Hossein Jahandideh

Profile photo of Hossein Jahandideh
"As a PhD student I have worked on three research projects, all which involve allocating resources over time to different products or services, modeled as stochastic dynamic programs. I am particularly excited by the advances of technology and the research questions that it entails. Topics that motivate my recent and foreseeable-future research directions include resource and revenue management for cloud services, new technology release, e-business, information systems, business analytics, etc."
 

DOTM PhD Student

About

Place of Origin
Isfahan, Iran

Education
BS in Electrical Engineering
Sharif University of Technology
Tehran, Iran 

Entered program in 2013

Temporal Resource Allocation in Production Systems and Services under Uncertainty
In my dissertation, I study the problem of allocating resources to minimize costs or to maximize revenue when the decision-maker is uncertain about the outcome of decisions. I consider three different industries, all which involve allocating resources over time to different products or services, modeled as stochastic dynamic programs. I approach these problems using a mix of analytical and numerical methods and provide optimal or near-optimal policies. Computational evidence suggest significant potential cost savings or revenue enhancements in all three models.

The first problem arises in refineries where a catalyst is used in a reactor to refine raw material. Catalyst performance is not known a priori; it is learned through production observations, and it decays as it is consumed. The firm must decide when to replace the costly catalyst to minimize the total inventory costs and catalyst replacement costs. I decompose this problem into two subproblems and propose approximate solutions to each subproblem to provide near-optimal decision policies. To test the performance of the policies, I provide a simulation procedure to compute a lower bound on the optimal average cost of the original problem. I test the proposed method using data from a leading food processing company and show that this method significantly outperforms current practice.

The second problem is on products for which value increases with age. For instance, a whiskey production firm can charge a higher price for its barrels of whiskey if they age for a longer Time. I analyze the decision of a firm that is considering introducing an older aged product to the market. The older age product has uncertain demand and competes with the younger age product both in production capacity and in the market demand. The goal is to maximize the expected discounted revenue extracted from a fixed yearly production capacity, while considering uncertainty in demand, product substitution, and the learning process. We solve a simple version of the problem in closed form. We use the observations from the closed form solution, in addition to some theoretical properties to derive heuristics for more complex settings.

The third project is on selling cloud computing services. I consider a cloud provider which hosts applications such as websites and mobile apps. 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 pricing mechanism to indirectly select the applications hosted and maximize revenue. I model the provider's pricing problem as a large-scale stochastic dynamic program and decompose it into single-resource problems by exploiting structural properties of the model. A pricing mechanism is provided by combining the solutions to the sub-problems. I also present novel upper bounds on the optimal revenue to evaluate the performance of the pricing mechanism.