Stochastic Optimization based on White-box Deterministic Approximations: Models, Algorithms and Application to Service Networks

Event Sponsor: 
Mathmatics and Computer Science Division Seminar
Start Date: 
Jun 12 2018 - 10:00am
Building 240/Room 4301
Argonne National Laboratory
Mohan Krishnamoorthy
Speaker(s) Title: 
George Mason University
Sven Leyffer

In recent years, due to increased global competition, there is a need for analysis and optimization solutions to reduce production cost and increase efficiency of operations. This research is driven by the need for analysis and optimization solutions for production planning of complex service networks. Service networks are typically composed of unit manufacturing processes, base contract services, vendor services, transportation services, and other supply chain artifacts. These networks often involve physical or virtual inventories of products, parts, and materials that are used to anticipate uncertainties in supply and throughput of machines. The white box models for service networks may be described using non-linear arithmetic and may be non-deterministic due to the presence of noise.

In this talk, I will present the model design to allow composition of a large variety of complex service networks by translating it into uniform representations called performance models whose metrics are a function of the input parameters and controls subject to the satisfaction of feasibility constraints. Additionally, I will present a one-stage stochastic optimization algorithm for the problem of finding process controls that minimize the expectation of cost while satisfying multiple deterministic and stochastic feasibility constraints with a given high probability. The proposed algorithm is based on a series of deterministic approximations to produce a candidate solution set and on a refinement step using stochastic simulations with optimal simulation budget allocation. The experimental study conducted on the performance model of a real-world manufacturing service network shows the proposed algorithm significantly outperforms four popular simulation-based stochastic optim ization algorithms.

Miscellaneous Information: 

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