Assembly line balancing and genetic algorithms

Assembly Line Balancing (ALB) refers to the problem of assigning operations to (workstations) stations along an assembly line, optimaly balance. Since Henry Ford introduce the assembly lines, ALB has been an optimization problem of significant industrial importance: the efficiency difference between an optimal and a sub-optimal assignment can yield economies (or waste) than reaching millions of dollars per year. ALB is a classic Operations Research (OR) optimization problem, having been tackled by OR over several decades. Many algorithms have been proposed for the problem. [Falkenauer E. (2005)]. Although a Simple ALB Problem (SALBP) only takes into account two constraints (either the precedence constraints plus the cycle time, or the precedence constraints plus the number of workstations), it is by far the variant of line balancing that has been the most researched. The contribusion so that effort can be found in Falkenauer and Delchambre (1992), where they proposed a Grouping Genetic Algorithm approach that achieved some of the best performance in the field. The Grouping Genetic Algorithm technique itself was presented in detail in Falkenauer (1998). However well researched, the SALBP is hardly applicable in industry. The fact has not escaped the attention of the OR researches, and Becker and Scholl (2004) define many extensions to SALBP, yielding a common denomination GALBP (Generalized Assembly Line Balancing Problem). Each of the extensions reported in their authoritative survey aims to handle an additional difficulty present in real-world line balancing. Since the ALB problem falls into the NP-hard class of combinatorial optimization problems, numerous research efforts have been directed towards the development of computer efficient approximation algorithms or heuristics. In this context, GAs are intelligent random search mechanisms that are applied to various combinatorial optimization problems such as scheduling, TSP, and ALB. The existing studies in the literature have indicated that GA can be used as a very effective search technique in solving dificult problems because of its ability to move from one solution set to another and felexibility to incorporate the problem specific characteristics. Those aspects have been cleared in Fernando G. F (2002), also by applying the Genetic Algorithm. This chapter is organized as follows. One Section recalls the ALB Problem definitions and its classification. In another Section, some heuristic methods using for ALB problems are introduced. Last Section describes Genetic Algorithms playing for ALB problem and finally presents conclusions