Since independence, India has been one of the few developing countries to invest extensively in both science and technical education. In India, technical education plays a pivotal role in human resource development while creating skilled manpower, increasing industrial productivity and enhancing the quality of life. If a technical institute means to be effective in developing learned and qualified engineers, then it would be useful to know the performance of that technical institution. However, measuring the performance of a technical institution has received very little attention because it is very difficult to measure its output. Thus, this paper focuses on assessing the performance of eight Indian Institutes of Technology (IITs) using a combined approach of data envelopment analysis (DEA) and technique of order preference by similarity to ideal solution (TOPSIS). In the first phase, DEA is applied to shortlist the efficient IITs having the desired characteristics from the stakeholders’ point of view, and TOPSIS method is then employed to rank those efficient IITs while also identifying the best performing IIT. It is observed that IIT Kharagpur outperforms all the considered IITs which exactly corroborates with the findings of the recently published surveys/reports.

Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance.

With the ever-increasing demands for high surface finish and complex shape geometries, conventional metal removal methods are now being replaced by non-traditional machining (NTM) processes. These NTM processes use energy in its direct form to remove materials in the form of atoms or molecules to obtain the required accuracy and burr-free machined surface. In order to exploit the optimal capabilities of the NTM processes, it is often required to determine the best possible combinations of their controllable parameters. Different non-conventional optimization techniques have been used for dealing with these process optimization problems because of their inherent advantages and capabilities for arriving at the almost global optimal solutions. This paper reviews the applications of different non-conventional optimization techniques for parametric optimization of NTM processes. It is observed that electrical discharge machining processes have been optimized most number of times, followed by wire electrical discharge machining processes. In most of the cases, the past researchers have preferred to maximize material removal rate. Genetic algorithm has been found to be the most popular non-conventional optimization technique.

Activity based costing (ABC) is a new age accounting system developed to overcome the limitations of traditional costing system, which in today’s technologically advanced world has lost its ability to provide accurate and useful cost information often required in taking various managerial decisions, like setting of selling price, ascertaining profitability of products as well as customers, taking a make or buy decision, eliminating the non-beneficial activities, and rewarding the employees. In this paper, an ABC model is developed for a government hospital in the state of Bihar in India to quantify its various public health related services. Although, there are limited published research papers related to application of ABC model in a specific department or method of a healthcare system, none of them has investigated its scope for organization-wide implementation. The results obtained from the developed ABC model in the public hospital would provide more comprehensive and accurate cost information, and radically contribute to the hospital administration in effective planning, controlling and decision making. This paper also elucidates about various obstructions and difficulties encountered during the implementation phase of ABC model in an Indian healthcare setting.

With ever increasing demand for manufactured products of hard alloys and metals with high surface finish and complex shape geometry, more interest is now being paid to non-traditional machining (NTM) processes, where energy in its direct form is used to remove material from workpiece surface. Compared to conventional machining processes, NTM processes possess almost unlimited capabilities and there is a strong believe that use of NTM processes would go on increasing in diverse range of applications. Presence of a large number of NTM processes along with complex characteristics and capabilities, and lack of experts in NTM process selection domain compel for development of a structured approach for NTM process selection for a given machining application. Past researchers have already attempted to solve NTM process selection problems using various complex mathematical approaches which often require a profound knowledge in mathematics/artificial intelligence from the part of process engineers. In this paper, four NTM process selection problems are solved using an integrated PROMETHEE (preference ranking organization method for enrichment evaluation) and GAIA (geometrical analysis for interactive aid) method which would act as a visual decision aid to the process engineers. The observed results are quite satisfactory and exactly match with the expected solutions.

Industrial robots are mainly employed to perform repetitive and hazardous production jobs, multi-shift operations etc. to reduce the delivery time, improve the work environment, lower the production cost and even increase the product range to fulfill the customers’ needs. When a choice is to be made from among several alternative robots for a given industrial application, it is necessary to compare their performance characteristics in a decisive way. As the industrial robot selection problem involves multiple conflicting criteria and a finite set of candidate alternatives, different multi-criteria decision-making (MCDM) methods can be effectively used to solve such type of problem. In this paper, ten most popular MCDM methods are considered and their relative performance are compared with respect to the rankings of the alternative robots as engaged in some industrial pick-n-place operation. It is observed that all these methods give almost the same rankings of the alternative robots, although the performance of WPM, TOPSIS and GRA methods are slightly better than the others. It can be concluded that for a given industrial robot selection problem, more attention is to be paid on the proper selection of the relevant criteria and alternatives, not on choosing the most appropriate MCDM method to be employed.

The recent development of world is being adversely affected by the scarcity of power and energy. To survive in the next generation, it is thus necessary to explore the non-conventional energy sources and efficiently consume the available sources. For efficient exploitation of the existing energy sources, a great scope lies in the use of Rankin cycle-based thermal power plants. Today, the gross efficiency of Rankin cycle-based thermal power plants is less than 28% which has been increased up to 40% with reheating and regenerative cycles. But, it can be further improved up to 47% by using supercritical power plant technology. Supercritical power plants use supercritical boilers which are able to withstand a very high temperature (650-720?C) and pressure (22.1 MPa) while producing superheated steam. The thermal efficiency of a supercritical boiler greatly depends on the material of its different components. The supercritical boiler material should possess high creep rupture strength, high thermal conductivity, low thermal expansion, high specific heat and very high temperature withstandability. This paper considers a list of seven supercritical boiler materials whose performance is evaluated based on seven pivotal criteria. Given the intricacy and difficulty of this supercritical boiler material selection problem having interactions and interdependencies between different criteria, this paper applies fuzzy analytic network process to select the most appropriate material for a supercritical boiler. Rene 41 is the best supercritical boiler material, whereas, Haynes 230 is the worst preferred choice.

Selection of material for a specific engineering component, which plays a significant role in its design and proper functioning, is often treated as a multi-criteria decision-making (MCDM) problem where the most suitable material is to be chosen based on a given set of conflicting criteria. For solving these MCDM problems, the designers do not generally know what should be the optimal number of criteria required for arriving at the best decisive action. Those criteria should be independent to each other and their number should usually limit to seven plus or minus two. In this paper, five material selection problems are solved using three common MCDM techniques to demonstrate the effect of number of criteria on the final rankings of the material alternatives. It is interesting to observe that the choices of the best suited materials solely depend on the criterion having the maximum priority value. It is also found that among the three MCDM methods, the ranking performance of VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) method is the best.

In today’s global and dynamic business environment, manufacturing organizations face the tremendous challenge of expanding markets and meeting the customer expectations. It compels them to lower total cost in the entire supply chain, shorten throughput time, reduce inventory, expand product choice, provide more reliable delivery dates and better customer service, improve quality, and efficiently coordinate demand, supply and production. In order to accomplish these objectives, the manufacturing organizations are turning to enterprise resource planning (ERP) system, which is an enterprise-wide information system to interlace all the necessary business functions, such as product planning, purchasing, inventory control, sales, financial and human resources into a single system having a shared database. Thus to survive in the global competitive environment, implementation of a suitable ERP system is mandatory. However, selecting a wrong ERP system may adversely affect the manufacturing organization’s overall performance. Due to limitations in available resources, complexity of ERP systems and diversity of alternatives, it is often difficult for a manufacturing organization to select and install the most suitable ERP system. In this paper, two ERP system selection problems are solved using fuzzy multi-objective optimization on the basis of ratio analysis (MOORA) method and it is observed that in both the cases, SAP is the best solution.