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Tactile communication that relies on the human sense of touch replicated using vibration motors is increasingly being explored for seamless communication on construction jobsite. However, the technological efficacy cannot secure the users’ acceptability of the tactile communication devices. This study aims to assess the factors affecting the wearability of such a portable tactile device based on the responses from practicing professionals.

The investigation adapted a three-step phenomenological interviewing approach to seek feedback from construction personnel in Texas, the USA, regarding the viability of wearable tactile communication. The interviewees expressed various opinions about the on-body placement upon exposure to a portable tactile feedback prototype developed for this study, which was used to derive inferences regarding the factors affecting its on-field acceptability.

All the participants of the round-table study (11 out of 11) considered tactile feedback as a viable mode of communication on construction jobsite. Seven professionals supported the integration of a tactile device with the hard hat, whereas the rest preferred tactile eyeglasses. Weatherability, rechargeability, traceability, safety and social receptivity were identified as the major factors affecting the on-body placement of the wearable tactile communication device.

This paper presents a roadmap to gain construction industry opinion on the factors that can affect the on-body placement of a wearable tactile communication device. The five aforementioned factors impacting tactile communication acceptability were used to evaluate 10 potential on-body placements. The findings have implications for research and development of wearable tactile devices and the subsequent acceptability of such a device on the jobsite.

This study aims to enhance an enterprise’s private cloud services by optimally determining the ownership of cloud computing resources and responsibility for maintenance and operations. The core objective is to identify the most cost-effective private cloud deployment model at the intersection of technology and business considerations.

This study evaluates three ownership and responsibility models, each encompassing decisions related to candidate data center locations, resource provisioning, and demand placements. Drawing from the cloud computing literature, these models are referred to as deployment models. The research formulates a private cloud deployment model selection problem and introduces an established Lagrangian-relaxation-based optimization approach, combined with a novel greedy relieving-pooling heuristic, to facilitate model selection.

This study identifies the optimal deployment model for a representative instance using real test-bed data from the US, demonstrating the private cloud deployment model selection problem. Various numerical examples are analyzed to explore the influence of environmental parameters. Generally, the virtual PC model is optimal for low demand arrival rates and resource requirements, while the on-premises PC model is preferable for higher values of these parameters. Additionally, the virtual PC model is found to be optimal when enroute latency coefficients are large.

This study contributes to the literature by formulating an optimization problem that integrates performance, financial, and assurance metrics for enterprises. The introduction of a solution approach enables enterprises to make informed decisions regarding ownership and responsibility design. The study effectively bridges the gap between academic research and industry demands from a business perspective.

This study aims to examine the anticipated and actual challenges encountered by occupational therapy and physiotherapy students during their first full-time professional placement and to understand the strategies they implemented to manage their multiple life roles.

Longitudinal qualitative research examined students’ anticipated and reported challenges with their first block professional placement and the strategies they implemented during it. In total, 22 occupational therapy and physiotherapy students were interviewed at two time points (pre- and post-placement), producing 44 interview data points. Transcribed interviews were analysed thematically using a hybrid approach.

Pre-placement, students perceived potential challenges related to the distance between their placement location and where they resided and their ability to maintain balance in their multiple roles. Post-placement, the main reported challenge was maintaining role balance, due to unexpected challenges and students’ unanticipated tiredness. Students implemented strategies to assist with managing multiple roles and reflected on the benefits and drawbacks of placements. They also considered the necessary future adjustments.

This study highlighted the importance of social support and the need for proactive recovery strategies to negate the tiredness that students experienced on placement.

This is the first study, to our knowledge, to investigate how allied health students, on their first block of professional placement, balanced their multiple roles over time.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

This study aims to gain a deeper understanding of the industry practice to guide the formation of support tools with a rigorous theoretical backing. Cross-functional teams are an essential component in new product development (NPD) of complex products to promote comprehensive coverage of product design, marketing, sales, support as well as many other activities of business. Efficient use of teams can allow for greater technical competency coverage, increased creativity, reduced development times and greater consideration of ideas from a variety of stakeholders. While academics continually aspire to propose methods for improved team composition, there exists a gap between research directions and applications found within industry practice.

Through interviewing product development managers working across a variety of industries, this paper investigates the common practices of team utilization in an organizational setting. Following these interviews, this paper proposes a conceptual two-dimensional management support model aggregating the primary drivers of team success and providing direction to systematically address features of team management and composition.

Based on this work, product managers are recommended to continually address the positioning of members throughout the entire NPD process. In the early stages, individuals are to be placed to work on project components with explicit consideration toward the perceived complexity of tasks and individual competency. Throughout the development process, individuals’ positions vary based on new information while continued emphasis is placed on maintaining a shared understanding.

Bridging the gap between theory and application within product development teams is a necessary step toward improved product develop. Industrial settings require practical solutions that can be applied economically and efficiently within their organization. Theoretical reflections postulated by academia support improved team design; however, to achieve true success, they must be applicable when considering product development.

Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.

This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.

The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.

This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.

This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

This paper aims to deal with intra and inter-cell layout problems in cellular manufacturing systems. The model is organized to minimize the total handling cost, i.e. intra and inter-cell handling costs in a continuous environment.

The research was conducted by developing a mixed integer mathematical model. Due to the complexity and NP-hard nature of the cellular manufacturing layout problem, which mostly originated from binary variables, a “graph-pair” representation is used for every machine set and cells each of which manipulates the relative locations of the machines and cells both in left-right and below-up direction. This approach results in a linear model as the binary variables are eliminated and the relative locations of the machines and cells are determined. Moreover, a genetic algorithm as an efficient meta-heuristic algorithm is embedded in the resulting linear programming model after graph-pair construction.

Various numerical examples in both small and large sizes are implemented to verify the efficiency of the linear programming embedded genetic algorithm.

Considering the machine and cell layout problem simultaneously within the shop floor under a static environment enabled managers to use this concept to develop the models with high efficiency.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

This study aims to deal with the multi-mode resource-constrained project scheduling problem (MRCPSP) with the ability to transport resources among multiple sites, aiming to minimize the total completion time and the total cost of the project simultaneously.

To deal with the problem under consideration, a bi-objective optimization model is developed. All activities are interconnected by finish-start precedence relations, and pre-emption is not allowed. Then, the ɛ-constraint optimization method is used to solve 24 different-sized instances, ranging from 5 to 120 activities, and report the makespan, total cost and CPU time. A set of Pareto-optimal solutions are determined for some instances, and sensitivity analyses are performed to find the impact of changing parameters on objective values.

Results highlight the importance of resource transportability assumption on project completion time and cost, providing useful insights for decision makers and practitioners.

A novel bi-objective optimization model is proposed to deal with the multi-site MRCPSP, considering both the cost and time of resource transportation between multiple sites. To the best of the authors’ knowledge, none of the studies in the project scheduling area has yet addressed this problem.