In the world around us it is rare for any problem to concern only a single value or objective. Generally, multiple objectives or parameters have to be met or optimized before any ‘master’ or ‘holistic’ solution is considered adequate. Most realistic optimization problems, particularly those in design, require the simultaneous optimization of more than one objective function.
Published in Chapter:
Simulation Model of Ant Colony Optimization for the FJSSP
Li-Ning Xing (National University of Defense Technology, China), Ying-Wu Chen (National University of Defense Technology, China), and Ke-Wei Yang (National University of Defense Technology, China)
Copyright: © 2009
|Pages: 7
DOI: 10.4018/978-1-60566-026-4.ch551
Abstract
The job shop scheduling problem (JSSP) is generally defined as decision-making problems with the aim of optimizing one or more scheduling criteria. Many different approaches, such as simulated annealing (Wu et al., 2005), tabu search (Pezzella & Merelli, 2000), genetic algorithm (Watanabe, Ida, & Gen, 2005), ant colony optimization (Huang & Liao, 2007), neural networks (Wang, Qiao, &Wang, 2001), evolutionary algorithm (Tanev, Uozumi, & Morotome, 2004) and other heuristic approach (Chen & Luh, 2003; Huang & Yin, 2004; Jansen, Mastrolilli, & Solis-Oba, 2005; Tarantilis & Kiranoudis, 2002), have been successfully applied to JSSP. Flexible job shop scheduling problem (FJSSP) is an extension of the classical JSSP which allows an operation to be processed by any machine from a given set. It is more complex than JSSP because of the addition need to determine the assignment of operations to machines. Bruker and Schlie (1990) were among the first to address this problem.