This paper addresses the cell formation problem with alternative part routings, considering machine capacity constraints. Given processes, machine capacities and quantities of parts to produce, the problem consists in defining the preferential routing for each part optimising the grouping of machines into manufacturing cells. The main objective is to minimise the inter-cellular traffic, while respecting machine capacity constraints. To solve this problem, the authors propose an integrated approach based on a multiple-objective grouping genetic algorithm for the preferential routing selection of each part (by solving an associated resource planning problem) and an integrated heuristic for the cell formation problem.

No 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.

No 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.