On Solving Large-Size Generalized Cell Formation Problems via a Hard Computing Approach Using the PMP

Abstract

In this paper, we show that the hard computing approach using the p-median problem (PMP) is a very effective strategy for optimally solving large-size generalized cell formation (GCF) problems. The soft computing approach, relying on heuristic or metaheuristic search algorithms, has been the prevailing strategy for solving large-size GCF problems with a short computation time at the cost of the global optimum in large instances of GCF problems; however, due to recent advances in computing technology, using hard computing techniques to solve large-sized GCF problems optimally is not time-prohibitive if an appropriate mathematical model is built. We show that the hard computing approach using the PMP-type model can even solve large 0–1 GCF instances optimally in a very short computation time with a powerful mixed integer linear programming (MILP) solver adopting an exact search algorithm such as the branch-and-bound algorithm.

Standardizing Components and Rotating Workers Using GT-Based Algorithm—A Case Study

“…GT algorithms can be used to group similar parts or machines [41,42], meaning that GT provides a method of sorting machines into machine cells and parts into part families [43][44][45][46][47][48][49]. Several algorithms have been developed to solve the component standardization problem, such as heuristics [50][51][52][53][54][55], genetic algorithms [56][57][58][59][60], and closed neighbor algorithms [43,44,61].…”