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The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.

Assembly line balancing is a key for organizational productivity in terms of reduced number of workstations for a given production volume per shift. Mixed-model assembly line balancing is a reality in many organizations. The mixed-model assembly line balancing problem comes under combinatorial category. So, in this paper, an attempt has been made to develop three genetic algorithms for the mixed-model assembly line balancing problem such that the combined balancing efficiency is maximized, where the combined balancing efficiency is the average of the balancing efficiencies of the individual models. At the end, these three algorithms and another algorithm in literature are compared in terms of balancing efficiency using a randomly generated set of problems through a complete factorial experiment, in which "Algorithm", "Problem Size" and "Cycle Time" are used as factors with two replications under each of the experimental combinations to draw inferences and to identify the best of the four algorithms. Then, through another set of randomly generated small and medium size data, the results of the best algorithm are compared with the optimal results obtained using a mathematical model. It is found that best algorithm gives the optimal solution for all the problems in the second set of data, except for one problem which cannot be solved using the model. This observation supports the fact that the best algorithm identified in this paper gives superior results. KeywordsAssembly Line Balancing, Cycle Time, Genetic Algorithm, Crossover Operation, Mixed-Model, Mathematical Model P. Sivasankaran, P. M. Shahabudeen 675

The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of Al 2 O 3 of 5%. The turning experiments were carried out in Siemens-CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of Al 2 O 3 nano lubrication gives better value of both MRR and SR.

This research addresses scheduling problem of n jobs on a Hybrid Flow Shop (HFS) with unrelated parallel machines in each stage. A monolithic mixed integer linear programming (MILP) model is presented to minimize the maximum completion time (makespan). As the research problem is shown to be strongly NP-hard, a Lagrangian relaxation (LR) algorithm is developed to handle the HFS scheduling problem. We design two approaches, simplification of subproblems and dominance rules, to solve the subproblems which are generated in each iteration. For evaluation purposes, numerical experiments with small and large size problems are randomly generated with up to 50 jobs and four stages. The experimental results show that the Lagrangian relaxation approaches outperform the MILP model with respect to CPU time. Furthermore, from the results, it can be conclude that the simplification of subproblems shows slightly better solutions in comparison with dominance rules to solve the subproblems.