An adapted Genetic Algorithm to solve Generalized Cell Formation

Abstract

This paper addresses the cell formation problem with alternative process plans and machine capacity constraints. Given alternative process plans, machine capacities and quantities of parts to produce, the problem consists in defining preferential process and routing for each part (grouping of operations into machines) optimizing machines grouping into manufacturing cells. The problem can be decomposed in two distinct sub-problems: operations grouping on resources, yielding flows between the machines, and grouping of these latter into independent cells. The objective of the proposed method is to optimize both groupings (operations on machines and machines into cell) minimizing of the inter-cellular moves. To solve simultaneously both grouping interdependent problems, we propose a modified grouping genetic algorithm (SIGGA). In our adapted grouping genetic algorithm, each chromosome is composed of two parts, one part for each problem. According to different application rates, the genetic operators are applied on the first, on the second problem, or on both problems. Finally, the population chromosomes simultaneously evolve in both problems.

Comparison between simultaneous and iterative resolutions for generalized cell formation problems with a Genetic Algorithm

“…The SIGGA (Simultaneous resolution by a Grouping Genetic Algorithm) is presented in Fig. 1 (completely described in Vin (2010)). This algorithm is based on a classical GGA.…”

“…Progressively, new production parameters have been introduced, such as sequence operation, cost, alternative process plans, part volumes, machine capacity, labour-related factors, and flexibility. Vin (2010) proposes a classification and a complete review based on these parameters used for a more complex cell formation problem.…”