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The purpose of this paper is to develop an agility evaluation approach to determine the most suitable agile system for implementing mass customization (MC) strategies. Evaluating the alternatives and comparing across them, the best practices of the efficient organization can be identified and transferred to different organizations.

Grey relation approach is a simple mathematical technique useful in situations where the information is not known precisely. Grey relation approach has been applied to measure the agility of various organizations based on agile entities and accordingly the organizations are ranked. The ranking so obtained is compared with the ranking obtained by a popular multi‐attribute decision making (MADM) process known as Fuzzy TOPSIS (technique for order preference by similarity to ideal solution) to test the robustness of the proposed method. It is to be noted that grey theory considers the condition of the fuzziness and can deal flexibly with the fuzziness situation.

It is demonstrated that the grey approach is an appropriate method for solving MADM problems in an uncertain situation with less computational efforts. The alternatives can easily be benchmarked and the best agile system can be selected. As the ranking obtained through grey relation approach closely agree with the ranking found from Fuzzy TOPSIS method, the robustness of the proposed approach is validated. Both the methods lead to choosing a suitable agile system related to mass customization.

In this paper, the proposed approach has been compared with Fuzzy TOPSIS method to test the robustness of the method. Other MADM approaches may be used for comparison purpose to gain insight into the methodology of the proposed approach.

An alternative approach for MADM is proposed to obtain good decisions in an uncertain environment and used for agility evaluation in selected organizations. As agile manufacturing is relatively a new concept, certain and complete information on systems are not available. In such situations, the proposed method can deal with the issue conveniently and results in workable solutions.

This paper aims to resolve the uncertain problem in suppliers selection chain management system through using the proposed multiple attribute decision‐making (MADM) approach.

The approach which combines grey system theory with rough set theory is proposed.

This proposed approach take advantage of mathematical analysis power of grey system theory and at the same time take advantage of data mining and knowledge discovery power of rough set theory. It will be suitable to decision making under a more uncertain environment.

Provides a viewpoint on the attribute values and attribute weights of rough set decision table for all alternatives are decided by grey number based on grey system theory. The best ideal supplier can be decided by grey relational analysis based on grey number.

Facility location decisions are critical and should be taken after strategic evaluations. Globalization and integration of economies make such decisions further complex and risk prone. The purpose of this paper is to identify and assess the risk factors to be considered while taking new facility location decision associated with global supply chain and device the methodology. A grey-based multi-criteria decision-making (MCDM) approach is used for this purpose, which also takes in to account the uncertainty in decision making. Such approach enables final decision to be more real and practical. The paper also highlighted and discussed the criteria on the basis of which the management can select the best suitable site.

The risk factors related to facility location for a global firm are identified. To select the location of a global facility with least risk, grey-based MCDM approach is formulated. This grey-based MCDM is demonstrated using the hypothetical case of an industrial valve manufacturing global firm. The grey approach is used to analyse location alternatives based on various decision criteria for extracting comparative ranking.

The paper presents a tool for strategic and planning level. It helps supply chain managers to identify the risks related to a candidate location. Then it guides the supply chain manager at strategic level to find the least risky location for a manufacturing facility.

This paper demonstrates the grey-based MCDM approach for determining less risky location to locate a new manufacturing unit so that practitioners can use this approach for taking other strategic decisions. The supply chain configuration can be decided subsequently which will yield more practical results and the decision taken will be more fruitful for firm.

The extensive literature review reveals that there are many models in the literature that addressed the issue of risk minimization in supply chain, but it was also noticed that there are limited number of models that minimize risk in locating a global facility considering the uncertainty of data in decision making. This is the first time that grey-based MCDM approach is formulated and used to find most suitable facility location under risk.

In the current customer-driven market, the manufacturers have to be highly responsive and flexible to deliver a variety of products. Hence, to meet this dynamic and uncertain market changes, the production system, which enables the manufacturing of such variety of products should be able to meet such diverse, dynamic changes. Hence, selecting a suitable manufacturing system is a key strategic decision for today's manufacturing organization, which needs to survive in these uncertain market conditions. Hence, the purpose of this paper is to present a decision-making model for selecting the best manufacturing system and also discuss the criteria on the basis of which the management can select the same.

A case of small- and medium-sized company is presented, in which the management is deciding to establish a most suitable manufacturing system. To supplement this, a suitable multi-criteria decision-making model (MCDM), the grey approach is used to analyze manufacturing system alternatives based on various decision criteria to arrive a comparative ranking.

An extensive analysis of grey-based decision-making model described grey decision matrix, grey normalized decision matrix, grey weighted normalized decision matrix and grey possibility degrees for three alternatives revealed that lean manufacturing systems was found to be the most suitable manufacturing system among three alternatives for a given case.

The same study can be extended by including sub-criteria with main criteria for selection of manufacturing system by utilizing two MCDM techniques such as AHP or ANP with Grey approach.

The Grey approach has been discussed in a detailed way and it will be useful for the managers to use this approach as a tool for solving similar type of decision-making problems in their organizations in the future.

Although, the problem of selecting a suitable manufacturing system is often addressed both in practice and research, very few reports are available in the literature of Grey-based decision models that demonstrated its application for selecting a suitable manufacturing systems.

In present research, the authors conducted the massive literature review and collected the information, in regards to material handling system (MHS) to build a multi criteria MHS hierarchical module consists of ecological cum fiscal criteria. Moreover, similar literature review assisted the authors to resolve and eventually construct the effectual and robust approach. The purpose of this paper is to facilitate the managers for benchmarking the MHS alternatives operating under similar module via robust decision support system (DSS).

In present research, the proposed module dealt with ecological (subjective) and fiscal (objective) criteria, where subjective criteria associated with incompleteness, vagueness, imprecision, as well as inconsistency, solicited the discrete information in terms of Grey set via linguistic scale from experts panel. The objective information (capital) has been assigned by expert’s panel in terms of Grey set. To robustly evaluate and select the admirable MHS, three approaches named: degree of possibility, technique for order preference similar to ideal solution as well as Grey relational analysis fruitfully applied to connect and unite discrete information.

The performance evaluation of MHSs has been carried out under concert of individual fiscal criteria excluding ecological criteria in past researches. Moreover the previous developed DSS tackled sole approach under individual fiscal criteria. The authors found the broad applications of fuzzy sets except Grey set theory in the same context for measuring the performance of MHS alternatives. Aforesaid research gaps have been transformed into research objectives by incorporating the module for both fiscal cum ecological criteria. This research embraces a robust DSS, which has been explored to select the admirable MHS alternative.

An empirical case study has been carried out in order to demonstrate the legitimacy of holistic Grey-MCDM method, implemented over multi criteria MHS hierarchical module. Proposed DSS seems to be the best for organisations, which believe to appraise and select the MHS including fiscal as well as ecological criteria excluding individual fiscal criteria. Moreover, subjective cum objective or individual subjective or objective criteria can be extended with respect to varieties of MHSs.

The purpose of this paper is to survey and express the advantages and disadvantages of the existing approaches for solving grey linear programming in decision-making problems.

After presenting the concepts of grey systems and grey numbers, this paper surveys existing approaches for solving grey linear programming problems and applications. Also, methods and approaches for solving grey linear programming are classified, and its advantages and disadvantages are expressed.

The progress of grey programming has been expressed from past to present. The main methods for solving the grey linear programming problem can be categorized as Best-Worst model, Confidence degree, Whitening parameters, Prediction model, Positioned solution, Genetic algorithm, Covered solution, Multi-objective, Simplex and dual theory methods. This survey investigates the developments of various solving grey programming methods and its applications.

Different methods for solving grey linear programming problems are presented, where each of them has disadvantages and advantages in providing results of grey linear programming problems. This study attempted to review papers published during 35 years (1985–2020) about grey linear programming solving and applications. The review also helps clarify the important advantages, disadvantages and distinctions between different approaches and algorithms such as weakness of solving linear programming with grey numbers in constraints, inappropriate results with the lower bound is greater than upper bound, out of feasible region solutions and so on.

Stainless steel is widely used in different manufacturing sectors. The purpose of this study is to optimize the process parameters of machining while processing SS316L alloy. The optimization of machining characteristics in the case of SS316L alloy greatly improves the quality and productivity economically.

The machining variables in current research are depth of cut, spindle speed and feed rate. The optimization of response characteristics was carried out using the intelligent approach of grey, regression and teaching learning-based optimization (TLBO) and Taguchi-Grey approach. Planning of experiments was made using Taguchi’s based L27 orthogonal array. With the implementation of grey, the response characteristics were normalized and converted into a single response. The regression analysis was used for empirical modeling of the single response induced from the grey application. TLBO is further used to investigate the combinations of machining variables and compared with grey theory.

The grey-TLBO based multi-criteria decision-making approach suggests that the optimized setting for material removal rate, mean roughness depth (Rz) and cutting force (Fz) is spindle speed (N): 720 rpm; feed rate (F): 0.3 mm/rev; depth of cut (DoC): 1.7 mm. The grey theory suggests an optimized setting as N: 720 rpm; F: 0.2 mm/rev and DoC: 1.7 mm.

The parametric optimization during the turning of SS316L using grey-TLBO based intelligent approach is not performed till now. Thus, this intelligent approach will give a path to the researchers working in this direction. However, the grey theory performs better as compared to the grey-TLBO approach.

The oil and gas industry is a crucial economic sector for both developed and developing economies. Delays in extraction and refining of these resources would adversely affect industrial players, including that of the host countries. Supplier selection is one of the most important decisions taken by managers of this industry that affect their supply chain operations. However, determining suitable suppliers to work with has become a phenomenon faced by these managers and their organizations. Furthermore, identifying relevant, critical and important criteria needed to guide these managers and their organizations for supplier selection decisions has become even more complicated due to various criteria that need to be taken into consideration. With limited works in the current literature of supplier selection in the oil and gas industry having major methodological drawbacks, the purpose of this paper is to develop an integrated approach for supplier selection in the oil and gas industry.

To address this problem, this paper proposes a new uncertain decision framework. A grey-Delphi approach is first applied to aid in the evaluation and refinement of these various available criteria to obtain the most important and relevant criteria for the oil and gas industry. The grey systems theoretic concept is adopted to address the subjectivity and uncertainty in human judgments. The grey-Shannon entropy approach is used to determine the criteria weights, and finally, the grey-EDAS (evaluation based on distance from average solution) method is utilized for determining the ranking of the suppliers.

To exemplify the applicability and robustness of the proposed approach, this study uses the oil and gas industry of Iran as a case in point. From the literature review, 21 criteria were established and using the grey-Delphi approach, 16 were finally considered. The four top-ranked criteria, using grey-Shannon entropy, include warranty level and experience time, relationship closeness, supplier’s technical level and risks which are considered as the most critical and influential criteria for supplier evaluation in the Iranian oil and gas industry. The ranking of the suppliers is obtained, and the best and worst suppliers are also identified. Sensitivity analysis indicates that the results using the proposed methodology are robust.

The proposed approach would assist supply chain practicing managers, including purchasing managers, procurement managers and supply chain managers in the oil and gas and other industries, to effectively select suitable suppliers for cooperation. It can also be used for other multi-criteria decision-making (MCDM) applications. Future works on applying other MCDM methods and comparing them with the results of this study can be addressed. Finally, broader and more empirical works are required in the oil and gas industry.

This study is among the first few studies of supplier selection in the oil and gas industry from an emerging economy perspective and sets the stage for future research. The proposed integrated grey-based MCDM approach provides robust results in supplier evaluation and can be used for future domain applications.

Decision making is the task of selecting the most appropriate alternative among a finite set of possible alternatives with respect to some attributes. The attributes may be subjective or objective (or combination of both), depending upon the situation; requirements may also be conflicting. In practice, most of the real-world decision-making problems are based on subjective evaluation criteria which are basically ill-defined and vague. Since subjective human judgment bears ambiguity and vagueness in the decision making; application of grey numbers set theory may be proved fruitful in this context. The paper aims to discuss these issues.

Owing to the advantages of grey numbers set theory in tackling subjectivity in decision making; the crisp-TODIM needs to be extended by integrating with grey numbers set theory in order to facilitate decision making consisting of subjective data. Hence, the unified objective of this paper is to propose a grey-based TODIM approach in the context of decision making.

Application potential of grey-TODIM has been demonstrated through a case empirical robot selection problem. Result obtained thereof, has also been compared to that of existing grey-based decision support systems available in literature.

Application potential of grey-based decision support systems (grey-TOPSIS, grey analysis, grey-MOORA) have been highlighted in available literature resource. However, the shortcoming of these approaches is that they do not consider decision-makers’ risk attitude while decision making. TODIM method is derived from the philosophy of Cumulative Prospect Theory (CPT) which considers risk averting attitude of the decision maker in case of gain and risk seeking attitude in case of loss, while comparing dominance between two alternatives with respect to a particular criterion. Hence, this paper contributes a mathematical foundation of TODIM coupled with grey numbers set theory for logical decision making.

Advanced manufacturing machines (AMMs) are searched as a momentous asset across the manufacturing societies for quenching and addressing the production units under economical circumstances, i.e. production of high-quality of goods under feasible cost. AMMs are significant in holding the managers against their rivals and competitors with high profit margins. The authors developed the decision support mechanism/portfolio (DSM-P) consist of knowledge-based cluster approach with a dynamic model. The purpose of research work is to measure overall economic worth of AMMs under objective and grey-imperfect (mixed) data by exploring the proposed DSM-P.

The authors developed the DSM-P that consist of knowledge-based cluster, three multi-criteria decision-making (MCDM) techniques-1-2-3 with complementary grey relational analysis-4(GRA), approach with a dynamic model (complied by technical plus cost and agility measures of AMMs). The proposed DSM-P enables the manager to map the overall economic worth of candidate AMMs under objective and grey-mixed data.

The presented DSM-P assist the managers for handling the selection problem of AMMs, i.e. CNCs, robots, automatic-guided vehicle, etc under mixed (objective cum grey) data. To enable the readers for intensely understand the work, the utility of proposed approach is displayed by illustrating a polar robot evaluation and selection problem. It is ascertained that the robot candidate-11 alternative is fulfilling the entire technical cum cost and agility measures.

The DSM-P provides more precise and reliable outcomes due to a usage of the dominance theory. Under the dominance theory, the ranks are obtained by MCDM techniques-1-2-3 are compared with ranks gathered by the GRA-4 under objective cum grey data, formed the novelties in presented research work. From a future perspective, the grey-based models in DSM-P can be built/extended/constructed more extensive and can be simulated by the same approach.