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This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

This study aims to review the status quo, current state of research, research hot themes and research gaps in sustainable facilities management (SFM) in the built environment (BE) through an extant literature review.

To map and analyze knowledge paths in the context of SFM research, a sequential explanatory mixed-method review involving bibliometric and content analysis was used to help identify current research trends, research hot themes and knowledge gaps. The Scopus search engine was used to find 169 relevant articles. For a better understanding of the literature accumulated, a bibliometric analysis was carried out by using VOSviewer to reveal current research themes, the status quo and current state of research as well as research gaps.

Through the literature review and content analysis, the current research themes on SFM revealed from the study include green building technologies, assessment methods of SFM, smart buildings and building information modeling. The research hot themes in SFM include smart buildings and green building technologies, green buildings (GB), architectural and building designs in the university sector, assessment methods in buildings and decision-making and the adoption of asset and facility management in the university sector. Indoor air pollution, intelligent buildings, climate change, maintenance, environmental management, facilities, historic preservation, environmental performance, energy management, etc. are the research gaps identified from the study, and these serve as potential areas for future research studies under SFM. It was recognized that facilities managers are increasingly involved with sustainability policies within their organizations and are developing sustainability agendas to keep up with the changing nature of the facilities management (FM) profession.

The findings of this study hold relevance to the FM practice, as the integration of SFM by facilities managers can lead to waste reduction, decreased operating expenses and reduced energy consumption. In addition, occupants of sustainable buildings experience improved conditions that contribute to better health and productivity, thus boosting their overall well-being. Consistent with the themes of smart buildings and green technologies, revealed to be the hot themes in the SFM research scope, properties with sustainable features can command higher rental rates and property values, appealing to a broader range of stakeholders. SFM practices in universities can aid in saving money from reduced facility operational costs and improve the image of institutions while creating better indoor environments for students and staff. The analyses of countries involved in research can open doors for the establishment of research groups and the development of collaboration between universities in different countries researching similar topics of interest.

The geographical scope of this study is not limited and, therefore, encourages broad applicability of the findings to the global sustainable BE.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

The goal of this study was to determine the impact of access to credit facilities on financial performance among farmers of cooperative societies. The study also tested the predictive power of financial literacy.

The descriptive survey research design was used for the study while the sample size was 240 farmers of cooperative societies from South-East Nigeria. The farmers were categorised into those with access to credit facilities and those without access to credit facilities. A structured questionnaire was used to collect data for the study. Data were analysed using multiple analyses of variance (MANOVA) and multiple regression analysis.

Farmers with access to credit facilities reported higher financial performance such as return on investment, working capital, net profit, profit margin and sales. However, those without access to credit facilities reported lower mean scores on financial performance. Also, financial literacy, like financial knowledge, attitude and awareness, significantly predicts the impact of access to credit facilities on financial performance. It was also found that the duration of repayment of credit facilities, like medium and long term, contributes more to improving financial performance.

This study has shown that even though access to credit facilities impacts financial performance, financial literacy is an important consideration. Also, the duration of repayment is a crucial factor.

The retail sector has the largest energy consumption among commercial buildings in the U.S. Although previous studies explored benefits, barriers and solutions for implementing sustainability in various building sectors, research focused on retail facilities has been very scarce. This study aims to explore U.S. facilities managers’ perceptions of barriers that prevented the implementation of energy-efficiency practices in the retail sector. Their perceptions were compared by facility size and facilities management company’s business revenue.

An online survey was distributed to the members of the International Facility Management Association and the author's LinkedIn network. The survey responses were analyzed using descriptive statistical analysis and ANOVA.

Managers from large facilities, as opposed to those from small ones, significantly more agreed that the unavailability of building automation systems, a lack of professional writing skills and a lack of awareness of life cycle cost (LCC) were the barriers. Business revenue did not cause significantly different perceptions of the barriers except for a lack of awareness of LCC and a lack of support from upper management.

This study fills the research gap on energy efficiency in the retail sector by revealing U.S. facilities managers’ perceptions of the barriers to the implementation of energy-efficiency practices in retail stores. This novel study compares perceptions of the facilities managers by facility size and business revenue; this comparison has not been performed before. The study also identified several new barriers to the implementation of energy efficiency in the retail sector.

The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.

This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.

The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.

Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

This study aims to explore the level of independence among persons with disabilities (PWDs) towards their use of public buildings’ facilities, as well as assess the anxiety and relevant challenges regarding such use.

In a cross-sectional survey design, 91 consenting PWDs were recruited via purposive sampling at diverse locations and invited to complete self-report questionnaires on their use of public buildings’ facilities.

Only 19 PWDs (20.9%) were able to independently use such facilities. The participants’ level of anxiety, observed to be mostly severe (41.7%), was significantly associated with sex and affected body region (p < 0.05). Most PWDs identified “lack of amenities” (85.7%), “inconvenience” (78%) and “safety issues” (59.4%) as relevant challenges towards the use of public buildings’ facilities.

This research emphasises the urgent need to universally improve PWDs’ level of independence towards the use of public buildings’ facilities alongside eradicating anxiety and challenges experienced towards such use. Accessibility ought to be promoted among PWDs, with specific attention given to women and those affected in multiple body regions; as such populations are prone to experiencing substantial anxiety. This approach should entail the provision of barrier-free facilities as well as ensuring their safe and convenient use by PWDs.

This study sought and provided key feedback from PWDs on the current built environment, which ought to be considered by relevant stakeholders to improve accessibility, promote well-being and contribute towards meeting the global goals of reducing inequality and making cities/communities inclusive, safe, resilient and sustainable.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.

The purpose of this study is to solve the problem of healthcare waste management in developing countries. The buildup of medical waste has attracted the attention of all spheres of society due to the expanding population and developing economy. Timely collection and processing of medical waste are extremely important due to its potential hazards. Although the problem of planning medical waste management has been addressed in developed countries, it persists in several developing countries. This research is motivated by an example of a city in India characterized by a dense population, abundant health-care facilities and a lack of planning for managing large medical waste generated daily.

The authors address the problem of designing the network of collection and processing facilities for medical waste and optimizing the vehicle route that collects and transfers the waste between facilities. Due to distinct topographic restrictions in the considered city, the collection and transfer process needs to be conducted in two echelons – from hospitals to collection centers using smaller vehicles and then to the processing facilities using trucks. This work addresses these two problems as a two-echelon location-routing problem.

A mixed-integer programming model is developed to minimize the cost of opening the facilities and transporting medical waste. Several managerial insights are drawn up to assist planners and decision-makers.

This study follows a case study approach to provide a descriptive and prescriptive approach to hospital waste management in the ancient city of Varanasi. The city has witnessed unplanned growth over the years and is densely populated. The health-care facilities in the city have a large catchment area and attract patients from neighboring districts. The situation analysis based on secondary data and unstructured interviews of the stakeholders suggests that the ad hoc approach prevails in present hospital waste management in the city.

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.