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The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented.

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In this paper, optimization procedures based on the genetic algorithm, tabu search, ant colony algorithm and particle swarm optimization Algorithm were developed for the optimization of machining parameters for milling operation. This paper describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. An objective function based on maximum profit in milling operation has been used. An example has been presented at the end of the paper to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using the method of feasible directions and handbook recommendations.

Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a timesaving model that satisfies all the accuracy requirements.

An ant colony based optimisation procedure has been developed to optimise grinding conditions, viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using a multi-objective function model with a weighted approach for the surface grinding process. The procedure evaluates the production cost and production rate for the optimum grinding condition, subjected to constraints such as thermal damage, wheel wear parameters, machine tool stiffness and surface finish. The results are compared with Genetic Algorithm (GA) and Quadratic Programming (QP) techniques. Nomenclature a p down feed of grinding (mm/pass) a w total thickness of cut (mm) A o initial wear flat-area percentage (%) b e empty width of grinding (mm) b s width of wheel (mm) b w width of workpiece (mm) B k positive definite approximation of the Hessian doc depth of dressing (mm) c d cost of dressing ($) c s cost of wheel per mm 3 ($/mm 3 ) CTtotal production cost ($/pc) CT * expected production cost limit ($/pc) d g grind size (mm) D e diameter of wheel (mm) f b cross feed rate (mm/pass) G grinding ratio k a constant dependent on coolant and wheel grind type k u wear constant (mm -1 ) k c cutting stiffness (N/mm) k m static machine stiffness (N/mm) k s wheel wear stiffness (N/mm) L lead of dressing (mm/rev) L e empty length of grinding (mm) L w length of workpiece (mm) M c cost per hour labour and administration ($/h) N d total number of pieces to be grouped during the life of dressing (pc) N t batch size of workpieces (pc) N td total number of workpieces to be grouped during the life of dressing (pc) P number of workpieces loaded on the table (pc) R a surface roughness (lm) R a * surface finish limit during rough grinding (lm) R c workpiece hardness (Rockwell hardness number) R em dynamic machine characteristics S d distance of wheel idling (mm) S p number of spark out grinding (pass) t sh time of adjusting machine tool (min) t i time of loading and unloading workpiece (min) T ave average chip thickness during grinding (lm) U specific grinding energy (J/mm) U * critical specific grinding energy (J/mm 3 ) V r speed of wheel idling (mm/min) V s wheel speed (m/min) V w workpiece speed (m/min) VOLwheel bond percentage (%) WRP workpiece removal parameter (mm 3 /min-N) WRP * workpiece removal parameter limit (mm 3 /min-N) WWP wheel wear parameter (mm 3 /min-N) W i weighting factor, 0 £ W i £ 1

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