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The purpose of this paper is to propose a hybrid-simulated annealing algorithm to address the lacunas in production logistics. The primary focus is laid on the basic understanding of the critical quandary occurring in production logistics, and subsequently research attempts are undertaken to resolve the issue by developing a hybrid algorithm. A logistics problem associated with a flow shop (FS) having a string of jobs which need to be scheduled on m number of machines is considered.

An attempt is made here to introduce and further establish a hybrid-simulated annealing algorithm (NEHSAO) with a new scheme for neighbourhood solutions generation, outside inverse (OINV). The competence in terms of performance of the proposed algorithm is enhanced by incorporating a fast polynomial algorithm, NEH, which provides the initial seed. Additionally, a new cooling scheme (Ex-Log) is employed to enhance the capacity of the algorithm. The algorithm is tested on the benchmark problems of Carlier and Reeves and subsequently validated against other algorithms reported in related literature.

It is clearly observed that the performance of the proposed algorithm is far superior in most of the cases when compared to the other conventionally used algorithms. The proposed algorithm is then employed to a FS under dynamic conditions of machine breakdown, followed by formulation of three cases and finally identification of the best condition for scheduling under dynamic conditions.

This paper proposes an hybrid algorithm to reduce makespan. Practical implementation of this algorithm in industries would lower the makespan and help the organisation to increse their profit

The recent global market trend is seemed enforcing existing manufacturing organizations (as well as service sectors) to improve existing supply chain systems or to take up/adapt advanced manufacturing strategies for being competitive. The concept of the agile supply chain (ASC) has become increasingly important as a means of achieving a competitive edge in highly turbulent business environments. An ASC is a dynamic alliance of member enterprises, the formation of which is likely to introduce velocity, responsiveness, and flexibility into the manufacturing system. In ASC management, supplier/partner selection is a key strategic concern. Apart from traditional supplier/partner selection criteria; different agility-related criteria/attributes need to be taken under consideration while selecting an appropriate supplier in an ASC. The paper aims to discuss these issues.

Therefore, evaluation and selection of potential supplier in an ASC have become an important multi-criteria decision making problem. Most of the evaluation criteria being subjective in nature; traditional decision-making approaches (mostly dealing with objective data) fail to solve this problem. However, fuzzy set theory appears an important mean to tackle with vague and imprecise data given by the experts. In this work, application potential of the fuzzy multi-level multi-criteria decision making (FMLMCDM) approach proposed by Chu and Velásquez (2009) and Chu and Varma (2012) has been examined and compared to that of Fuzzy-techniques for order preference by similarity to ideal solution (TOPSIS) and Fuzzy-MOORA in the context of supplier selection in ASC.

It has been observed that similar ranking order appears in FMLMCDM as well as Fuzzy-TOPSIS. In Fuzzy-MOORA, the best alternative appears same as in case of FMLMCDM as well as Fuzzy-TOPSIS; but for other alternatives ranking order differs. A comparative analysis has also been made in view of working principles of FMLMCDM, Fuzzy-TOPSIS as well as Fuzzy-MOORA.

Application feasibility of FMLMCDM approach has been verified in comparison with Fuzzy-TOPSIS and Fuzzy-MOORA in the context of agile supplier selection.

The concept of agile supply chain (ASC) has become increasingly important as means of achieving a competitive edge in turbulent business environments. An ASC is a dynamic alliance of member enterprises, the adaptation of which is likely to introduce velocity, responsiveness and flexibility into the manufacturing system. In ASC management, supplier/partner selection is a key strategic concern; influenced by various agility-related criteria/attributes. Therefore, evaluation and selection of potential supplier in an ASC has become an important multi-criteria decision-making problem. The purpose of this paper is to report, a supplier selection procedure (module) in the context of ASC.

During supplier selection, subjectivity of evaluation information (human judgment) often creates conflict and bears some kind of uncertainty. To overcome this, the present work attempts to explore vague set theory to deal with uncertainties in the supplier selection decision-making process. Since, vague sets can provide more accurate information as compared to fuzzy sets. It considers true membership function as well as false membership function which give more superior results for uncertain information. In this procedure, first, linguistic variables have been used to assess appropriateness rating (performance extent) as well as priority weights for individual quantitative or qualitative criterions. Second, the concept of degree of similarity and probability of vague sets has been used to determine appropriate ranking order of the potential supplier alternatives.

A case empirical example has been provided. It has been proved that the methodology would be fruitful in considering different evaluation criterion (indices); may be contradicting in nature like beneficial and cost criterions. The application of vague set theory has also been proved as a better option to work under uncertain (fuzzy) decision-making environment in comparison to fuzzy set theory.

The application of vague set theory in multi-criteria group decision making has been reported in literature to a limited extent. Application of vague set as a decision-making tool in agile supplier selection appears relative new and unexplored work area. The work has got remarkable managerial implications.

The purpose of this paper is to develop some 2-tuple linguistic aggregation operators based on Muirhead mean (MM), which is combined with multiple attribute group decision making (MAGDM) and applied the proposed MAGDM model for supplier selection under 2-tuple linguistic environment.

The supplier selection problem can be regarded as a typical MAGDM problem, in which the decision information should be aggregated. In this paper, the authors investigate the MAGDM problems with 2-tuple linguistic information based on traditional MM operator. The MM operator is a well-known mean type aggregation operator, which has some particular advantages for aggregating multi-dimension arguments. The prominent characteristic of the MM operator is that it can capture the whole interrelationship among the multi-input arguments. Motivated by this idea, in this paper, the authors develop the 2-tuple linguistic Muirhead mean (2TLMM) operator and the 2-tuple linguistic dual Muirhead mean (2TLDMM) operator for aggregating the 2-tuple linguistic information, respectively. Some desirable properties and special cases are discussed in detail. Based on which, two approaches to deal with MAGDM problems under 2-tuple linguistic information environment are developed. Finally, a numerical example concerns the supplier selection problem is provided to illustrate the effectiveness and feasibility of the proposed methods.

The results show that the proposed can solve the MAGDM problems within the context of 2-tuple linguistic information, in which the attributes are existing interaction phenomenon. Some 2-tuple aggregation operators based on MM have been developed. A case study of supplier selection is provided to illustrate the effectiveness and feasibility of the proposed methods. The results show that the proposed methods are useful to aggregate the linguistic decision information in which the attributes are not independent so as to select the most suitable supplier.

The proposed methods can solve the 2-tuple linguistic MAGDM problem, in which the interactions exist among the attributes. Therefore, it can be used to supplier selection problems and other similar management decision problems.

The paper develop some 2-tuple aggregation operators based on MM, and further present two methods based on the proposed operators for solving MAGDM problems. It is useful to deal with multiple attribute interaction decision-making problems and suitable to solve a variety of management decision-making applications.

The purpose of this paper is to develop a novel integrated supplier selection approach incorporating decision maker’s risk attitude using the artificial neural network (ANN), analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods.

In the proposed approach, the ANN model is used to classify decision maker’s risk attitude; the fuzzy AHP method is used to determine the relative weights of evaluation criteria; and the fuzzy TOPSIS method is used to evaluate ratings of suppliers. Finally, experiments are conducted to verify the feasibility and efficiency of the proposed integrated approach.

Experiments results show that the proposed integrated approach is effective and efficient to help decision makers to select suitable suppliers according to their risk attitudes.

The aim of this paper is to develop a novel integrated supplier selection approach incorporating decision maker’s risk attitude using the ANN, AHP and TOPSIS methods. The decision maker’s risk attitude toward procurement transaction is originally considered in supplier selection process.

The purpose of this paper is to study a modified multiplicative analytic hierarchy process (MMAHP) method, which is combined with multi-criteria decision making (MCDM) and applied MMAHP model for solving green supplier selection problem.

Supplier selection is typically a MCDM problem including both qualitative and quantitative factors that has to be taken into consideration. To select the best green suppliers with the highest potential for meeting a firm’s needs consistently, the MMAHP is utilized in this study. Then a green supplier selection problem of a well-known automobile manufacturing company in Qingdao is investigated. The authors also make a comparison of the results with that of the traditional AHP, during which the authors observe that the MMAHP is an effective approach for the considered problem and potential rank reversals can be avoided, that is, when a new supplier is added, the ranking of suppliers does not change and maintains its original relative ratio.

A numerical example of green supplier selection is utilized to verify the proposed approach. The results show that the MMAHP is an effective approach for the considered problem and potential rank reversals can be avoided.

The proposed approach can be used to solving green supplier selection problems and can avoid the rank reversal.

The paper introduces the MMAHP method to help researchers to choose more effective approach for green supplier selection.

Proposing an improved model for evaluating criticality of non-value added (waste) in operation is necessary for realizing sustainable manufacturing practices. The purpose of this paper is concerning on improvement of the decision support model for evaluating risk criticality lean waste occurrence by considering the weight of modified FMEA indices and the influence of waste-worsening factors causing the escalation of waste risk magnitude.

Integration of entropy and Taguchi loss function into decision support model of modified FMEA is presented to rectify the limitation of previous risk reprioritization models in modified FMEA studies. The weight of the probability components and loss components are quantified using entropy. A case study from industry is used to test the applicability of the integration model in practical situation.

The proposed model enables to overcome the limitations of using subjective determination on the weight of modified FMEA indices. The inclusion of the waste-worsening factors and Taguchi loss functions enables the FMEA team to articulate the severity level of waste consequences appropriately over the use of ordinal scale in ranking the risk of lean waste in modified FMEA references.

When appraising the risk of lean waste criticality, ignorance on weighting of FMEA indices may be inappropriate for an accurate risk-based decision-making. This paper provides insights to scholars and practitioners and others concerned with the lean operation to understand the significance of considering the impact of FMEA indices and waste-worsening factors in evaluating criticality of lean waste risks.

The method adopted is for quantifying the criticality of lean waste and inclusion of weighting of FMEA indices in modified FMEA provides insight and exemplar on tackling the risk of lean waste and determining the most critical waste affecting performability of company operations.

Integration of the entropy and Taguchi loss function for appraising the criticality of lean waste in modified FMEA is the first in the lean management discipline. These findings will be highly useful for professionals wishing to implement the lean waste reduction strategy.

Successful device development brings substantial revenues to medical device manufacturing industries. This paper aims to evaluate factors contributing to the success of medical device development (MDD) using grey DEMATEL (decision-making trial and evaluation laboratory) methodology through an empirical case study.

The factors are identified through literature review and industry experts’ opinions. Grey-based DEMATEL methodology is used to establish the cause-effect relationship among the factors and develop a structured model. Most significant factors contributing to the success of MDD are identified. An empirical case study of an MDD and manufacturing organisation is presented to demonstrate the use of the grey DEMATEL method. Sensitivity analysis is carried out to check robustness of results.

The results of applying the grey DEMATEL methodology to evaluate success factors of MDD show that availability of experts and their experience (SF4) is the most prominent cause factor, and active involvement of stakeholders during all stages of MDD (SF3) and complete elicitation of end-user requirements (SF1) are the most prominent effect factors for successful MDD. A sensitivity analysis confirms the reliability of the initial solution.

The findings will greatly help medical device manufacturers to understand the success factors and develop strategies to conduct successful MDD processes.

In the past, few success factors to MDD have been identified by some researchers, but complex inter-relationships among factors are not analysed. Finding direct and indirect effects of these factors on the success of MDD can be a good future research proposition.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power.

The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill.

The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results.

The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.