Motivated by the success of implementing expert systems (ESs) based on artificial neural networks (ANNs) to improved classical rule-based expert systems (RBESs), this paper reports on the development of a neuro-based expert system (NBES) for facility layout construction in a manufacturing system. In an artificial intelligence (AI) technique such as the NBES, the semantic structure of If-Then rules is preserved, while incorporating the learning capability of ANNs into the inference mechanism. Unlike implementing a popular back propagation network (BPN) as an ES, the proposed BAMFLO (Bidirectional Associative Memories for Facility LayOut) system is an intelligent layout consultant system consisting of pipeline BAM neural networks with simple, fast incremental learning and multiple bidirectional generalization characteristics. This incrementability makes BAMFLO effective at acquiring, adding or adapting learned layout knowledge; thus it is possible to memorize newly extended If-Then layout rules without retraining old ones. The multi-bidirectionality gives BAMFLO the ability to quickly and reliably generalize a layout solution, and to further infer unknown facts from known facts through a complex knowledge base (memorization) without losing information. The solution process of BAMFLO contains three essential steps: training example generation, incremental learning and solution generalization. The examples (layout knowledge) can be generated from practical experience and/or classical layout software solutions for incrementally training BAMFLO; the process then derives multiply bidirectionally generalized construction layout solutions. The experimental results show that the BAMFLO scheme outperforms five classical layout methods used to generate training examples.

A neuro-based expert system for facility layout construction

A state-of-the-art review, spanning the last two decades, on application of metaheuristic methods in facility layout problems (FLPs) to gauge the current and emerging trends involving new design objectives, algorithms and methodologies to the combinatorial optimisation aspects is presented in this work. Fresh developments in emerging layout research, as analysed in this study, provide a perspective on what the future of the field will be like. A tendency of using metaheuristics, such as genetic algorithm (GA), simulated annealing (SA), ant colony optimisation (ACO) and particle swarm optimisation (PSO) with a trend towards multi-objective approaches to layout and material handling system design is observed.

Metaheuristic in facility layout problems: current trend and future direction

https://isiarticles.com/bundles/Article/pre/pdf/3594.pdf

Time-phased creation of hybrid manufacturing systems

The importance of the manufacturing shop design in the successful operation of a production system is well documented and, as a result, significant work has been devoted to this area Due to the inherent complexity of the global shop design problem, much research has focused on individual sub-problems, including the layout of manufacturing resources, the design of the shop aisle network, and the selection of the material handling equipment, rather than addressing the integrated problem directly This paper reviews literature contributions to shop design integration, as well as partial integration schemes such as the design for operation and control of material handling systems Furthermore, it examines the ability of models and solution approaches for individual sub-problems of shop design to support integration schemes

A review of current research in manufacturing shop design integration

Although the benefits of forming cells are widely recognized, certain industrial decision makers do not wish an entire transformation because of possible changes that may occur, for example, in the demand, mix or routing of products. Therefore, hybrid organization of functional departments and manufacturing cells in the same shop may appear quite relevant. The problem addressed here is how to allocate machines and products so as to design such systems that take advantages of both types of layout. We suggest an approach that favours cells for the parts that are regularly manufactured over time (i.e. stable parts), and functional departments for the parts that require more flexibility. We also consider the difficulties arising in the collection of precise data in reality. Hybrid shop design problem is formulated as a constrained fuzzy multi-objective optimization problem and an evolutionary programming algorithm is devised to solve it. The approach is illustrated in test examples.

Hybrid organization of functional departments and manufacturing cells in the presence of imprecise data

A method for the design of hybrid, cellular/functional, facilities is presented in this paper. This method targets the minimization of inter-cell material handling and the maximization of intra-cell directional material flow. Furthermore it develops a shop redesign plan that maximizes the net benefit obtained from the facility rearrangement.

R. Palmer

Papers

Design and implementation of a hybrid manufacturing facility