer must balance the costs of leasing rail cars versus stockouts (leading to plant closings) and inventory carrying costs. Using a model of circular queues and a simulation, the cost implications of leasing different numbers of rail cars are analyzed. It is concluded that stockout costs exceed the cost of excess inventory and capacity in the logistics system
Operational Research: Methods and Applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV, in prep. for journal submission. In V3, we add a proof that the
socially-optimal solution can be enforced as a general equilibrium, a
privacy-preserving distributed optimization algorithm, a description of the
receding-horizon implementation and additional numerical results, and proofs
of all theorem
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV and accepted by TCNS. In Version 4, the body of the paper is
largely rewritten for clarity and consistency, and new numerical simulations
are presented. All source code is available (MIT) at
https://dx.doi.org/10.5281/zenodo.324165
Mathematics in the Supply Chain
[no abstract available
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