In the present day, focused commercial centers, offering exchange credit, have turned into a usually received strategy. Past inventory models under reasonable postponement in installments generally accepted that the request of the things was either consistent or simply relying on the retail cost. In this paper, we proposed to sum up, two distribution centers inventory model for crumbling things when the provider offers the retailer to defer period and thus the retailer gives to postpone period to their clients. The request design has been figured in a powerful example, which can be communicated in a straight or exponential shape. Shortages were not permitted. The differing criteria and lead time, smashing expenses were thought to be constant elements of unit cost and lead time, separately. From these, a basic iterative calculation to acquire the ideal renewal number and time booking was given. At last, numerical cases were introduced to show the model and investigation of different parameters was additionally performed. Likewise, the impact of changes in the diverse parameters in the ideal aggregate cost was graphically displayed and the suggestions were examined in details.

The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

Electrical Discharge Machining (EDM) is one of the most basic non-conventional machining processes for production of complex geometries and process of hard materials, which are difficult to machine by conventional process. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The present study is focusing on the die sinking electric discharge machining (EDM) of AISI H 13, W.-Nr. 1.2344 Grade: Ovar Supreme for finding out the effect of machining parameters such as discharge current (GI), pulse on time (POT), pulse off time (POF) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) & Overcut (OC) using Square-shaped Cu tool with Lateral flushing. A well-designed experimental scheme is used to reduce the total number of experiments. Parts of the experiment are conducted with the L9 orthogonal array based on the Taguchi methodology and significant process parameters are identified using Analysis of Variance (ANOVA). It is found that MRR is affected by gap current & Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments are determined by which factor is most affected by the responses of MRR, Ra and OC. These experimental data are further investigated using Grey Relational Analysis to optimize multiple performances in which different levels combination of the factors are ranked based on grey relational grade. The analysis reveals that substantial improvement in machining performance takes place following this technique.

Friction Stir Welding (FSW) is a relatively new solid state joining technique which is used not only for joining the aluminum and its alloys but also has potential for joining dissimilar materials with very different physical and mechanical properties which are hard to weld using conventional fusion welding processes. Tensile shear testing is used to determine the Mechanical strength of friction stir lap (FSL) welds under static loading, fracture strength (σLap) corresponding to the maximum load in a test over the sample width is widely used as the strength value. During friction stir lap welding (FSLW) of dissimilar metals with large differences in melting temperatures, a metallurgical bond is established through the formation of interfacial intermetallic compounds. However, as these intermetallic compounds are generally believed to be brittle with little ductility, they are generally considered to have detrimental effect on fracture strength. The aim of the present research is to study how the interface structure is affected by FSW parameters and how the formation of interface structure affects fracture of Al-Steel and Al-Ti FSL welds.

In today’s business scenario, organizations are focusing on sustainable growth for performance measurement in supply chain. For this purpose, sustainable flexibility is an important issue to fulfill the environmental, economic and customer’s needs, which is helpful for sustainable growth for an industry. In this paper, different factors related to sustainable flexibility in supply chain management are identified through literature review and experts’ opinions in this domain. Further, an attempt has been made to develop the interactions among these factors using Total Interpretive Structural Modeling (T-ISM) technique. This paper also employs Cross Impact Matrix Multiplication Applied to Classifications (MICMAC) based analysis to create policy to implement for sustainable growth in industries. Findings of this research give valuable decision-making insight and implications about the relative importance and the interdependence of flexibility factors in sustainable supply chain management for academicians and practicing managers in sustainable growth of an industry.

This research paper is aimed to present a fuzzy Hybrid Multi-criteria decision making (MCDM) methodology for selecting employees. The present study aspires to present the hybrid approach of Fuzzy multiple MCDM techniques with tactical viewpoint to support the recruitment process of wind turbine service technicians. The methodology is based on the application of Fuzzy ARAS (Additive Ratio Assessment) and Fuzzy MOORA (Multi-Objective Optimization on basis of Ratio Analysis) which are integrated through group decision making (GDM) method in the model for selection of wind turbine service technicians’ ranking. Here a group of experts from different fields of expertise are engaged to finalize the decision. Series of tests are conducted regarding physical fitness, technical written test, practical test along with general interview and medical examination to facilitate the final selection using the above techniques. In contrast to single decision making approaches, the proposed group decision making model efficiently supports the wind turbine service technicians ranking process. The effectiveness of the proposed approach manifest from the case study of service technicians required for the maintenance department of wind power plant using Fuzzy ARAS and Fuzzy MOORA. This set of potential technicians is evaluated based on five main criteria.

In today’s era of higher competition in the business, there are many conditions such as offered concession in bulk purchasing, seasonality, higher ordering cost, etc., which force a retailer to purchase more quantities than needed or exceed the storage capacity. So in this situation the retailer has to purchase an extra warehouse named as rented warehouse to stock the extra quantity. In this paper an inventory model for deteriorating products with selling price dependent rate is developed. The occurring shortages are assumed to be partially backlogged and cycle time is also variable. The purpose of the development of this model is to compute the amount and time of order which can optimize the total average cost of the system. A solution procedure and numerical example are presented to illustrate the implementation of the proposed study. Sensitivity analysis concerning with distinct system parameters is also presented to demonstrate the model.

For the Government projects, Value for Money (VFM) is an important parameter based on which the projects are procured through Public-Private Partnership (PPP) by the public sector. The objective of this paper is to identify and evaluate perceptions of VFM factors affecting Indian road projects undertaken by PPP. Eighteen VFM factors were shortlisted through a literature survey and were validated with experienced professionals. A questionnaire survey was conducted to investigate the most important VFM factors and was grouped into three categories: financial implications, the expertise of the private sector, and contract efficiency using factor analysis. Important VFM factors were identified using the Importance Index Method. The perception was analyzed with various statistical tools. The findings from this study provide more information and understanding about VFM in an early stage, which will be helpful for decision making about PPP.

Empirical research has identified the phenomenon of destination bonding as a result of summated physical and emotional values associated with the destination. Physical values, namely natural landscape & other physical settings and emotional values, namely the enculturation processes, have a significant role to play in portraying visitors’ cognitive framework for destination preference. The physical values seemed to be the stimulator for bonding that embodies action or behavior tendencies in imagery. The emotional values were the conditions that lead to affective bonding and are reflected in attitudes for a place which were evident in text narratives. Social networking on virtual platforms offers the scope for hybrid cognitive expression using imagery and text to the visitors. Instagram has emerged as an application-window to capture these hybrid cognitions of visitors. This study focuses on assessing the relationship between hybrid cognition of visitors expressed via Instagram and their bond with the destination. Further to this, the study attempts to examine the impact of hybrid cognition of visitors on the behavioral pattern of prospective visitors to the destination. The study revealed that sharing of visual imageries and related text by the visitors is an expression of the physico-emotional bonding with the destination. It was further established that hybrid cognition strongly asserts destination bonding and has been also found to have moderating impact on the link between destination bonding and electronic-word-of-mouth.

Maintenance Qualitn Function Deployment (MQFD) is a methodology for improving the quality and effectiveness of maintenance services in a manufacturing organization. One major part of it is House of Quality (HoQ). HoQ translates the experts’ voice into technical requirements for the improvement of maintenance quality. These data are generally vague in nature. Fuzzy numbers are generally used to represent vague data in HoQ. Since some parameters are predefined in fuzzy approach, the experts’ opinion may not be truly reflected in the HoQ analysis. In this work, a rough set - fuzzy approach, is proposed for MQFD to overcome this drawback.The objective of this model is to prioritize the technical requirements effectively with the proper reflection of customers/experts’ perceptions in the output. An illustrative example is presented to explain this approach.