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Spare parts management plays a relevant role for equipment-intensive companies. An important step of such process is the spare parts classification, enabling properly managing different items by taking into account their peculiarities. The purpose of this paper is to review the state of the art of classification of spare parts for manufacturing equipment by presenting an extensive literature analysis followed by an industrial assessment, with the final aim to identify eventual discrepancies.

Not only is the attention put on the literature about the subject, but also on an on-field analysis, that is presented comprehending an extensive survey and two in-depth exploratory case studies. The copper mining sector was chosen being representative for the case of capital intensive plants where the cost of maintenance has relevant weight on the total operating cost.

The paper highlights the status of the scientific literature on spare parts classification by showing the current situation in the real industrial world. The paper depicts the existing barriers that leave gaps between theory and real practice for the application of an effective multi-criteria spare parts classification.

The paper provides a review of the theory on spare parts classification methods and criteria, as well as empirical evidences especially for what concern current situation and barriers for an effective implementation in the industrial environment. The paper should be of interest to both academics and practitioners, since it provides original insights on the discrepancies between scientific and industrial world.

Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

Activities on direct value chain directly add value to the products/services delivered to the customers. This value addition can be further enhanced by taking up flexibility initiatives on these direct value chain activities. The purpose of this paper is to identify flexibility initiatives on the direct value chain and carry out their valuation.

Since different value addition criteria (i.e. benefits and costs) may be both tangible and intangible, an interpretive valuation based on multi-criteria framework would be desirable. The paper deploys the efficient interpretive ranking process (IRP) for this purpose. It uses total interpretive structural modeling (TISM) to derive weights of criteria based on their respective driving power. The paper also makes a methodological contribution to assess transitive dominance in IRP based on transitivity check as used in the modified TISM process.

The paper uses a multi-criteria valuation to examine the proposition that the flexibility initiatives will add a higher value as we go downstream in the value chain. The flexibility initiatives linked with marketing such as product upgradation and switching, customized services and dynamic pricing are found to be most value adding in character.

The main limitation of this study is that it is generic in nature and need to be replicated in multiple case situations. The methodology proposed can be utilized for specific case analysis for flexibility valuation on the direct value chain.

The practitioners may be able to use the proposed method of TISM–IRP with improvements in real-life applications. The proposed multi-criteria valuation is interpretive in nature and can be utilized by practicing managers in group settings even if they do not have complete data.

The strategy and operations researchers will find promise in the methodology to investigate and prioritize the initiatives planned to enhance flexibility in different value chain activities. The researchers on multi-criteria decision making will find the methodological enhancement proposed in terms of graphically deriving the transitive dominance to be useful in other applications as well.

A multi‐criteria model is developed for analyzing the land‐use strategies for reducing the future social and economic costs in an area with potential natural hazard. A multi‐criteria decision‐making procedure consists of generating alternatives, establishing criteria, assessment of criteria weights and application of the compromise ranking method (VIKOR). The alternatives are the scenarios of sustainable hazard effects mitigation, generated in the form of comprehensive land‐use plans. The alternative plans should consider the redevelopment of urban areas and infrastructures, multi‐purpose land use, including restrictions on building in hazardous areas. The evaluation of alternatives is implicated with imprecision (or uncertainty) of established criteria, and the fuzzy multi‐criteria model is developed to deal with “qualitative” (unquantifiable or linguistic) or incomplete information. The planning goal is to compromise competitive land uses through choice of the best (compromise) combination of uses. An application of this model is illustrated with the post‐earthquake regional planning problem in central Taiwan.

Wireless sensor networks are expected to be an integral part of any pervasive computing environment. This implies an ever‐increasing need for efficient energy and resource management of both the sensor nodes, as well as the overall sensor network, in order to meet the expected quality of data and service requirements. There have been numerous studies that have looked at the routing of data in sensor networks with the sole intention of reducing communication power consumption. However, there has been comparatively little prior art in the area of multi‐criteria based routing that exploit both the semantics of queries and the state of sensor nodes to improve network service longevity. In this paper, we look at routing in sensor networks from this perspective and propose an adaptive multi‐criteria routing protocol. Our algorithm offers automated reconfiguration of the routing tree as demanded by variations in the network state to meet application service requirements. Our experimental results show that our approach consistently outperforms, in terms of Network Lifetime and Coverage, the leading semantic‐based routing algorithm which reconfigures the routing tree at fixed periods.

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.

To propose a multi‐criteria approach for ranking the performance of the economic sectors of the Portuguese economy and to identify the most attractive sectors.

A multi‐criteria approach using ELECTRE III and SRF software for eliciting the weights of criteria is used to solve the ranking problem statement.

The sector non‐metallic mineral products was ranked in first place in relation to all the rankings; the wholesale trade and retailing trade sectors were ranked in second place in relation to all the rankings; the sector wood, cork and furniture was ranked often in second place; the electronic and electrical industry sectors was ranked in third place; the agriculture and fishing sector was ranked in the tail‐end of the ranking; the agro‐industry sector and the transportation and distribution sector consistently were ranked in the lowest positions, just before the agriculture and fishing sector.

Other evaluation criteria can be used to improve the relative ranking classification, namely strategic and operational performance measures.

The results of this application are useful for investors, business leaders, and policy makers. From the perspective of potential investors, investment opportunities can be evaluated based on the attractiveness of the economic sectors studied. From the perspective of Portuguese policy makers and business leaders, sectors of the Portuguese economy that are in need of fundamental restructuring are easily identified. For these sectors, business strategies and policy initiatives that are based on benchmarking effective innovations are in order. Therefore, joint partnership between business leaders, policy makers and higher education to lay the ground for innovative practices in these sectors may prove very useful.

The proposed multi‐criteria approach utilized in this study is simple; furthermore it has the capacity to deal well with uncertainty. This study also contributes to an important, yet relatively new body of application‐based literature, dealing with a multi‐criteria approach to decision making with the aid of fuzzy theory methodologies.

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 to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation.

Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models.

Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs.

The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and C considering multiple criteria and relationships among those criteria. The framework is flexible enough to add additional criteria and to modify fuzzy-rule-base at any point of time by the decision makers. This model can be easily integrated to any customized Enterprise Resource Planning applications.

The value of this paper is in applying Fuzzy-rule-based approach for Multi-criteria Inventory Classification of spare parts. This rule-based approach considering multiple criteria is not very common in classification of spare parts inventories. Total cost comparison is made to compare the performance of proposed model with the traditional classifications and the result shows that proposed fuzzy-rule-based classification approach performs better than the traditional ABC and gives almost the same cost as aggregate scoring model. Hence, this method is valid and adds a new value to spare parts classification for better management decisions.

The purpose of this paper is to investigate, through a comparative analysis, the applicability of lean manufacturing practices, such as value stream mapping (VSM), for productivity improvement in recycling centres (RCs) aided by multi-criteria decision analysis.

The study is carried out in five RCs that sort the municipal solid waste of Porto Alegre, one of the main cities in Brazil. Since all of the centres present their labour composed by poor communities’ members, cultural and social characteristics may represent an incremental challenge for lean implementation. Further, these centres are organised in cooperatives, in which decisions are taken through a participatory way and all their members are entitled to vote, undermining and retarding the decision-making process.

The integration of a multi-criteria decision-making tool to the lean practices enables the prioritisation of improvements, complementing the final stage of VSM. In particular, this contribution becomes especially important in cooperatives managed by community, where decisions are often complex and time-consuming. Finally, despite the increasing pressure for better performance of RCs, the existent mindset is still far from the private sector, where lean practices were conceived. Further, the findings suggest that, despite processes similarities, it is not feasible to declare the existence of a one-best practice to such scenario.

In theoretical terms, the authors demonstrate through a multi-case study the adequacy of analytic hierarchy process as a decision analysis tool complementary to the VSM, enabling a broader perspective about this subject. Concerning the practical contribution, the comprehension of the adaptation needs for lean practices implementation within the production context of solid waste RCs provides a framework with guidelines for this sector, when incorporating lean activities. Lean practitioners and eventual municipal authorities involved in improving productivity of community-managed RCs might benefit from this framework, since they will be able to emphasise the development of recommended and already tested lean practices that tend to improve their operational performance.