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Purpose Maintenance plans are programmes, which follow maintenance appraisals, contain information of what to do and the time approximates for accomplishments. They also deal with how to carry out maintenance jobs. In contemporary period, curiosity has proliferated about how sustainability affects manufacturing plans. The purpose of this paper is to offer a comprehensive notion of maintenance sustainability in maintenance planning. The literature has downplayed maintenance sustainability but may support in understanding how to crack the present company-community conflicts about the negative influence of manufacturing on the environment. Design/methodology/approach This study develops the idea of selecting the proper maintenance strategy based on integrated fuzzy axiomatic design (FAD) principle and fuzzy-TOPSIS. This work suggests that the maintenance function is an uncertain, activity-oriented system. To fully appreciate the proposed framework, the work employs data from a cement manufacturing plant to test the structure. This study offers 20 influential factors on which it build the fundamental structure of maintenance system sustainability for manufacturing concerns. A novel literature contribution that departs from existing conceptions is the classical determination of weights of each sustainability factor, employing fuzzy entropy weighting approach. Furthermore, work innovatively determines the ranking of some important tenets of sustainability in maintenance and optimises the maintenance consumables employing the FAD principle. Findings Interestingly, the output of the investigation revealed differences as the work adopts fuzzy-TOPSIS in comparison with FAD principle. Originality/value Case examination of a real-life manufacturing venture validated the claims, showing maintenance workforce training as a top-echelon strategy for maintenance system sustainability.

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The growing interest in technicians' workloads research is probably associated with the recent surge in competition. This was prompted by unprecedented technological development that triggers changes in customer tastes and preferences for industrial goods. In a quest for business improvement, this worldwide intense competition in industries has stimulated theories and practical frameworks that seek to optimise performance in workplaces. In line with this drive, the present paper proposes an optimisation model which considers technicians' reliability that complements factory information obtained. The information used emerged from technicians' productivity and earned-values using the concept of multi-objective modelling approach. Since technicians are expected to carry out routine and stochastic maintenance work, we consider these workloads as constraints. The influence of training, fatigue and experiential knowledge of technicians on workload management was considered. These workloads were combined with maintenance policy in optimising reliability, productivity and earned-values using the goal programming approach. Practical datasets were utilised in studying the applicability of the proposed model in practice. It was observed that our model was able to generate information that practicing maintenance engineers can apply in making more informed decisions on technicians' management.

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The mini-grid proliferation has helped to improve the current state of electricity supply in several rural areas in developing countries. This is due to the innovations in renewable energy technologies. The impact of this development is the establishment of mini-grid business. There is a need for mini-grid business owners to identify the most suitable energy source for a particular area. To achieve this, proper analysis of risks that impact mini-grid business operations is required for optimal energy source selection. The current study addresses this problem by proposing a conceptual framework that considered risk factors. The conceptual framework analysed scenarios where expected risk values are specified and not specified by decision-makers. This was achieved using fuzzy axiomatic design (FAD), intuitionistic entropy method, and TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) methods. The TOPSIS and FAD results were combined using WASPAS (weighted aggregated sum product assessment) method. The proposed conceptual framework was applied in sub-Sahara Africa, Lagos, Nigeria. During the application of the proposed framework, five renewable energy sources and thirteen types of risks were considered. Information from four decision-makers was used to demonstrate the applicability of the framework. The results obtained showed that unpredictable electricity demand and construction completion risks were identified as the least and most important risks for the selection of renewable energy sources for mini-grid, respectively. The FAD and TOPSIS methods identified wind and biomass energy as the best-ranked energy source for mini-grid business, respectively. The WASPAS method and the FAD results were the same.

Today, maintenance plays a significant responsibility in all stages of equipment life due to strong government interests in environmentally conscious manufacturing. The need of the hour is to ignore the sparse attention to sustainable maintenance research and pursue valuable links between maintenance strategy and sustainable maintenance. In maintenance strategy choice, available reports have not sufficiently addressed the imbalance caused by uncertainties in maintenance practices. In addition, current reports on maintenance strategy sustainability focus on technical and economic aspects of maintenance and scantily treat environmental, social and safety criteria. This affects the quality of decisions in maintenance systems. To remedy this situation, this study applies fuzzy entropy weight and PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluation) in ranking maintenance sustainability strategies. The proposed approach is tested in a cement plant. Based on the choice criteria, the PROMETHEE methods results identified the best maintenance strategy as maintenance optimisation strategy. Workforce training strategy was identified as the worst maintenance sustainability strategy. These obtained results were compared with fuzzy TOPSIS (Technique of Preference Order by Similarity to Ideal Solution) approach and the practical application of the approach was verified. The results serve as a basis and a platform for further application of the approach in other manufacturing companies.

Socio-technical and economic attributes consideration are very important during a renewable energy technology selection for a community. When decision-makers considered these attributes under a dynamic nature, they arrive at a robust decision. Hence, this study proposes an integrated model for renewable energy technologies evaluation under a dynamic condition. We developed the model using dynamic intuitionistic fuzzy Einstein geometric averaging operator, intuitionistic fuzzy entropy, and the intuitionistic fuzzy technique for order of preference by similarity to ideal solution method (TOPSIS). This model's applicability was tested using five renewable energy technologies-solar (PT 1), wind (PT 2), hydroelectricity (PT 3), geothermal (PT 4) and biomass (PT 5) and five attributes (risk factor, payback reliability, social benefit, change in demand and cost). Based on five energy experts, from academia and industry, opinions, the proposed model identified biomass energy technology as the most suitable energy technology. Three existing multi-criteria models were used to verify the proposed model; the proposed model performance was consistent with the existing models' results. From most suitable the least suitable, the model ranked these technologies PT 5 > PT 2 > PT 3 > PT 1 > PT 4 .