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The purpose of this paper is to study the use of a heterogeneous and evolutionary team approach based on different sources of knowledge to address a real-world problem within the telecommunication domain: the frequency assignment problem (FAP). Evolutionary algorithms have been proved as very suitable strategies when they are used to solve NP-hard optimization problems. However, these algorithms can find difficulties when they fall into local minima and the generation of high-quality solutions when tacking real-world instances of the problem is computationally very expensive. In this scenario, the use of a heterogeneous parallel team represents a very interesting approach.

The results have been validated by using two real-world telecommunication instances which contain real information about two GSM networks. Contrary to most of related publications, this paper is focussed on aspects which are relevant for real communication networks. Moreover, due to the stochastic nature of metaheuristics, the results are validated through a formal statistical analysis. This analysis is divided in two stages: first, a complete statistical study, and after that, a full comparative study against results previously published.

Comparative study shows that a heterogeneous evolutionary proposal obtains better results than proposals which are based on a unique source of knowledge. In fact, final results provided in the work surpass the results of other relevant studies previously published in the literature.

The paper provides a complete study of the contribution provided by the different metaheuristics included in the team and the impact of using different sources of evolutionary knowledge when the system is applied to solve a real-world FAP problem. The conclusions obtained in this study represent an original contribution never reached before for FAP.

The purpose of this paper is to address a multiobjective FAP (frequency assignment problem) formulation. More precisely, two conflicting objectives – the interference cost and the separation cost – are considered to characterize FAP as an MO (multiobjective optimization) problem.

The contribution to this specific telecommunication problem in a real scenario follows a recent approach, for which the authors have already accomplished some preliminary results. In this paper, a much more complete analysis is performed, including two well‐known algorithms (such as the NSGA‐II and SPEA2), with new results, new comparisons and statistical studies. More concretely, in this paper five different algorithms are presented and compared. The popular multiobjective algorithms, NSGA‐II and SPEA2, are compared against the Differential Evolution with Pareto Tournaments (DEPT) algorithm, the Greedy Multiobjective Variable Neighborhood Search (GMO‐VNS) algorithm and its variant Greedy Multiobjective Skewed Variable Neighborhood Search (GMO‐SVNS). Furthermore, the authors also contribute with a new design of multiobjective metaheuristic named Multiobjective Artificial Bee Colony (MO‐ABC) that is included in the comparison; it represents a new metaheuristic that the authors have developed to address FAP. The results were analyzed using two complementary indicators: the hypervolume indicator and the coverage relation. Two large‐scale real‐world mobile networks were used to validate the performance comparison made among several multiobjective metaheuristics.

The final results show that the multiobjective proposal is very competitive, clearly surpassing the results obtained by the well‐known multiobjective algorithms (NSGA‐II and SPEA2).

The paper provides a comparison among several multiobjective metaheuristics to solve FAP as a real‐life telecommunication engineering problem. A new multiobjective metaheuristic is also presented. Preliminary results were enhanced with two well‐known multiobjective algorithms. To the authors' knowledge, they have never been investigated for FAP.

The purpose of this paper is to focus on sensor data fluctuations. Context‐aware applications in the real world adapt their behavior to contexts abstracted from real‐world situations sensed as physical quantities by heterogeneous and distributed sensors. Most of the adaptations are programmed as rules derived from human experience in making environments comfortable and efficient. Preparation of sufficient rules, however, is difficult because oversights and exceptional contexts are inevitable.

This paper, focuses on sensor data fluctuations and calculates the probabilities indicating the frequency of such fluctuations.

The results help to confirm the preset rules of the context‐aware applications and find previously unnoticed situations that the context‐aware applications should cope with.

The paper shows how this method is applied to log data captured in an office in order to evaluate the method's capabilities and consider the feasibility of abstracting the newly observed situations into rules.

The purpose of this paper is to obtain an optimal mobile robot path planning by the hybrid algorithm, which is developed by two nature inspired meta-heuristic algorithms, namely, cuckoo-search and bat algorithm (BA) in an unknown or partially known environment. The cuckoo-search algorithm is based on the parasitic behavior of the cuckoo, and the BA is based on the echolocation behavior of the bats.

The developed algorithm starts by sensing the obstacles in the environment using ultrasonic sensor. If there are any obstacles in the path, the authors apply the developed algorithm to find the optimal path otherwise reach the target point directly through diagonal distance.

The developed algorithm is implemented in MATLAB for the simulation to test the efficiency of the algorithm for different environments. The same path is considered to implement the experiment in the real-world environment. The ARDUINO microcontroller along with the ultrasonic sensor is considered to obtain the path length and time of travel of the robot to reach the goal point.

In this paper, a new hybrid algorithm has been developed to find the optimal path of the mobile robot using cuckoo search and BAs. The developed algorithm is tested with the real-world environment using the mobile robot.

Health scientists and urban planners have long been interested in the influence that the built environment has on the physical activities in which we engage, the environmental hazards we face, the kinds of amenities we enjoy, and the resulting impacts on our health. However, it is widely recognized that the extent of this influence, and the specific cause-and-effect relationships that exist, are still relatively unclear. Recent reviews highlight the need for more individual-level data on daily activities (especially physical activity) over long periods of time linked spatially to real-world characteristics of the built environment in diverse settings, along with a wide range of personal mediating variables. While capturing objective data on the built environment has benefited from wide-scale availability of detailed land use and transport network databases, the same cannot be said of human activity. A more diverse history of data collection methods exists for such activity and continues to evolve owing to a variety of quickly emerging wearable sensor technologies. At present, no “gold standard” method has emerged for assessing physical activity type and intensity under the real-world conditions of the built environment; in fact, most methods have barely been tested outside of the laboratory, and those that have tend to experience significant drops in accuracy and reliability. This paper provides a review of these diverse methods and emerging technologies, including biochemical, self-report, direct observation, passive motion detection, and integrated approaches. Based on this review and current needs, an integrated three-tiered methodology is proposed, including: (1) passive location tracking (e.g., using global positioning systems); (2) passive motion/biometric tracking (e.g., using accelerometers); and (3) limited self-reporting (e.g., using prompted recall diaries). Key development issues are highlighted, including the need for proper validation and automated activity-detection algorithms. The paper ends with a look at some of the key lessons learned and new opportunities that have emerged at the crossroads of urban studies and health sciences.

We do have a vision for a world in which people can walk to shops, school, friends' homes, or transit stations; in which they can mingle with their neighbors and admire trees, plants, and waterways; in which the air and water are clean; and in which there are parks and play areas for children, gathering spots for teens and the elderly, and convenient work and recreation places for the rest of us. (Frumkin, Frank, & Jackson, 2004, p. xvii)

The purpose of this paper is to demonstrate how Internet of Things (IoT) technology can enable highly distributed elevator equipment servicing by using remote-monitoring technology to facilitate a shift from traditional corrective maintenance (CM) and time-based maintenance (TBM) to more predictive, condition-based maintenance (CBM) in order to achieve various benefits.

Literature review indicates that CBM has advantages over conventional CM and TBM from a theoretical perspective, but it depends on continuous monitoring enhancement via advanced IoT technology. An in-depth case study was carried out to provide practical evidence that IoT enables elevator firms to achieve CBM.

From a theoretical perspective, the CBM of elevators makes business sense. The challenges lie in data collection, data analysis and decision making in real-world business contexts. The main findings of this study suggest that CBM can be commercialized via IoT in the case of elevators and would improve the safety and reliability of equipment. It would, thus, make sense from technological, process and economic perspectives.

Our longitudinal real-world case study demonstrates a practical way of making the CBM of elevators widespread. Integrating IoT and other advanced technology would improve the safety and reliability of elevator equipment, prolong its useful life, minimize inconvenience and business interruptions due to equipment downtime and reduce or eliminate major repairs, thus greatly reducing maintenance costs.

The main contribution of this paper lies in the empirical demonstration of the benefits and challenges of CBM via IoT relative to conventional CM and TBM in the case of elevators. The authors believe that this study is timely and will be valuable to firms working on similar research or commercialization strategies.

The purpose of this paper is to provide comprehensive information on preventive maintenance (PM) planning and methods used in the industry in order to achieve an effective maintenance system.

The literature review is organized in a way that provides the general overview of the researches done in the PM. This paper discusses the literatures that had been reviewed on four main topics, which are the holistic view of maintenance policies, PM planning, PM planning concept and PM planning-based in developing optimal planning in executing PM actions.

PM policy is one of the original proactive techniques that has been used since the start of researches on maintenance system. Review of the methods presented in this paper shows that most researches analyse effectiveness using artificial intelligence, simulation, mathematical formulation, matrix formation, critical analysis and multi-criteria method. While in practice, PM activities were either planned based on cost, time or failure. Research trends on planning and methods for PM show that the variation of approaches used over the year from early 1990s until today.

Research about PM is known to be extensively conducted and majority of companies applied the policy in their production line. However, most analysis and method suggested in published literatures were done based on mathematical computation rather than focussing on solution to real problems in the industry. This normally would lead to the problems in understanding by the practitioner. Therefore, this paper presented researches on PM planning and suggested on the methods that are practical, simple and effective for application in the real industry.

The originality of this paper comes from its detail analysis of PM planning in term of its research focus and also direction for application. Extensive reviews on the methods adopted in relation to PM planning based on the planning-based such as cost-based, time-based and failure-based were also provided.

Building information modelling (BIM) and radio frequency identification (RFID) technologies have been extensively explored to improve supply chain visibility and coordination of material flow processes, particularly in the pursuit of Industry 4.0. It remains challenging, however, to effectively use these technologies to enable the precise and reliable coordination of material flow processes. This paper aims to propose a new workflow designed to include the use of detailed look-ahead plans when using BIM and RFID technologies, which can accurately track and match both the dynamic site needs and supply status of materials.

The new workflow is designed according to lean theory and is modeled using business process modeling notation. To digitally support the workflow, an integrated BIM-RFID database system is constructed that links information on material demands with look-ahead plans. The new workflow is then used to manage material flows in the erection of an office building with prefabricated columns. The performance of the new workflow is compared with that of a traditional workflow, using discrete event simulations. The input for the simulations was derived from expert opinion in semi-structured interviews.

The new workflow enables contractors to better observe on-site status and differences between the actual and planned material requirements, as well as to alert suppliers if necessary. The simulation results indicate that the new workflow has the potential to reduce the duration of the material flow processes by 16.1% compared with the traditional workflow.

The new workflow is illustrated using a real-world-like situation with input data based on expert opinion. Although the workflow shows potential, it should be tested on a real-world site.

The new workflow allows project participants to combine detailed near-term look-ahead plans with BIM and RFID technologies to better manage material flow processes. It is particularly useful for the management of engineer-to-order components considering the dynamic site progress.

The research improves on existing research focused on using BIM and RFID technologies to improve material flow processes by showing how the workflow can be adapted to use detailed look-ahead plans. It reinforces data-driven construction material management practices through improved visibility and reliability in planning and control of material flow processes.

The purpose of this paper and its companion (Part I: single and two‐component supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In this part, attention is devoted to multi‐silo supply chain and the relationships between the components. The first part of the paper aims to consider two types of experimental supply chains: with one‐to‐many and many‐to‐one relationships. The second half of the paper aims to present two approaches on optimising the material flow in the real‐world supply chain network.

Cooperative coevolutionary and classical sequential approaches are taken to address the experimental multi‐silo supply chains. Due to the nature and the complexity of the supply chain presented in the second half of the paper, evolutionary algorithm was not sufficient to tackle the problem. A fuzzy‐evolutionary algorithm is proposed to address the problem.

The proposed systems produce solutions better than solutions proposed by human experts and in much shorter time.

The paper discusses various algorithms to provide the decision support for the real‐world problems. The system proposed for the real‐world supply chain is in the process of integration to the production environment.

The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.