Nature-Inspired and Evolutionary Techniques for Automation

In this chapter, nature-inspired and evolutionary techniques (ET) will be introduced for treating automation problems in advanced planning and scheduling, assembly line system, logistics, and transportation. ET is the most popular meta-heuristic method for solving NP-hard optimization problems. In the past few years, ETs have been exploited to solve design automation problems. Concurrently, the field of ET reveals a significant interest in evolvable hardware and problems such as scheduling, placement, or test pattern generation. The rest of this chapter is organized as follows. First, the background and developments of nature-inspired and ETs are described. Then basic schemes and working mechanism of genetic algorithms (GA), swarm intelligence, and other nature-inspired optimization algorithms are given. Multi-objective evolutionary algorithms for treating optimization problems with multiple and conflicting objectives are presented. Features of evolutionary search, such as hybrid evolutionary search, enhanced EA via learning, and evolutionary design automation are presented. Next, the various applications based on ETs for solving nonlinear/combinatorial optimization problems in automation are surveyed. In terms of advanced planning and scheduling (APS), the facility layout problems, planning and scheduling of manufacturing systems, and resource-constrained project scheduling problems are included. In terms of assembly line system, various assembly lines balancing (ALB) models are included. In terms of logistics and transportation, location allocation models and various types of logistics network models are included.