Deterministic annealing algorithm: tutorial, application to pickup and delivery problem and computational aspects

The deterministic annealing (DA) method, used for the solution of several nonconvex problems, offers the ability to avoid shallow local minima of a given cost surface and the ability to minimize the cost function even when there are many local minima. The method is established in a probabilistic framework through basic information-theoretic techniques such as maximum entropy and random coding. It arises naturally in the context of statistical mechanics by the emulation of a physical process whereby a solid is slowly cooled and at zero temperature assumes its minimum energy configuration. We start with the introduction to DA method and then present a tutorial to describe the algorithm steps. Also, we discuss the connections of DA method with Statistical Mechanics and Rate-Distortion Theory. Next, we present the application of DA method to pickup and deliver scheduling problem with time windows. Finally, a computational complexity analysis for DA is presented for a given temperature schedule. The case study focuses on the geometric cooling law $T(t)=\rho T(t-1), 0<\rho<1$, where $T(t)$ is the temperature at time $t$.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Parekh, Pratik Mayur

• Deterministic Annealing Algorithm
• Computational Aspects

Statement dc:rights

• Copyright 2015 Pratik Mayur Parekh

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/78688

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/78688