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The aim of this paper is to present a method for finding the optimum balance between sequence-dependent setup costs, holding costs, delivery costs and delay penalties in an integrated production–distribution system with lot sizing decisions.

Two mixed integer linear programming models and an optimality property are proposed for the problem. Since the problem is NP-hard, a genetic algorithm reinforced with a heuristic is developed for solving the model in large-scale settings. The algorithm parameters are tuned using the Taguchi method.

The results obtained on randomly generated instances reveal a performance advantage for the proposed algorithm; it is shown that lot sizing can reduce the average cost of the supply chain up to 11.8%. Furthermore, the effects of different parameters and factors of the proposed model on supply chain costs are examined through a sensitivity analysis.

Although integrated production and distribution scheduling in make-to-order industries has received a great deal of attention from researchers, most researchers in this area have treated each order as a job processed in an uninterrupted time interval, and no temporary holding costs are assumed. Even among the few studies where temporary holding costs are taken into consideration, none has examined the effect of splitting an order at the production stage (lot sizing) and the possibility of reducing costs through splitting. The present study is the first to take holding costs into consideration while incorporating lot sizing decisions in the operational production and distribution problem.

A perfect knowledge management (KM) initiative is one that achieves its objectives without any failure during a pre-defined period. However, KM implementation is not perfect in every organization as it requires substantial changes in organizational infrastructures, including culture, structure, and technology. Therefore, the purpose of this paper is to propose a model for assessing the reliability of KM to help organizations evaluate their ability to implement KM successfully by identifying key reliability variables, modeling the complex interaction structure among variables, and determining the probability of failure for each KM capability.

In this study, relevant variables are identified by a thorough analysis of related references in literature. In order to determine the compound structure of complicated interactions among variables, a group-based approach is utilized. Based on the combined cognitive maps, a cognitive network is constructed as a framework for graphically representing the logical relationships between variables and capturing the uncertainty in the dependency among these variables using conditional probabilities. The applicability of the proposed approach and the efficacy of the model was verified and validated with data from a banking institution.

Results show that KM reliability can be defined by the degree to which required KM capabilities, including infrastructure and process capabilities, have the ability to perform as intended in a certain organizational environment. Furthermore, it is demonstrated that reliability assessment of KM through a hybrid approach of fuzzy cognitive map and Bayesian network is possible and useful.

The proposed reliability assessment model facilitates the process of understanding why and how failures occur in KM. Moreover, the proposed approach evaluates the probability of success for each variable as well as for the entire KM initiative. Therefore, it can provide insight for managers and executives into the degree of reliability for their existing KM and prevention of failures in vital factors through necessary actions.

The suggested approach to KM reliability assessment is a novel method that provides powerful arguments for a more holistic view of KM reliability factors, which is crucial for the successful implementation of KM.

The purpose of this paper is to develop a novel hybrid multi-criteria decision-making (MCDM) model to help organizations select their knowledge-based strategy effectively. Knowledge management (KM) initiatives are often started with the selection of a strategy, which is a critical decision for a successful KM implementation.

KM initiatives are often started with the selection of a strategy, which is a critical decision for a successful KM implementation. Thus, the aim of this paper is to develop a novel hybrid MCDM model to help organizations select their knowledge-based strategy effectively.

Results illustrate that the proposed model is efficient to consider the complex interactions among criteria and provides a consistent decision with less pair-wise comparisons. Furthermore, a case study indicates that a “codification versus tacitness” strategy is preferred over other strategies considering nine main domain criteria.

The contribution of this paper is threefold: it addresses the gaps in KM literature on the effective and efficient assessment of KM strategy selection; it provides a comprehensive and systematic framework that combines analytic network process (ANP) and consistent fuzzy preference relations (CFPR) to assess KM implementation strategy; and it illustrates a real-world study to exhibit the applicability of the proposed approach and the efficacy of the framework.

This paper aims to provide a framework for evaluating the impact of implementing customer relationship management (CRM) based on the balanced scorecard (BSC). The outcomes illustrate the gaps between the present conditions of CRM implementation in a specific organization, which leads to some strategic remedies. These remedies are going to be ranked to achieve the best solution for enhancing the quality of CRM in the organization.

This study investigates the weights of measures presented in the CRM‐BSC by distributing the questionnaires among 44 experts in the beverage industry of Iran. It also benefits from judgment‐purposive in non‐probability sampling method for collecting data. The results are analyzed through a fuzzy approach. The strategic remedies for the drawbacks of the organization that were obtained from the CRM‐BSC are also proposed by the experts. These remedies are again evaluated by questionnaires and some selective tools of multi‐criteria decision‐making approach namely: simple additive weighting and technique for ordering preference by similarity to ideal solution.

Through the evaluation process, six significant gaps related to the CRM performance of the organization are agreed upon. For each of these gaps, the strategic remedies are proposed by the experts. The outcomes of ranking these remedies imply that customer feedbacks, updating managerial knowledge and employee belongingness should be the main objectives of the manufacturer for improvement.

This study provides a better understanding of a more effective CRM system for different kinds of organizations by first, clarifying the customer‐related performance gaps of the target organization and second, by presenting strategic solutions for the detected areas. The framework could be also beneficial in other fields of industry, but the relevancy of the measures should be considered.

The CRM‐BSC framework is customized to the Iranian industrial environment. The structure of the measures in the scorecard is proposed for the first time.

As a way of assessing the ability of organizations to discover and manage unexpected failures in organizational capabilities of knowledge management (KM), this study aims to develop a measurement instrument that involves the five reliability dimensions of preoccupation with failure, reluctance to simplify interpretations, sensitivity to operations, commitment to resilience, and deference to expertise.

To generate measurement items, previous research related to organizational reliability, high reliability theory, mindfulness, and required organizational capabilities of KM was reviewed. The measurement instrument was then verified in terms of reliability and validity, empirically using data from 240 companies in North America. Internal consistency of measurements, measurement item reliability, and construct reliability were examined to ensure the reliability of the instrument. Based on confirmatory factor analysis using structural equation modelling, construct validity was also tested.

The reliability evaluation instrument for KM suggested in this study was constructed with four dimensions, preoccupation with failure in KM, sensitivity to KM operations, commitment to resilience in KM, and deference to expertise. The related measurement items were also identified.

This instrument is useful for researchers and executives looking for appropriate outcomes through the implementation of KM initiatives. Furthermore, the study provides a starting point for further research on KM reliability.

To date, while many of the KM success or failure studies have relied on developing success factors or organizational capability requirements, few studies have been conducted to identify evaluation measures that can assess the cognitive infrastructure that enables simultaneous adaptive learning and provides organizational reliability infrastructure through the management of unwanted, unanticipated, and unexplainable failures in KM-required capabilities.

Although the topic of knowledge management (KM) failure has emerged over the past several years, no specific theory has been proposed about the ability of an organization to discover and manage unexpected failures in the organizational capabilities of KM. Thus, the main aim of this paper is to develop a theory of KM reliability by taking into account the availability of existing theory of high reliability for organizations. Furthermore, this study aims to empirically evaluate the impact of a reliable KM on organizational performance by developing a reliability measurement instrument.

The study develops and tests a theoretical framework whereby the reliable KM is supported on its reliability aspects and organizational performance on its financial, process, and internal aspects. Based on a questionnaire, data were obtained from a sample of 254 companies in North America. The measurement model was tested and confirmed by using structural equation modeling (SEM).

The results show that the reliable KM has a multi-dimensional structure as described by the proposed theoretical framework. Additionally, the results underscore the importance of KM reliability in creating conditions favorable for a firm's success.

It was verified that the reliable KM affects the measures of organizational performance, including financial, process, and internal performance. This is useful for researchers and executives looking for appropriate outcomes through the implementation of KM initiatives. Furthermore, this study provides a starting point for further research on KM reliability.

This study claims that a key to successful KM is to create a cognitive infrastructure that enables simultaneous adaptive learning and provides an organizational reliability infrastructure through the management of unwanted, unanticipated, and unexplainable failures in the KM's required capabilities.

This paper seeks to develop a model for risk management of knowledge loss in a project‐based organization in Iran.

This study uses a multi‐stage research approach. In the first stage, existing practices are examined to develop a model for risk management of knowledge loss. In the second stage, the model is evaluated by testing it in a case study. The methods integrated as the foundations of the Integrated KM and RM model are: the PMBOK risk management (RM) approach, the Fraunhofer IPK knowledge management (KM) model, and the TVA knowledge risk assessment framework.

The analytical approach includes a six‐step integrated model that manages the risk of critical knowledge in the case study. The results show that, after a year of implementing the model, the job positions facing knowledge loss were reduced by 88 percent.

The integrated KM and RM model can be used to assist the planning, establishment and evaluation of knowledge loss in projects. This helps to ensure that key issues regarding knowledge loss are covered during the planning and implementation phases of project management.

This study provides an integrated perspective of KM in project‐based organizations. It offers valuable guidelines that can help decision makers consider key issues during a risk assessment of knowledge factors in project management. Outputs of this model can prepare an extensive assessment report about the risk of knowledge loss in a project‐based organization with suggestions for preservation plans to mitigate its effects.

This study aims to propose a new genetic algorithm for solving supply chain scheduling and routing problem in a multi-site manufacturing system. The main research question is: How is the production and transportation scheduled in a multi-site manufacturer? Also the sub-questions are: How is the order assigned to the suppliers? What is the production sequence of the assigned orders to a supplier? How is the order assignment to the vehicles? What are the vehicles routes to convey the orders from the suppliers to the manufacturing centers? The authors’ contributions in this paper are: integration of production scheduling and vehicle routing in multi-site manufacturing supply chain and proposing a new genetic algorithm inspired from the role model concept in sociology.

Considering shared transportation system in production scheduling of a multi-site manufacturer is investigated in this paper. Initially, a mathematical model for the problem is presented. Afterwards, a new genetic algorithm based on the reference group concept in sociology, named Reference Group Genetic Algorithm (RGGA) is introduced for solving the problem. The comparison between RGGA and a developed algorithm of literature closest problem, demonstrates a better performance of RGGA. This comparison is drawn based on many test problems. Moreover, the superiority of RGGA is certificated by comparing it to the optimum solution in the small size problems. Finally, the authors use real data collected from a drug manufacturer in Iran to test the performance of the algorithm. The results show the better performance of RGGA in comparison with obtained outputs from the real case.

The authors presented the mathematical model of the problem and introduced a new genetic algorithm based on the “reference group” concept in sociology. Robert K. Merton is a sociologist who presented the concept of reference groups in society. He believed that some people in each society such as heroes or entertainment artists affect other people. The proposed algorithm uses the reference group concept to the genetic algorithm, namely, RGGA. The comparison of the proposed algorithm with DGA and the optimum solution shows the superiority of RGGA. Finally, the authors implement the algorithm in a real case of drug manufacturing and the results show that the authors’ algorithm gives better outputs than obtained outputs from the real case.

One of the major objectives of supply chains is to create a competitive advantage for the final product. This intension is only achieved when each and every element of the supply chain considers customers’ needs in every function of theirs. This paper studies scheduling in the supply chain of a multi-site manufacturing system. It is assumed that some suppliers produce raw material or initial parts and convey them by a fleet of vehicles to a multi-site manufacturer.

Nowadays, the risk assessment and reliability engineering of various production processes have become an inevitable necessity. Because if these risks are not going to be evaluated and no solution is going to be taken for their prevention, managing them would be really hard and costly in case of their occurrence. The importance of this issue is much higher in producing healthcare products due to their quality's direct impact on the health of individuals and society.

One of the most common approaches of risk assessment is the failure mode and effects analysis (FMEA), which is facing some limitations in practice. In this research, a new generalized multi-attribute failure mode analysis approach has been proposed by utilizing the best–worst method and linguistic 2-tuple representation in order to evaluate the production process of hemodialysis solution in a case of Tehran, Iran.

According to the results, entry of waste to the mixing tanker, impurity of raw materials and ingredients and fracture of the mixer screw have been identified as the most important potential failures. At last, the results of this research have been compared with the previous studies.

Some reinforcement attributes have been added to the traditional FMEA attributes in order to improve the results. Also, the problems of identical weights for attributes, inaccuracy in experts' opinions and the uncertainties in prioritizing the potential failures were improved. Furthermore, in addition to the need for less comparative data, the proposed approach is more accurate and comprehensive in its results.

Expert systems have come to the forefront in the modeling of problems. One of the major problems facing the expert system designers is to develop an accurate knowledge base and a meaningful model of uncertainty associated with complex models. Decision-making is based on knowledge, and decision system support needs a knowledge base as well. An adequate knowledge acquisition (KA) process leads to accurate knowledge and improves the decision-making process. To manage the risk of a medical service (twin pregnancy in this case) a knowledge management system was created. The captured knowledge may be associated with an uncertainty. This study aims to introduce a method for evaluating the reliability of a tacit KA model. It assisted engineering managers in assessing and prioritizing risks. The study tried to use this method in risk management and new case in the health domain.

In this study, relevant variables were identified in the knowledge management literature reviews and the domain of expertise management. They are validated by a group of domain experts. Kendall’s W indicator was used to assess the degree of consensus. On the basis of combined cognitive maps, a cognitive network was constructed. Using Bayesian belief networks and fuzzy cognitive maps, an uncertainty assessment method of tacit KA was introduced. To help managers focus on major variables, a sensitivity analysis was conducted. Reliability of model was calculated for optimistic and pessimistic values. The applicability and efficacy of the proposed method were verified and validated with data from a medical university.

Results show that tacit KA uncertainty can be defined by independent variables, including environmental factors, personality and acquisition process factors. The reliability value shows the accuracy of the captured knowledge and the effectiveness of the acquisition process. The proposed uncertainty assessment method provides the reliability value of the acquisition model for knowledge engineers, so it can be used to implement the project and prevent failures in vital factors through necessary actions. If there is not a satisficed level of reliability, the KA project reliability can be improved by risk factors. The sensitivity analysis can help to select proper factors based on the resources. This approach mitigated some of the disadvantages of other risk evaluation methods.

The contribution of this study is to combine the uncertainty assessment with tacit KA based on fuzzy cognitive maps and the Bayesian belief networks approach. This approach used the capabilities of both narrative and computational approaches.