International Journal of Industrial Engineering & Production Research

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In this paper the problem of serial batch scheduling in a two-stage hybrid flow shop environment with minimizing Makesapn is studied. In serial batching it is assumed that jobs in a batch are processed serially, and their completion time is defined to be equal to the finishing time of the last job in the batch. The analysis and implementation of the prohibited transference of jobs among the machines of stage one in serial batch is the main contribution of this study. Machine set-up and ready time for all jobs are assumed to be zero and no Preemption is allowed. Machines may not breakdown but at times they may be idle. As the problem is NP-hard, a genetic algorithm is developed to give near optimal solutions. Since this problem has not been studied previously, therefore, a lower bound is developed for evaluating the performance of the proposed GA. Many test problems have been solved using GA and results compared with lower bound. Results showed GA can obtain a near optimal solution for small, median and large size problems in reasonable time.

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Assembly lines are special kinds of production systems which are of great importance in the industrial production of high quantity commodities. In many practical manufacturing systems, configuration of assembly lines is fixed and designing a new line may be incurred huge amount of costs and thereby it is not desirable for practitioners. When some changes related to market demand occur, it is worthwhile to re-balance an existing line rather than balancing a new one. Hence, in this paper we suggest a re-balancing model of an existing assembly line in which a new demand related cycle time (CT) is embedded to the traditional assembly line balancing problem (ALBP) as a new parameter. It does not focus on balancing a new line instead it considers a more realistic problem which is re-balancing an existing line. The objective is to re-schedule the tasks in order to reduce the current CT to the new required one such that two criteria are optimized: (i) minimization of the incurred costs and (ii) minimization of non-smoothing of reconfigured line. To solve the considered problem, an effective differential evolution algorithm is developed. Furthermore, to enhance the performance of algorithm, its parameters are optimized by the use of Taguchi method which is a conventional statistical technique for parameter design. The obtained results from computational experiments on benchmark instances show the effectiveness of suggested algorithm against other methods.

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Manufacturing process frequently employs optimization of machining parameters in order to improve product quality as well as to enhance productivity. The material removal rate is a significant indicator of the productivity and cost efficiency of the process. Taguchi method has been implemented for assessing favorable (optimal) machining condition during the machining of nylon by considering three important cutting parameters like cutting speed, feed rate and depth of cut during machining on CNC. The objective of the paper is to find out, which process parameters having more impacts on material removal rate during turning operation on nylon using analysis of variance (ANOVA). An Orthogonal array has been constructed to find the optimal levels of the turning parameters and further signal-to-noise (S/N) ratio has been computed to construct the analysis of variance table. The results of ANOVA shown that feed rate has most significant factor on MRR compare to cutting speed and depth of cut for nylon. The confirmation experiments have conducted to validate the optimal cutting parameters and improvement of MRR from initial conditions is 555.56%.

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Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. In this paper, a bi-objective mathematical model is proposed to design a network of preventive healthcare facilities so as to minimize total travel and waiting time as well as establishment and staffing cost. Moreover, each facility acts as M/M/1 queuing system. The number of facilities to be established, the location of each facility, and the level of technology for each facility to be chosen are provided as the main determinants of a healthcare facility network. Since the developed model of the problem is of an NP-hard type, tri-meta-heuristic algorithms are proposed to solve the problem. Initially, Pareto-based meta-heuristic algorithm called multi-objective simulated annealing (MOSA) is proposed in order to solve the problem. To validate the results obtained, two popular algorithms namely, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are utilized. Since the solution-quality of all meta-heuristic algorithms severely depends on their parameters, Taguchi method has been utilized to fine tune the parameters of all algorithms. The computational results, obtained by implementing the algorithms on several problems of different sizes, demonstrate the reliable performances of the proposed methodology.

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CNC turning is widely used as a manufacturing process through which unwanted material is removed to get the high degree of surface rough. In this research article, Taguchi technique was coupled with grey relation analysis (GRA) to optimize the turning parameters for simultaneous improvement of productivity, average surface roughness (R_a), and root mean square roughness (R_q).Taguchi technique L27 (3⁴) orthogonal array was used in this experimental work. Feed, speed, and depth of cut were considered as the controllable process parameters. average roughness (R_a), root mean square roughness (R_q),and material removal rate (MRR) were considered as the performance characteristic and from TGRA result, it was revealed that the optimum combinational parameters for multi-performance, based on mean response values and confirmation experiments with Taguchi-based GRA is A1B1C1 (Vc=400 rpm, f=0.06 mm/rev, and DOC=0.5 mm). The optimum values obtained from experimental investigations for R_a was 6.86 μm, and MRR was 20690.31 mm³/s,further analysis of variance(ANOVA) were applied and it was identified that the depth of cut having most significant effect followed by speed and feed for multiresponse optimization. The percentage contribution of depth of cut was 38.28.71 %, speed was 11.89 % and feed was 8.466 %.
CNC turning is widely used as a manufacturing process through which unwanted material is removed to get a high degree of surface roughness. In this research article, Taguchi technique was coupled with grey relation analysis (GRA) to optimize the turning parameters for simultaneous improvement of productivity, the average surface roughness (R_a), and root means square roughness (R_q). Taguchi technique L27 (3⁴) orthogonal array was used in this experimental work. Feed, speed, and depth of cut were considered as the controllable process parameters. average roughness (R_a), root mean square roughness (R_q), and material removal rate (MRR) were considered as the performance characteristic and from TGRA result, it was revealed that the optimum combinational parameters for multi-performance, based on mean response values and confirmation experiments with Taguchi-based GRA is A1B1C1 (Vc=400 rpm, f=0.06 mm/rev, and DOC=0.5 mm). The optimum values obtained from experimental investigations for R_a was 6.86 μm, and MRR was 20690.31 mm³/s, further analysis of variance(ANOVA) were applied and it was identified that the depth of cut having most significant effect followed by speed and feed for multiresponse optimization. The percentage contribution of the depth of cut was 38.28.71 %, speed was 11.89 % and feed was 8.466 %.

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In the present work, a model based on dimensional analysis (DA) coupled with the Taguchi method to analyze the impact of silicon carbide (SiC) has been presented. The wire cut electrical discharge machining (WEDM) performance of aluminium silicon carbide (AlSiC) metal matrix composite (MMC) has been critically examined. To formulate the DA based models, total 18 experiments were conducted using Taguchi’s L₁₈ mixed plan of experimentation. The input data used in the DA models are a pulse on time, pulse off time, wire feed rate, % SiC, wire tension, flushing pressure etc. According to these process parameters, DA models for the surface roughness and the material removal rate was predicted. The formulated DA models have shown a strong correlation with the experimental data. The analysis of variance (ANOVA) has been used to find out the impact of individual parameters on response parameters.  
 

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The present work aims to investigate Abrasive Water Jet Machining parameters for machining of Al-Al₂O₃ Metal Matrix Composite. Plan of experiments, based on Taguchi’s analysis technique were performed using L₉ orthogonal array. A correlation was established between concentration of Al₂O₃, Stand-off distance, pressure and Transverse feed with Metal Removal Rate, Surface Roughness, Over-cut and Taper angle by regression analysis. On the basis of experimental results and S/N ratio analysis, ranking of the parameters has been done. The analysis of variance (ANOVA) has been used to find out the impact of individual parameters on response parameters. Al₂O₃ concentration plays a very significant role in determination of MRR and surface roughness. Also overcut is largely influenced by stand off distance. Furthermore,  multi-objective optimization can be carried out using advanced optimization techniques.  This work helped to generate technical database for industrial applications of MMC.

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Abstract: Wire Electrical Discharge Machining (WEDM) is a non-traditional thermal machining process used to manufacture irregularly profiled parts. Machining of ductile cast iron (ASTM A536) under several machining factors, which affect the WEDM process, is presented. The considered machining factors are pulse on time (T_on), pulse off (T_off), peak current (Ip), voltage (V), and wire speed (S). To optimize the machining factors, their setting is performed via an experimental design using the Taguchi method. The optimization objective is to achieve maximum Material Removal Rate (MRR) and minimum Surface Roughness (SR). Additionally, the analysis of variance (ANOVA) is used to identify the most significant factor. Also, a regression analysis is carried out to forecast the MRR and SR dependent on defined machining factors. Depending on consequences, the best regulation factors for reaching the maximum MRR are T_on = 32 μs, T_off = 8 μs, Ip = 4 A, S = 40 mm/min. and V = 70 volt. Whereas, the optimal control factors that achieve the minimum SR is T_on = 8 μs, T_off = 8 μs, Ip = 2 A, S = 20 mm/min, and V= 30 volt. It is hypothesized that the perfect combination of control factors that achieves minimum SR and maximum MRR is T_on = 8 μs, T_off = 8 μs, Ip=5 A, S=50 mm/min. The microstructure of the machined surface in the optimal machining conditions shows a very narrow recast layer at the top of the machined surface.