A Stochastic Dynamic Program for Valuing Options on Futures

2013 ◽  
Vol 34 (12) ◽  
pp. 1185-1201 ◽  
Author(s):  
Mohamed A. Ayadi ◽  
Hatem Ben-Ameur ◽  
Tymur Kirillov ◽  
Robert Welch
Keyword(s):  
Dynamic Program ◽  
Stochastic Dynamic ◽  
Stochastic Dynamic Program

scholarly journals Loading-balance relay-selective strategy based on stochastic dynamic program

2018 ◽  
Vol 23 (4) ◽  
pp. 493-500
Author(s):  
Wei Zhao ◽  
Lin Zhao ◽  
Weidong Wu ◽  
Sigen Chen ◽  
Shaohui Sun ◽  
...  
Keyword(s):  
Dynamic Program ◽  
Stochastic Dynamic ◽  
Selective Strategy ◽  
Stochastic Dynamic Program

Individualized Pricing for a Cloud Provider Hosting Interactive Applications

2020 ◽  
Vol 12 (4) ◽  
pp. 130-147
Author(s):  
Hossein Jahandideh ◽  
Julie Ward Drew ◽  
Filippo Balestrieri ◽  
Kevin McCardle
Keyword(s):  
Dynamic Pricing ◽  
Large Scale ◽  
Mobile Apps ◽  
Dynamic Program ◽  
Cloud Services ◽  
Cloud Provider ◽  
Stochastic Dynamic ◽  
Interactive Applications ◽  
Pricing Mechanism ◽  
Stochastic Dynamic Program

We consider a cloud provider that hosts interactive applications, such as mobile apps and online games. Depending on the traffic of users for an application, the provider commits a subset of its resources (hardware capacity) to serve the application. The provider must choose a dynamic pricing mechanism to indirectly select the applications hosted and maximize revenue. We model the provider’s pricing problem as a large-scale stochastic dynamic program. To approach this problem, we propose a tractable approach to enable decomposing the multidimensional stochastic dynamic program into single-dimensional subproblems. We then extend the proposed framework to define an individualized dynamic pricing mechanism for the cloud provider. We present novel upper bounds on the optimal revenue to evaluate the performance of our pricing mechanism. The computational results show that a contract-based model of selling interactive cloud services achieves significantly greater revenue than the prevalent alternative and that our pricing scheme attains near-optimal revenue.

The sequence relay selection strategy based on stochastic dynamic programming

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740076
Author(s):  
Rui Zhu ◽  
Xihao Chen ◽  
Yangchao Huang
Keyword(s):  
Stochastic Dynamic Programming ◽  
Relay Selection ◽  
Relay Node ◽  
Selection Strategy ◽  
Stochastic Dynamic ◽  
Channel State ◽  
State Information ◽  
Convergence Performance ◽  
Relay Node Selection ◽  
Stochastic Dynamic Program

Relay-assisted (RA) network with relay node selection is a kind of effective method to improve the channel capacity and convergence performance. However, most of the existing researches about the relay selection did not consider the statically channel state information and the selection cost. This shortage limited the performance and application of RA network in practical scenarios. In order to overcome this drawback, a sequence relay selection strategy (SRSS) was proposed. And the performance upper bound of SRSS was also analyzed in this paper. Furthermore, in order to make SRSS more practical, a novel threshold determination algorithm based on the stochastic dynamic program (SDP) was given to work with SRSS. Numerical results are also presented to exhibit the performance of SRSS with SDP.

Profit or Growth? Dynamic Order Allocation in a Hybrid Workforce

2021 ◽  
Author(s):  
Eryn Juan He ◽  
Joel Goh
Keyword(s):  
Operations Management ◽  
Numerical Study ◽  
Growth Dynamics ◽  
Service Organizations ◽  
Network Effect ◽  
Stochastic Dynamic ◽  
Long Run ◽  
Last Mile ◽  
Stochastic Dynamic Program ◽  
Last Mile Delivery

Modern digital technology has enabled the emergence of the hybrid workforce in service organizations, where a firm uses on-demand freelancers to augment its traditional labor supply of employees. Freelancers are typically supplied by an electronic platform. How should demand be allocated between employees and freelancers? Under what conditions is the system (comprising the firm and its platform) sustainable in the long run? We investigate these questions in the context of last-mile delivery. We develop a discrete-time, stochastic dynamic program that captures the system’s profit from serving demand and the platform’s growth dynamics. The dynamic model incorporates a service constraint for the platform and a simple version of a stochastic network effect. We find that the answers to our research questions critically depend on two key parameters: the mean and variance of the cross-network effect. We conduct a numerical study with data from a last-mile delivery firm in Vietnam to illustrate our findings. This paper was accepted by Vishal Gaur, operations management.

SPLINE APPROXIMATIONS TO VALUE FUNCTIONS

1997 ◽  
Vol 1 (1) ◽  
pp. 255-277 ◽  
Author(s):  
MICHAEL A. TRICK ◽  
STANLEY E. ZIN
Keyword(s):  
Bellman Equation ◽  
Value Function ◽  
Approximate Solutions ◽  
Upper And Lower Bounds ◽  
Stochastic Dynamic ◽  
Value Functions ◽  
Stochastic Dynamic Program ◽  
Low Dimensional ◽  
The Value Function ◽  
Bounds On The Solution

We review the properties of algorithms that characterize the solution of the Bellman equation of a stochastic dynamic program, as the solution to a linear program. The variables in this problem are the ordinates of the value function; hence, the number of variables grows with the state space. For situations in which this size becomes computationally burdensome, we suggest the use of low-dimensional cubic-spline approximations to the value function. We show that fitting this approximation through linear programming provides upper and lower bounds on the solution to the original large problem. The information contained in these bounds leads to inexpensive improvements in the accuracy of approximate solutions.

scholarly journals Coordinating Pricing and Empty Container Repositioning in Two-Depot Shipping Systems

2020 ◽  
Vol 54 (6) ◽  
pp. 1697-1713
Author(s):  
Tao Lu ◽  
Chung-Yee Lee ◽  
Loo-Hay Lee
Keyword(s):  
Programming Model ◽  
Control Policy ◽  
Approximate Model ◽  
Dynamic Program ◽  
Stochastic Dynamic ◽  
Empty Container ◽  
Empty Container Repositioning ◽  
Joint Decisions ◽  
Dimensional State Space ◽  
In Transit

This paper studies joint decisions on pricing and empty container repositioning in two-depot shipping services with stochastic shipping demand. We formulate the problem as a stochastic dynamic programming model. The exact dynamic program may have a high-dimensional state space because of the in-transit containers. To cope with the curse of dimensionality, we develop an approximate model where the number of in-transit containers on each vessel is approximated with a fixed container flow predetermined by solving a static version of the problem. Moreover, we show that the approximate value function is [Formula: see text]-concave, thereby characterizing the structure of the optimal control policy for the approximate model. With the upper bound obtained by solving the information relaxation–based dual of the exact dynamic program, we numerically show that the control policies generated from our approximate model are close to optimal when transit times span multiple periods.

COMPUTING AVERAGE OPTIMAL CONSTRAINED POLICIES IN STOCHASTIC DYNAMIC PROGRAMMING

2001 ◽  
Vol 15 (1) ◽  
pp. 103-133 ◽  
Author(s):  
Linn I. Sennott
Keyword(s):  
Quality Of Service ◽  
Optimal Policy ◽  
Stochastic Dynamic ◽  
Value Iteration ◽  
Service Cost ◽  
Constrained Problem ◽  
Service Delay ◽  
Finite State ◽  
Stochastic Dynamic Program

A stochastic dynamic program incurs two types of cost: a service cost and a quality of service (delay) cost. The objective is to minimize the expected average service cost, subject to a constraint on the average quality of service cost. When the state space S is finite, we show how to compute an optimal policy for the general constrained problem under weak conditions. The development uses a Lagrange multiplier approach and value iteration. When S is denumerably infinite, we give a method for computation of an optimal policy, using a sequence of approximating finite state problems. The method is illustrated with two computational examples.

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