Different metaheuristic methods have been used to solve clustering problems. This paper addresses the problem of manufacturing cell formation using a modified particle swarm optimisation (PSO) algorithm. The main modification made to the original PSO algorithm consists on that in this work it is not used the vector of velocities as the standard PSO algorithm does. The proposed algorithm uses the concept of proportional likelihood with modifications, a technique that is used in data mining techniques. Some simulations are presented and compared. The criterion used to group the machines in cells is based on the minimization of inter-cell movements. The computational results show that the PSO algorithm is able to find the optimal solutions on almost all instances.

Starting from the premise that the management of fixture systems in the area of machining has been somewhat neglected from the standpoint of its technological development, the work reported on herein focuses on the development of a methodology for the management of these systems. Based on the findings of a survey carried out at companies operating in this sector and on a review of the literature on the subject, both of which confirm this lack, hypotheses are drawn up and supporting data compiled to underpin the development of a systematic, extensive and flexible model. The model, founded on principles of standardization and modularity, basically consists of four main stages: the company's definition/characterization of fixtures management, selection of the type of fixture suitable for the company, rationalization and organization of the area, and the construction of planning and control architectures within the company. We foresee that the formalization of the scientifically-based management support model proposed here will contribute significantly to productive and R&D sectors, computerizing the fixtures management methods used by companies and integrating their managerial philosophies and procedures for machining tools and fixtures