he VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje which means multi-criteria optimization and compromise solution, in Serbian) method has already become a quite popular multi-criteria decision making tool for its computational simplicity and solution accuracy. This method focuses on selecting and ranking from a set of feasible alternatives, and determines compromise solution for a problem with conflicting criteria to help the decision maker in reaching a final course of action. It determines the compromise ranking list based on the particular measure of closeness to the ideal solution. Depending upon the type of decision problem and necessity of the decision maker, apart from VIKOR method, different variants of it, like comprehensive VIKOR, fuzzy VIKOR, regret theory-based VIKOR, modified VIKOR and interval VIKOR methods have also been subsequently developed. In this paper, the ranking performance of original VIKOR method and its five variants is analyzed based on two demonstrative examples. It is observed that interval VIKOR method performs unsatisfactorily and when the information in a decision problem is imprecise, fuzzy VIKOR method should always be preferred. But, for any decision problem, original VIKOR is the best method for solution without unnecessarily complicating the related mathematical computations.
The paper deals with the performance analysis and optimization for Carbonated Soft Drink Glass Bottle (CSDGB) filling system of a beverage plant using Particle Swarm Optimization (PSO) approach. The CSDGB system consists of seven main subsystems arranged in series namely Uncaser, Bottle Washer, Electronic Inspection Station, Filling Machine, Crowner, Coding Machine and Case Packer. Considering exponential distribution for probable failures and repairs, mathematical modeling is performed using Markov Approach (MA). The differential equations have been derived on the basis of probabilistic approach using transition diagram. These equations are solved using normalizing condition and recursive method to drive out the steady state availability expression of the system i.e. system’s performance criterion. The performance optimization of system has been carried out by varying the number of particles and number of generations. It has been observed that the maximum availability of 90.27% is achieved at flock size of 55 and 90.84% at 300th generation. Thus, findings of the paper will be useful to the plant management for execution of proper maintenance decisions.
An appropriate sequence of assembly operations increases the productivity and enhances product quality there by decrease the overall cost and manufacturing lead time. Achieving such assembly sequence is a complex combinatorial optimization problem with huge search space and multiple assembly qualifying criteria. The purpose of the current research work is to develop an intelligent strategy to obtain an optimal assembly sequence subjected to the assembly predicates. This paper presents a novel hybrid artificial intelligent technique, which executes Artificial Immune System (AIS) in combination with the Genetic Algorithm (GA) to find out an optimal feasible assembly sequence from the possible assembly sequence. Two immune models are introduced in the current research work: (1) Bone marrow model for generating possible assembly sequence and reduce the system redundancy and (2) Negative selection model for obtaining feasible assembly sequence. Later, these two models are integrated with GA in order to obtain an optimal assembly sequence. The proposed AIS-GA algorithm aims at enhancing the performance of AIS by incorporating GA as a local search strategy to achieve global optimum solution for assemblies with large number of parts. The proposed algorithm is implemented on a mechanical assembly composed of eleven parts joined by several connectors. The method is found to be successful in achieving global optimum solution with less computational time compared to traditional artificial intelligent techniques.
This paper suggests an advanced hybrid multi output optimization technique by applying weighted principal component analysis (WPCA) incorporated with response surface methodology (RSM). This investigation has been carried out through a case study in CNC turning of Aluminum alloy 63400 for surface roughness (Ra) and tool vibration (db) optimization. Primarily, input parameters such as spindle speed (N), feed rate (S) and depth of cut (t) are designed for experiment by using RSM Box-Behnken methodology. The aluminum alloy workpieces are machined by using coated carbide tool (inserts) in dry environment. Secondly, the empirical model for the responses as the functions of cutting parameters are obtained through RSM technique and the adequacy of the models have been checked using analysis of variance (ANOVA). Finally, the process parameters are optimized using WPCA technique. The confirmatory experiment has been performed using optimized result and it reveals that multiple response performance index (MPI) value was increased by 0.2908 from initial setting. The increases in MPI value indicates that the aforesaid optimization methodology is suitably acceptable for multi response optimization for turning process.
A note published by Chinta et al. (2016) [Chinta, S., Kommadath, R. & Kotecha, P. (2016) A note on multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO). Information Science, 373, 337-350.] reported some impediments in implementation of MO-ITLBO algorithm. However, it is observed that their comments are based on incorrect understanding of TLBO, ITLBO and MO-ITLBO algorithms. Their raised issues are thoroughly addressed in this paper and it is proved that MO-ITLBO algorithm has no lacunae.
The objective of the present work is to use a suitable method that can optimize the process parameters like pulse on time (TON), pulse off time (TOFF), wire feed rate (WF), wire tension (WT) and servo voltage (SV) to attain the maximum value of MRR and minimum value of surface roughness during the production of a fine pitch spur gear made of copper. The spur gear has a pressure angle of 20⁰ and pitch circle diameter of 70 mm. The wire has a diameter of 0.25 mm and is made of brass. Experiments were conducted according to Taguchi’s orthogonal array concept with five factors and two levels. Thus, Taguchi quality loss design technique is used to optimize the output responses carried out from the experiments. Another optimization technique i.e. desirability with grey Taguchi technique has been used to optimize the process parameters. Both the optimized results are compared to find out the best combination of MRR and surface roughness. A confirmation test was carried out to identify the significant improvement in the machining performance in case of Taguchi quality loss. Finally, it was concluded that desirability with grey Taguchi technique produced a better result than the Taguchi quality loss technique in case of MRR and Taguchi quality loss gives a better result in case of surface roughness. The quality of the wire after the cutting operation has been presented in the scanning electron microscopy (SEM) figure.
Turning experiments were conducted on a novel aluminum alloy (LM6)/fly ash composite based on the response surface and face centered central composite design methodology. The effects of cutting parameters on surface roughness and tool wear were investigated. Multiple regression models were developed for the responses and the adequacies of the developed models were tested at 95% confidence interval using the analysis of variance (ANOVA) technique. Carbide inserts (Model: CNMG 120408-M5) were used for turning the specimens in a CNC turning machine (model: LT-16). The test for significance of the regression models, the test for significance on individual model coefficients and the lack-of-fit tests were performed using the statistical Design-Expert7.0v software environments. R2 indicated the model significance and the value was more than 97%, revealed that the relation between cutting responses and input parameters held good for more than 97% and the model was adequate.
The present study investigates performance and feasibility of application of low cost cemented carbide insert in dry machining of AISI 52100 steel hardened to (55 ± 1 HRC) which is rarely researched as far as machining of bearing steel is concerned. Machinability studies i.e. flank wear, surface roughness and morphology analysis of chip has been investigated and statistical regression modeling has been developed. The test has been conducted based on Taguchi L16 OA taking machining parameters like cutting speed, feed and depth of cut. It is observed that uncoated cemented carbide insert performs well at some selected runs (Run 1, 5 and 9) which show its feasibility for hard turning applications. The developed serrated saw tooth chip of burnt blue colour adversely affects the surface quality. Adequacy of the developed statistical regression model has been checked using ANOVA analysis (depending on F value, P value and R2 value) and normal probability plot at 95% confidence level. The results of optimal parametric combinations may be adopted while turning hardened AISI 52100 steel under dry environment with uncoated cemented carbide insert.
Quality decisions are one of the major decisions in inventory management. It affects customer’s demand, loyalty and customer satisfaction and also inventory costs. Every manufacturing process is inherent to have some chance causes of variation which may lead to some defectives in the lot. So, in order to cater the customers with faultless products, an inspection process is inevitable, which may also be prone to errors. Thus for an operations manager, maintaining the quality of the lot and the screening process becomes a challenging task, when his objective is to determine the optimal order quantity for the inventory system. Besides these operational tasks, the goal is also to increase the customer base which eventually leads to higher profits. So, as a promotional tool, trade credit is being offered by both the retailer and supplier to their respective customers to encourage more frequent and higher volume purchases. Thus taking into account of these facts, a strategic production model is formulated here to study the combined effects of imperfect quality items, faulty inspection process, rework process, sales return under two level trade credit. The present study is a general framework for many articles and classical EPQ model. An analytical method is employed which jointly optimizes the retailer’s credit period and order quantity, so as to maximize the expected total profit per unit time. To study the behavior and application of the model, a numerical example has been cited and a comprehensive sensitivity analysis has been performed. The model can be widely applicable in manufacturing industries like textile, footwear, plastics, electronics, furniture etc.
The uncertainty in the supply chains (SCs) for manufacturing and services firms is going to be, over the coming decades, more important for the companies that are called to compete in a new globalized economy. Risky situations for manufacturing are considered in trying to individuate the optimal positioning of the order penetration point (OPP). It aims at defining the best level of information of the client’s order going back through the several supply chain (SC) phases, i.e. engineering, procurement, production and distribution. This work aims at defining a system dynamics model to assess competitiveness coming from the positioning of the order in different SC locations. A Taguchi analysis has been implemented to create a decision map for identifying possible strategic decisions under different scenarios and with alternatives for order location in the SC levels. Centralized and decentralized strategies for SC integration are discussed. In the model proposed, the location of OPP is influenced by the demand variation, production time, stock-outs and stock amount. Results of this research are as follows: (i) customer-oriented strategies are preferable under high volatility of demand, (ii) production-focused strategies are suggested when the probability of stock-outs is high, (iii) no specific location is preferable if a centralized control architecture is implemented, (iv) centralization requires cooperation among partners to achieve the SC optimum point, (v) the producer must not prefer the OPP location at the Retailer level when the general strategy is focused on a decentralized approach.