Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standarized Production Processes

FRUTOS, MARIANO; OLIVERA, ANA CAROLINA; FERNANDO TOHMÉ

Real-World Applications of Genetic Algorithms

INTECH

Lugar: Rijeka; Año: 2012; p. 113 - 132

To schedule production in a Job-Shop environment means to allocate adequately theavailable resources. It requires to rely on efficient optimization procedures. In fact, the Job-Shop Scheduling Problem (JSSP) is a NP-Hard problem (Ullman, 1975), so ad-hocalgorithms have to be applied to its solution (Frutos et al., 2010). This is similar to othercombinatorial programming problems (Olivera et al., 2006), (Cortés et al., 2004). Mostinstances of the Job-Shop Scheduling Problem involve the simultaneous optimization of twousually conflicting goals. This one, like most multi-objective problems, tends to have manysolutions. The Pareto frontier reached by an optimization procedure has to contain auniformly distributed number of solutions close to the ones in the true Pareto frontier. Thisfeature facilitates the task of the expert who interprets the solutions (Kacem et al., 2002). Inthis paper we present a Genetic Algorithm linked to a Simulated Annealing procedure ableto schedule the production in a Job-Shop manufacturing system (Cortés et al., 2004), (Tsai &Lin, 2003), (Wu et al., 2004), (Chao-Hsien & Han-Chiang, 2009).