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The purpose of this paper is to develop a model that permits more rational planning for preventive maintenance, by controlling failures in the specific context of equipment breakdown. Thus not only the cost and reliability parameters are dealt with, but also the peculiarities of different contexts in which maintenance activities occur. Furthermore, it aims to include Bayesian methodology in the procedure to overcome main difficulties in failure data.

A multi‐criteria decision‐aiding model capable of overcoming the two main difficulties related to preventive maintenance: establishing a replacement periodicity based on more than one criterion, and the ability to provide a solution in uncertainties situations, has been developed from adaptation of classical models. This model also uses Bayesian elements to address uncertainties during equipment failures.

The paper finds that in a preventive maintenance planning, as a multi‐criteria decision problem, different types of uncertainties may be identified, which may be categorized as external uncertainties and internal uncertainties. In the proposed model a division of procedures has been established, dealing with external uncertainties first, then the internal uncertainties related to the structure of the problem and analysis of the decision are addressed using the multi‐criteria decision‐making method PROMETHEE III that allows the amplification of the notion of indifference. In this way, a suitable structure to connect two types of uncertainties was structured.

The model will assist the decision‐maker in preventive maintenance planning to take uncertainties into account, seeing the alternatives that are closest, through an amplification of the notion of indifference provided by PROMETHEE III. Furthermore, in practice the proposed model have an impact on maintenance cost and reliability of production plant.

This paper proposes a multi‐criteria decision‐aiding model capable of overcoming the two main difficulties related to preventive maintenance: establishing a replacement periodicity based on more than one criterion; and the ability to provide a solution even when failure data are unavailable or incomplete.

For the purpose of decision-making in real estate portfolio management, alternatives are to be collected, assessed and prioritized. Due to the complexity of real estate markets, investment products and management processes, the respective decision situations are subject to several parameters. Multi-criteria models must be used to exactly evaluate and prioritize alternatives. Such models can be found in other economic and social areas but must be transferred or re-developed for the purposes of real estate management. The paper aims to discuss these issues.

The paper includes the transfer of the basic outranking methodology, the PROMETHEE (Preference Ranking Organization METHod für Enrichment Evaluations) method in particular, to real estate issues. Methods used outside the real estate industry are analyzed, selected and adapted by using real estate parameters (transfer approach).

Structured multi-criteria processes such as PROMETHEE are suitable for the solution of complex real estate selection decisions. The methodology with regard to the respective issues is much more consistent and efficient. PROMETHEE avoids the restriction of criteria in mathematical calculations and the restriction of quality in simple scorings.

The target system and decision criteria of investors were used exemplarily. The individual parameters and criteria can lead to new model solutions.

Multi-criteria models such as PROMETHEE stringently and transparently solve complex decision problems and alternative evaluations in real estate portfolio management. They can be developed for strategic, tactical and operative decision situations. The decision quality and verification for compliance requirements improve.

Multi-criteria models such as PROMETHEE can also be developed for social, societal and political decision situations.

First adaption of the outranking procedure PROMETHEE to a real estate decision situation in market analysis and portfolio management.

Supplier selection is a complex and strategic activity needed in every organization, involving many stakeholders and different attributes as price, delivery performance, and product quality. Globalization, in the last decades, increased the competitiveness between vendors, enhancing the use of decision models to support the best choice based on optimizations and bidding variations due to specific needs. This chapter presents three models of multi-dimensional auctions to improve an international humanitarian NGO process procurement efficiency by reducing procurement costs and the decision-making process time. These models have the advantage to be easily implementable in typically complex environments where there is a large number of categories, suppliers, and other features.

The first proposed model uses combinatorial auctions and is suited for procurement, where suppliers can benefit from cost complementarity. The second one uses volume discount auctions and is suited for volumetric purchases, where discounts for large quantities are common. The third one is a multi-attribute model, which computes the best possible solution considering several criteria and can be used in case of complex purchases that involve various categories and trade-offs and are subject to spot prices.

Several design considerations for this type of auctions are reviewed, as well as the mathematical formulation to determine the best alternative (i.e., winner) that can be solved using simple tools like Microsoft Excel. The models are optimized by a mixed-integer programming, and the multi-attribute one is developed using multi-criteria decision analysis (MCDA). All three models developed in this research showed superior results compared to the baseline, being between 9% and 20% more efficient than a regular supplier selection (singly choosing the lowest price) and improving the bidding compliance.

The purpose of this paper is to support the management of organizational knowledge retention through a multi-criteria decision aiding–constructivist model.

This exploratory and descriptive case study presents a decision support model guided by the constructivist approach and proactive in its operationalization.

The objectives and concerns of decision-makers regarding the retention of organizational knowledge are identified and organized into six strategic areas of concern, namely, recognition, knowledge dissemination, organizational culture, succession of professionals, management of vulnerability origins and knowledge management; a multi-criteria model is constructed and operationalized by a cluster of cardinal scales, showing and measuring the status quo of the performance profile, both in a local and global way, to support the management of the organization's knowledge retention; activities are classified into three performance levels (compromising, competitive and excellent), supported by graphical and numerical evidence; and the process to generate actions to improve the performance of critical activities and create the conditions to maximize the results of the organization is illustrated.

Based on the model, decision-makers are now aware of the essential aspects to support knowledge retention management, enabling them to monitor the current situation and proactively respond to ensure that the current knowledge potential is maintained and exploited.

Use of a constructivist approach to support the management of knowledge retention, incorporating into the model the specifics of the context and the values of its managers, and thus giving it legitimacy.

Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

The purpose of this paper is to present the development of a multi-criteria decision-making model for use by maintenance managers to consider before making a decision on outsourcing.

Thirty-eight factors were identified for outsourcing maintenance services. These factors were grouped under six categories, namely: “strategic”, “management”, “technological”, “quality”, “economic” and “function characteristics”. The Analytic Hierarchy Process, as a multi-criteria decision-making model, was introduced and applied as an approach for maintenance managers in Saudi Arabian universities to consider before making a decision on outsourcing. A case study on the outsourcing decision of maintenance services of air-conditioning systems was carried out to apply the developed model.

Data analysis indicated that all outsourcing decision groups of factors have almost equal weight, with the “quality” group of factors having the highest weight and the “technological” group of factors having the least weight. Further, the analysis indicated, in general, that the recommended decision for the maintenance managers is to outsource. However, an application of the developed model through a case study on the outsourcing of maintenance services of air-conditioning systems showed that the recommended action is not to outsource.

The presented approach in this paper could be of practical benefit to maintenance managers in their decision making of whether or not to outsource maintenance services. The factors in the model were identified through a literature survey of research carried out in different countries. Therefore, the model could be applied in different settings, depending on the relative weight of the factors by the users.

The purpose of this paper is to propose a supply chain risk management (SCRM) maturity model combined with a fuzzy TOPSIS classification method to evaluate and sort an organization into a pre-defined maturity level.

An axiomatic and prescriptive research method guided this study. Therefore, it proposes a prescriptive approach of maturity classification based on a theoretical SCRM maturity model combined with a multi-criteria decision technique.

The results of a pilot application indicated a consistent classification and the value of the model for diagnosing flaws and pointing directions for improving operational and disruption risk management. Its comprehensiveness allows applying it to supply chains of several industry sectors.

The proposed model does not include all possible risks and could be revised in further developments. Also, adjustment of the maturity profiles of the multi-criteria decision-making (MCDM) model requires a learning process from practical applications.

The adoption of the risk management maturity grid by practitioners may bring the benefit of a more objective and comprehensive evaluation of risk management processes in the supply chain context.

An immediate social implication derives from the improvement actions that may result from the diagnosis of risk management vulnerabilities identified in the pilot application. In general, the proposed model has the potential to reduce risks, improve results and contribute to economic sustainability.

The maturity grid and decision model integrate overall aspects of risk management, bringing together managerial concepts to deal with a variety of supply chain operational risks. The combined multi-criteria classification procedure to sort the maturity level of an organization is also a novelty.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

The purpose of this paper is thus to develop a hybrid decision-making model for optimal coal blending strategy. Coal is one of the major resources contributing to generation of electricity and anthropogenic carbon-dioxide emission. Being formed from dead plant matter, it undergoes a series of morphological changes from peat to lignite, and finally to anthracite. Because of non-uniform distribution of coal over the whole earth and continuous variation in its compositions, coals mined from different parts of the world have widely varying properties. Hence, it requires an ideal blending strategy such that the coking coal having the optimal combination of all of its properties can be used for maximum benefit to the steel making process.

In this paper, a multi-criteria decision-making approach is proposed while integrating preference ranking organization method for enrichment of evaluations (PROMETHEE II and V) and geometrical analysis for interactive aid (GAIA) method to aid in formulating an optimal coal blending strategy. The optimal decision is arrived at while taking into account some practical implications associated with blending of coal, such as coal price from different reserves.

Different grades of coal are ranked from the best to the worst to find out the composition of constituent coals in the final blending process. Coals from the mines of two different geographical regions are considered here so as to prove the applicability of the proposed model. Adoption of this hybrid decision-making model would subsequently improve the performance of coal after blending and help in addressing some sustainability issues, like less pollution.

As this model takes into account the purchase price of coals from different reserves, it is always expected to provide more realistic solutions. Thus, it would be beneficial to deploy this decision-making model to different blending optimization problems in other spheres of a manufacturing industry. This model can further accommodate some more realistic criteria, such as availability of coal in different reserves as a topic of future research work.

The regular occurrence of natural disasters elevates the need for an effective method to measure organizational preparedness in responding to the adverse impact of disasters. In this context, this paper presents a new decision support model to assess organizational disaster preparedness using both subjective and objective disaster preparedness criteria in a multi-criteria decision-making context.

The statistical variance method is integrated with the proximity value index (PVI) technique to determine priority scores in order to rank organizational disaster readiness.

The results of applying the integrated model developed herein enable decision-makers to make informed decisions for assigning priority ranking of organizational disaster preparedness in a simpler and more efficient way.

Human resource is the most impacting criterion affecting hospital preparedness in undertaking action to cure disaster victims.

This paper offers an exemplar of a simple and efficient decision-making process considering the subjectivity associated with decision-making as well as the objectivity of data used for determining the priority ranking of organizational disaster preparedness.

Integrating statistical variance method with the PVI technique is novel and it has not been presented in previous studies. In fact, this study is the first to integrate both methods for selecting the priority ranking of organizational disaster preparedness.