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The purpose of this paper is to introduce a model of decision-making problem in multi-criteria optimization domain for project selection. The model is built by combining the soft set theory and analytic hierarchical model under fuzziness. Soft set model gives us the opportunity to use parameterization properties. Here, the authors have proved that multiple alternatives can be reduced to make the selection process computationally efficient. Here, the authors illustrate the hybrid method by means of an application of the new mathematical model of soft set theory.

This paper is designed to excel a decision support system with multiple criteria analysis tool, analytic hierarchy process combined with soft set theory under fuzziness.

In this paper, the authors have taken four projects P1, P2, P3 and P4. As per chosen parameters of softest theory the result of the illustrative example reveals that P2 is the best project. The ranking the authors get is in the order of P2, P3, P4 and P1. The algorithm leads the authors to maximize the proper choice in the environment of imprecise information. The main advantage of this method compare to others is that this hybrid method is very simple in terms of calculation and the computational complexity of the proposed algorithm is low.

This proposed decision support strategy for an intended project manager helped to take decision in the perspective environment.

Knowledge creation has received substantial attention by researchers, ever since the Socialization Externalization Combination Internalization (SECI) process was introduced. Learning Transfer System Inventory (LTSI) focuses on learning transfer and has been validated across many countries. The purpose of this paper is to explore the theoretical underpinnings between LTSI and SECI, and LTSI’s role in knowledge amplification as part of the organizational knowledge creation spiral.

The literature on organizational learning, SECI and LTSI is studied, as well as a detailed review of the LTSI constructs as it relates to SECI is completed.

Both SECI and LTSI emphasize the substantial interplay between explicit and tacit knowledge as the basis of knowledge interchange. The result of this research is a theoretical framework combining LTSI and SECI that can help understand knowledge transfer in terms of interplay of explicit and tacit knowledge and LTSI’s role in knowledge amplification as part of organizational knowledge creation.

It is proposed that LTSI can potentially be used to gauge the propensity of an organizational environment to create and transfer knowledge ontologically, as well as adopt knowledge management systems. The LTSI constructs into basic knowledge interactions (various combinations of explicit and tacit knowledge conversions) are categorized thus helping practitioners analyze the basic building blocks of learning transfer and knowledge creation. The theoretical association of the two frameworks paves the way for more empirical validation of the SECI model potentially.

This paper is an original attempt to associate SECI with LTSI.

The purpose of this paper is to develop and demonstrate an outsourcing model in which constraint resource prevents the throughput of the organization.

The paper proposes an integrated model by combining the Hurwicz criterion, the theory‐of‐constraints (TOC) and linear programming (LP) into a single evaluation model in a multi‐product constraint resource environment. A case study is presented to demonstrate the effectiveness of this model. The decision model compares four alternatives: standard cost accounting, standard TOC, LP analysis, and our own solution, which is an approach that combines TOC, LP, and the Hurwicz criterion.

The numerical results show that this model is superior and more realistically optimizes resource allocation and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This is the first time that the integrated model comprising of Hurwicz‐TOC‐LP model has been used to maximize the product throughput. Instead of calculating $ return per constraint minute, this method decides the priority of product and resource center that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing organization where one can compare the financial performance of the organization by selecting the right decision model.

The aim of this paper is to develop and demonstrate an outsourcing decision model in which constraint resource prevents the throughput of the organization.

An integrated model is proposed by incorporating the weighted product model (WPM) of AHP in estimating the priority of each product in a multi‐product constraint resource environment. A numerical example is presented to demonstrate the effectiveness of this model. The outsourcing decision model compares four alternatives: Standard cost accounting, standard theory‐of‐constraints (TOC), LP analysis and an original solution.

The numerical results show that the proposed model is superior and more realistically optimizes resource allocation and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This research is applicable to the companies which produce multi‐products in a situation in which market demand exceed the company's production capacity.

This is the first time that the WPM of AHP/TOC has been used to maximize the product throughput. Instead of calculating $return per constraint minute, it decides the priority of product that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing organization where one can compare the financial performance of the organization by selecting the right decision model.

Cost estimation based on expert's judgment is not an ideal approach, since human decisions are usually determined according to general attributes of limited and unstructured experience. The purpose of this paper is to develop a generic model of intelligence and cognitive science‐based method that can play an active role in process cost prediction within the shortest possible time.

In this paper, an intelligent system was conceived for prediction of total process cost of the product. The system is based on the concept of case‐based reasoning. It is a method for solving problems by making use of previous (source cases), similar situations and reusing information and knowledge about such situations. The source case data are generated by Taguchi technique and the cost function calculates the corresponding cost of each experiment in the economic time scale. The target case consists of the process variables whose cost needs to be determined. The cost for the source cases, consisting of the process variables of the already manufactured products are known in priori. The system calculates the similarities between the source cases and target cases and calculates the optimum cost. The fuzzy‐C‐means clustering method provides the model connecting the process parameters with total costs searched for.

The results show that the quality of predictions made by the intelligent system is comparable to the quality assured by the experienced expert. The proposed expert system is superior to traditional cost accounting system and assists inexperienced users in predicting the optimum process cost within the shortest possible time.

The research was limited to the traditional machining process.

The paper can be applied to any process industry and will have immense practical value.

This is the first time an expert system has been developed for the process industry that can calculate the process cost within a few days or a few hours before making an offer to a buyer.

The primary aim in this paper is to develop and demonstrate a theory of constraints (TOC) model in which constraint resource prevents the throughput of the organization.

In this paper, the authors propose an integrated model by combining Laplace criterion and TOC into a single evaluation model in a multiproduct constraint resource environment. A case study is illustrated to demonstrate the effectiveness of this model. The outsourcing decision model compares three alternatives: standard cost accounting, standard theory‐of‐constraints, and our own solution.

The numerical results show that the new approach is superior to Standard cost accounting and Theory of Constraints and presents a more realistic state of optimum allocation of resources and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This is the first time that the integrated model comprising of Laplace‐TOC model has been used to maximize the product throughput. Instead of calculating $return per constraint minute, this method decides the priority of product that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing Organization where one can compare the financial performance of the Organization by selecting the right decision model.

Technical education plays an important role in the development of a country in this age of knowledge economy. Indian technical education system is facing many opportunities and challenges, one of which is how to assess the performance of technical institutions based on multiple criteria. The purpose of this paper is to describe and illustrate an application of a structured approach to determine relative efficiency and ranking of a set of private engineering colleges under multi-criteria environment.

To cater to the increasing need of technical manpower, a very large number of private engineering colleges have been established in the state of West Bengal of eastern India within a very short period. Uniform and acceptable quality of the graduates from many of these private engineering colleges is a concern today and therefore the need for performance evaluation and ranking of these colleges is paramount. For the proposed framework a comparatively new multiple criteria decision-making tool, multiple objective optimization on the basis of simple ratio analysis (MOOSRA) is applied for performance evaluation of eight private engineering colleges taking into account some selected criteria. The subjective weights of the criteria are determined using fuzzy analytic hierarchy process (AHP).

For the analysis, the required data have been provided by the management of the colleges for the academic year of 2011-2012. Based on request of the management identities of these institutes are not disclosed. The institutes are considered as anonymous institute and coded as A, B, C, D, E, F, G and H, respectively. The result of the study reveals that E is the best and the ranking the authors get is in the order of E > F > A > H > D > C > G > B. The result shows that composite performance scores of institutions A, E and F are above the mean performance score value. Therefore these three institutions can be considered as the benchmark or peer group for the remaining five institutions which lie below the mean line of the performance score value.

This paper provides a comprehensive yet detailed methodology for performance evaluation of academic institutions. The novelty in the approach is that fuzzy AHP and MOOSRA are being used as a benchmarking technique in a simple methodology which is generic in nature. It is one of the few studies that evaluate the performance of technical institutions in India.

Due to liberalization, privatization and globalization, the need of competent technical manpower at an economical cost is increasing rapidly. Many foreign multinationals are focusing on India for employable talents. Many technical institutions with cutting edge technologies and leading edge techniques are being set up by foreign collaboration, national and private initiatives. The objective of this study is to propose a model for performance evaluation and benchmarking of Indian technical institutions from perspective of all stakeholders.

For the proposed framework, a multiple criteria decision‐making tool, distance‐based approach (DBA) methodology is applied for performance evaluation of seven Indian technical institutions taking into account some selected criteria like, faculty strength (FS), student intake (SI), number of PhD awarded (PhD), number of patents applied for (patent), the campus area in acres (CA) and tuition fee per semester in rupees (TF). Consulting the experts in various fields with the help of certain questionnaire and aggregating their views by conducting ameliorated nominal group technique session, we select these evaluation criteria. The subjective weights of the criteria are determined using analytic hierarchy process (AHP). For the analysis, the required data are collected from annual report published by Ministry of Human Resource and Development (MHRD) for the year of 2007‐08.

In this paper, we have chosen seven centrally funded technical institutions for study and the institutions are coded as A, B, C, D, E, F and G. The result of the study reveals that A is the best and F is the worst. The ranking we get is in the order of A≽B≽E≽C≽G≽D≽F. From the result it is understood that A can be considered as benchmark for B, C and E (which form the second group) and this second group can be considered as an improvement target for the rest. At the end a holistic technical education system model (HTESM) is proposed.

This paper is one of the few studies that evaluate the performance of technical institutions in India. The novelty in the approach is that DBA and AHP are being used as a benchmarking technique in a simple methodology which is generic in nature.

The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers. Employee profiles play a crucial role in the evaluation process to improve the training process performance. This paper focuses on the clustering of the employees based on their profiles into specific categories that represent the employees’ characteristics. The employees are classified into following categories: necessary training, required training, and no training. The work may answer the question of how to spend the budget of training for the employees. This investigation presents the use of fuzzy optimization and clustering hybrid model (data mining approaches) as a fuzzy imperialistic competitive algorithm (FICA) and k-means to find the employees’ categories and predict their training requirements.

Prior research that served as an impetus for this paper is discussed. The approach is to apply evolutionary algorithms and clustering hybrid model to improve the training decision system directions.

This paper focuses on how to find a good model for the evaluation of employee profiles. The paper introduces the use of artificial intelligence methods (fuzzy optimization (FICA) and clustering techniques (K-means)) in management. The suggestion and the recommendations were constructed based on the clustering results that represent the employee profiles and reflect their requirements during the training courses. Finally, the paper proved the ability of fuzzy optimization technique and clustering hybrid model in predicting the employee’s training requirements.

This paper evaluates employee profiles based on new directions and expands the implication of clustering view in solving organizational challenges (in TCT for the first time).

In this paper, the authors draw from the concept of a “focused factory” to examine whether a focused strategy provides superior performance over a non-focused strategy in firms experiencing service disruptions.

The authors test their hypotheses using panel data of the US domestic airline industry from 1998 to 2019.

Overall, the study findings show that a focused strategy provides superior financial performance over a non-focused strategy in both stable environments and unpredictable environments. The authors also find that the effect of service disruptions on profitability is less pronounced for firms following a focused strategy. This shows that focused firms need to grow over time to sustain profitability. Their post hoc analysis shows that for a non-focused strategy (but not for a focused strategy), firm size moderates the effect of service disruptions on profitability. This suggests that a firm pursuing a non-focused strategy can mitigate the negative effect of service disruptions by increasing its size.

This is the first study that examines the effectiveness of the focused strategy in mitigating service disruptions. The results provide further support for the effectiveness of the focused strategy in responding to service disruptions in service organizations.