A new branch-&-bound-enhanced genetic algorithm for the manufacturing cell formation problem

Abstract

This paper deals with the manufacturing cell formation (MCF) problem, which is based on group technology principles, using a graph partitioning formulation. An attempt has been made to take into account the natural constraints of real-life production systems, such as operation sequences, minimum and maximum numbers of cells, and maximum cell sizes. Cohabitation constraints were added to the proposed model in order to deal with the necessity of grouping certain machines in the same cell for technical reasons, and non-cohabitation constraints were included to prevent placing certain machines in close vicinity.First, the problem is solved with a genetic algorithm (GA), using a binary coding system that has proved superior to the classic integer coding systems. A new Branch-and-Bound (B&B) enhancement is then proposed to improve the GA's performance. The results obtained for medium-sized instances using this enhancement are better than those obtained using the GA alone. Given these results, it is reasonable to assume that the B&B enhancement will provide good results for large real-life problems.

Development of a Web-Based Decision Support System for Cell Formation Problems Considering Alternative Process Routings and Machine Sequences

“…The proposed CF solver was coded in Visual C++ programming languages and implemented on an Intel(R) 1.66 GHz personal computer with 1GB RAM. Table 4 shows the comparisons of our proposed CF solver with other approaches from the literature, that is, the GABB [10], the MIP [8] and the SA [9]. The bold characters indicate the best values obtained for each test problem.…”

“…In this regard, many models and solution approaches have been developed to identify machine cells and part families. These approaches can be classified into three main categories [4]: 1) mathematical programming (MP) models [5][6][7][8], 2) heuristic/meta-heuristic solution algorithms [9][10][11], and 3) similarity coefficient methods (SCM) [12,13].…”

A new method for decreasing cell-load variation in dynamic cellular manufacturing systems

“…But their model was failed to solve large scale problems. Boulif and Atif (2006) considered a situation in which cohabitation and non-cohabitation machines exist. In actual practice, such a condition may indeed happen because of safety reasons.…”

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

“…As the manufacturing systems became more and more complex with the passage of time, the researchers' interest abstracted towards the development of multiobjective models. This increased the complexity of the CFP, thus requiring higher computational Zhao et al [8] GA-fuzzy Hwang and Sun [9] GA-greedy heuristic Chu and Tsai [10] GGA-greedy heuristic Chi and Yan [11] GA-fuzzy Logic Gonçalves and Resende [12] G A -l o c a ls e a r c hh e u r i s t i c Pai et al [13] GA-fuzzy Logic Nsakanda et al [14] GA-large scale optimization technique Boulif and Atif [15] G A -b r a n c h & b o u n d…”