A stochastic programming approach to scheduling in TAC SCM

In this paper, we combine two approaches to handling uncertainty: we use techniques for finding optimal solutions in the expected sense to solve combinatorial optimization problems in an online setting. The problem we address is the scheduling component of the Trading Agent Competition in Supply Chain Management (TAC SCM) problem, a combinatorial optimization problem with inherent uncertainty (see www.sics.se/tac/). This problem is formulated as a stochastic program, and is solved using the sample average approximation (SAA) method in an online setting to find today’s optimal schedule, given probabilistic models of the future. This optimization procedure forms the heart of Botticelli, one of the finalists in the TAC SCM 2003 competition. Two sets of experiments are described, using one and two days’ worth of information about the future. In the two day experiments (using one day’s worth of information about the future), it is shown that SAA outperforms the expected value meth...

• Type: Conference
• Year: 2004
• Where: SIGECOM
• Authors: Michael Benisch, Amy R. Greenwald, Victor Naroditskiy, Michael Carl Tschantz
• Keywords: Combinatorial Optimization Problem, Online Setting, SIGECOM 2004, TAC SCM