Search Operations Research Articles

Mathematical model and adaptive multi-objective evolutionary algorithm for cellular manufacturing with mixed production mode

As the product mix in production changes dramatically, cell reconfiguration is requisite to smoothen the production process. Meanwhile, multiple production modes are simultaneously adopted in the site to promote productivity and assure flexibility, and thus the coordination scheduling among them becomes a challenging problem. To achieve cell reconfiguration and cell scheduling in a cellular manufacturing system in which no-idle flow-line and flexible job-shop production modes are hybridized, a mixed integer linear programming model is formulated and an enhanced adaptive multi-objective evolutionary algorithm is developed. In the proposed algorithm, a decision tree-based rule combination selector is developed to adaptively select the most appropriate rule combination fit for the given production scenario to generate a high-quality initial population. Three types of crossover operators and six objective-oriented local search operators are designed to increase the exploration and exploitation capability. An adaptive balance mechanism of exploration and exploitation is trained by Q-learning to maximize search efficiency. In addition, an adaptive adjustment mechanism of population size is designed to ensure diversity and speed up convergence. The comparative study demonstrates that the three proposed adaptive mechanisms are effective and the proposed algorithm with three adaptive mechanisms significantly outperforms other comparison algorithms in solving the studied problem.

Handling dynamic capacitated vehicle routing problems based on adaptive genetic algorithm with elastic strategy

With the development of e-commerce platforms, the logistics industry is also booming. The transportation process is at the core of the logistics industry, which influences the timeliness and rationality of logistics distribution. In the real world, there are many dynamic demands in logistics distribution, so logistics distribution should deal with dynamic demands quickly to realize more effective route planning. This paper proposes an adaptive genetic algorithm (AGA) with elastic strategy (AGA-ES) to solve the dynamic capacitated vehicle routing problems (DCVRPs). Firstly, AGA designs an adaptive local search operator, which can adaptively adjust the quantity of nodes that need local search, according to the fitness value of different individuals. At the same time, AGA develops adaptive crossover and mutation operators, which can automatically adjust the crossover and mutation probability based on the different fitness values. The experimental results verify the better performance of AGA on the traditional capacitated vehicle routing problems (CVRPs). After that, AGA combines elastic strategy (AGA-ES) to solve DCVRPs. This paper mainly concerns three types of actual demand information in DCVRPs: distribution nodes increase, distribution nodes decrease, and distribution road conditions change, according to the actual background. AGA-ES could interact with demand information and judge the types of different demands, and then adaptively plan new and reasonable routes after receiving one or more of the above demand information. The verification of benchmark data sets shows that AGA-ES can effectively deal with DCVRPs. Furthermore, this paper collects the longitude and latitude coordinates of 100 SF express stations in Xi’an, constructs the distribution information of these express stations, and verifies that AGA-ES can solve the DCVRPs under the actual background.

A machine learning-based motion training approach applied to multilegged and bipedal robots

To promptly gain an understanding of disasters as they occur and to draft plans for search and rescue operations, various types of robots are used. Robots not only increase rescue efficiency but also reduce firefighter casualties. Therefore, this study investigated how bionic robots can be utilized to search destroyed and chaotic disaster sites as quickly as possible. However, manually adjusting the motion parameters of robots performing robot motions is extremely inefficient. To resolve this problem, this study used machine learning algorithms to allow robots to train themselves in the environment and autonomously determine their optimal motion parameters. However, many types of machine learning algorithms exist, each with their own strengths and weaknesses. Therefore, this study designed a series of experiments to investigate the features of each algorithm in optimizing the robots’ motions; subsequently, this study compared the strengths and weaknesses of each algorithm based on their performance. The results indicated that for both multipedal and bipedal robots, the use of machine learning to find the optimal motion parameters is both feasible and practical.

Optimizing Postprocessing of Range-Gated Viewing Data for Maritime Search and Rescue Operations at Night and in Bad Weather Conditions

At night and in bad weather conditions the detection of persons and objects floating in the sea represents a major challenge for search and rescue operations (SAR). If conventional searchlights are used, backscattering from rain, fog and snow decreases detection range. Therefore, a compact and inexpensive range-gated viewing system which significantly reduces atmospheric backscattering was developed. The instrument was designed for detection ranges of several hundred meters. In this study, different image processing techniques were analyzed in terms of improved object detectability for a human observer and for a machine learning-based object detector, based on a real-world image dataset. On the one hand, noise of the camera is reduced by performing a non-uniformity correction (NUC) and on the other, the dynamic range of the images is adjusted and dark objects are accentuated by equalizing (EQ). The aim of this field study with the subsequent post processing steps was to improve visibility for both human observers and machine learning-based object detectors with low computing power, based on real-world image datasets. The results show that processing requirements are different in both cases, mainly due to human eye perception, which an automated detector does not rely on and therefore the performance of the object detector before the equalizing step is slightly better. However, the NUC improves the image quality in any case.

Modeling and multi-objective optimization for energy-aware scheduling of distributed hybrid flow-shop

With the development of economic globalization and sustainable manufacturing, energy-aware scheduling of distributed manufacturing systems has become a research hot topic. However, energy-aware scheduling of distributed hybrid flow-shop is rarely explored. Thus, this paper is the first attempt to study an energy-aware distributed hybrid flow-shop scheduling problem (DHFSP). We formulate a novel mathematical model of the DHFSP with minimizing makespan and total energy consumption (TEC) criteria. A hybrid multi-objective iterated greedy (HMOIG) approach is proposed to address this energy-aware DHFSP. In this proposed HMOIG, firstly, a new energy-saving method is presented and introduced into the model for reducing TEC criterion. Secondly, an integration initialization scheme is devised to produce initial solutions with high quality. Thirdly, two properties of DHFSP are used to invent a knowledge-based local search operator. Finally, we validate the effectiveness of each improvement component of HMOIG and compare it with other well-known multi-objective evolutionary algorithms on instances and a real-world case. Experimental results manifest that HMOIG is a promising method to solve this energy-aware DHFSP.

Exploring refugees' experience of accessing dental health services in host countries: a scoping review.

Refugees often face worse oral health outcomes, such as periodontal diseases and dental caries in host countries due to barriers including language and cultural differences, institutional discrimination, and restricted use of dental health services. This scoping review aims to map and summarise the available studies on refugees' experience of accessing dental health services in the host countries, to identify the main characteristics of the dental health services that refugees access and to explore the barriers and enablers to navigate the dental health service system in their host countries. The Joanna Briggs Institute (JBI) framework was adopted. PubMed, Scopus, Assia, CINAHL and Social Services Abstract were searched. A search strategy was developed using Medical Subject Headings (MeSH) terms and a combination of search operators and syntax used in MEDLINE were adopted for the remaining databases. Data were synthesised using thematic analysis. Fourteen articles were included. Most studies used qualitative methods and Australia seemed to be the country with the highest number of publications surrounding this topic. The included studies showed that refugees frequently encountered substantial obstacles when attempting to access dental services in host countries. Numerous barriers such as language barriers, cultural differences, and lack of health insurance or financial support hindered refugees' ability to access these services. Additionally, many refugees possessed limited knowledge of the dental care system in their new country. As a result of untreated dental problems, refugees suffered from pain and other health complications. This scoping review explored the challenges refugees have experienced in accessing dental health services in host countries, which included the key barriers such as affordability, accessibility, accommodation, availability, awareness, and acceptability. The scarcity of relevant research highlighted the need for a more comprehensive understanding of refugees' experiences accessing dental health services in host countries. Limited data were identified regarding evidence focusing on the characteristics of dental services accessed by refugees in host countries.

First Responder's Standard Operating Procedure for Employing Drones to Expedite Insurance Claims

Drones offer cost-effective real-time data collection, payload delivery, and have accelerated various commercial, industrial, and recreational applications. They are commonly used for research, disease transmission monitoring, disaster site assessment, and biohazard monitoring. However, disaster victim insurance claims are hampered by insufficient cooperation between first responders, typically the Civil Defence Department, and the insurance industry. This paper proposes integrating drones into standard operating procedures for disaster victim identification by first responders. Improved collaboration between first responders and insurers can expedite insurance claims, benefiting both parties and disaster victims. Insights are drawn from feedback provided by Civil Defence Department Malaysia's first responders and experienced forensic experts in search and rescue operations

A Study on the Effect of Belly‐Dragging Locomotion on a Robot that Mimics a Heavy Reptile

This study investigates the effect of tortoise‐inspired locomotion on enhancing energy efficiency in legged robots. Tortoises, known for their high locomotion efficiency, provide a unique model for robot and gait design, where their specific gait and posture significantly contribute to energy efficiency. This study hypothesizes that the gait and belly‐dragging posture of tortoises can improve the energy efficiency of robots. To test this hypothesis, a quadruped robot mimicking these tortoise characteristics is developed, utilizing the cost of transport (CoT) as a measure of energy efficiency. Dynamic simulations and real‐world experiments are conducted, varying parameters like robot size, mass, friction coefficient, and Froude number, to validate the generality of the findings. The results indicate that both belly dragging, a postural characteristic of tortoises, and their diagonal gait significantly lower the robot's CoT. This suggests that the energy‐efficient locomotion of tortoises, specifically the diagonal gait with belly dragging, is transferable to robotic platforms to enhance energy efficiency. The tortoise‐inspired robot design offers significant potential in applications such as search and rescue operations, space exploration, and payload transportation. Furthermore, the tortoise‐inspired locomotion strategy can be effectively integrated with other research focusing on energy efficiency improvements through mechanical structures or control strategies.

An adaptive shuffled frog-leaping algorithm for parallel batch processing machines scheduling with machine eligibility in fabric dyeing process

Fabric dyeing is an important production process in clothing industry and consists of some batch processing machines (BPM). In this study, parallel BPM scheduling problem with machine eligibility in fabric dyeing process is considered and an adaptive shuffled frog-leaping algorithm (ASFLA) is presented to minimise makespan and total tardiness simultaneously. A heuristic is used to produce initial population. Evolution quality of population on the last generation is used to implement adaptive population division and adaptive search process in each memeplex on the current generation. Search process of each memeplex is divided into some segments and search operators are dynamically adjusted after each segment is done. Adaptive elimination is performed on the worst memeplex. Extensive experiments are conducted to test the performance of ASFLA and the computational results demonstrate that new strategies of ASFLA are effective and ASFLA has promising advantages on the considered problem.

Collaborative robots (cobots) for disaster risk resilience: a framework for swarm of snake robots in delivering first aid in emergency situations.

Cobots are robots that are built for human-robot collaboration (HRC) in a shared environment. In the aftermath of disasters, cobots can cooperate with humans to mitigate risks and increase the possibility of rescuing people in distress. This study examines the resilient and dynamic synergy between a swarm of snake robots, first responders and people to be rescued. The possibility of delivering first aid to potential victims dispersed around a disaster environment is implemented. In the HRC simulation framework presented in this study, the first responder initially deploys a UAV, swarm of snake robots and emergency items. The UAV provides the first responder with the site planimetry, which includes the layout of the area, as well as the precise locations of the individuals in need of rescue and the aiding goods to be delivered. Each individual snake robot in the swarm is then assigned a victim. Subsequently an optimal path is determined by each snake robot using the A* algorithm, to approach and reach its respective target while avoiding obstacles. By using their prehensile capabilities, each snake robot adeptly grasps the aiding object to be dispatched. The snake robots successively arrive at the delivering location near the victim, following their optimal paths, and proceed to release the items. To demonstrate the potential of the framework, several case studies are outlined concerning the execution of operations that combine locomotion, obstacle avoidance, grasping and deploying. The Coppelia-Sim Robotic Simulator is utilised for this framework. The analysis of the motion of the snake robots on the path show highly accurate movement with and without the emergency item. This study is a step towards a holistic semi-autonomous search and rescue operation.

An improved finegrained ciphertext policy based temporary keyword search on encrypted data for secure cloud storage

We present a temporary keyword search over sensitive and confidential health data in a cloud environment. The cloud constitutes a semi-trusted domain, making it necessary for data owners to secure their data before outsourcing it through techniques like encryption. Attribute-based keyword search techniques tend to perform a search operation using a search token generated by an authorized user. These search tokens can lead to serious privacy threats, as they can extract all ciphertexts that may have been generated along with their keyword. Therefore, restricting search tokens to extract ciphertexts generated within a time interval is a more promising solution. In this paper, we present a novel ciphertext policy fine-grained temporary keyword that prevents the misuse of these search tokens. Further, it mitigates the risk of insider threats within healthcare organizations by limiting the window of opportunity for unauthorized access to minimum. To assess the security, our proposed scheme is formally proven to be secure against Selectively Chosen Keyword Attacks in the generic bilinear group model. Additionally, we demonstrate that the encryption algorithm’s complexity is linear in relation to the number of attributes. Our scheme’s significance and practicality are revealed by the performance evaluation.

Top 25 Most-Cited Articles on Robotic-Assisted Lumbar Spine Surgery.

Robot-guided lumbar spine surgery has evolved rapidly with evidence to support its utility and feasibility compared with conventional freehand and fluoroscopy-based techniques. The objective of this study was to assess trends among the top 25 most-cited articles pertaining to robotic-guided lumbar spine surgery. An "advanced document search" using Boolean search operator terms was performed on 16 November 2022 through the Web of Science and SCOPUS citation databases to determine the top 25 most-referenced articles on robotic lumbar spine surgery. The articles were compiled into a directory and hierarchically organized based on the total number of citations. Cumulatively, the "Top 25" list for robot-assisted navigation in lumbar spine surgery received 2240 citations, averaging 97.39 citations annually. The number of citations ranged from 221 to 40 for the 25 most-cited articles. The most-cited study, by Kantelhardt et al, received 221 citations, averaging 18 citations per year. As utilization of robot-guided modalities in lumbar spine surgery increases, this review highlights the most impactful studies to support its efficacy and implementation. Practical considerations such as cost-effectiveness, however, need to be better defined through further longitudinal studies that evaluate patient-reported outcomes and cost-utility. Through an overview of the top 25 most-cited articles, the present review highlights the rising prominence and technical efficacy of robotic-guided systems within lumbar spine surgery, with consideration to pragmatic limitations and need for additional data to facilitate cost-effective applications.

The impact of electrical hazards from overhead power lines on urban search and rescue operations during extreme flood events

Accurate flood forecasting and efficient emergency response operations are vital, especially in the case of urban flash floods. The dense distribution of power lines in urban areas significantly impacts search and rescue operations during extreme flood events. However, no existing emergency response frameworks have incorporated the impacts of overhead power lines on lifeboat rescue operations. This study aims to determine the necessity and feasibility of incorporating overhead power line information into an emergency response framework using Manville, New Jersey during Hurricane Ida as a test bed. We propose an integrated framework, which includes a building-scale flood model, urban point cloud data, a human vulnerability model, and network analysis, to simulate rescue operation feasibility during Hurricane Ida. Results reveal that during the most severe point of the flood event, 46% of impacted buildings became nonrescuable due to complete isolation from the road network, and a significant 67.7% of the municipality’s areas that became dangerous for pedestrians also became inaccessible to rescue boats due to overhead power line obstruction. Additionally, we identify a continuous 10-hour period during which an average of 43.4% of the 991 impacted buildings faced complete isolation. For these structures, early evacuation emerges as the sole means to prevent isolation. This research highlights the pressing need to consider overhead power lines in emergency response planning to ensure more effective and targeted flood resilience measures for urban areas facing increasingly frequent extreme precipitation events.

Prediction of RNA Secondary Structure Using Butterfly Optimization Algorithm

Ribonucleic acid (RNA) structure is vital to its ability to function within the cell. The ability to predict RNA structure is essential to implementing new medications and understanding genetic illnesses. It is also important in synthetic and computational biology. All these functions are directly related to its secondary structure. Also prediction of RNA secondary structure process is the most significant step to determining the tertiary structure of RNA. On account of this, prediction of secondary structure of RNA is the crying topic in bioinformatics. In this research, we present the swarm-based metaheuristic Butterfly Optimization Algorithm (BOA) method for predicting the secondary structure of RNA. The main feather of the BOA is that it can conduct both local and global search simultaneously. According to the problem perspective, we have redesigned the operators of BOA to perform global and local search operations in different ways. We have followed a thermodynamic model for the selection of the stable secondary structure with minimum Gibbs free energy. Predicting the minimum free energy value we also developed an “Optimize” function to search the new optimize structure. This function increases the prediction efficiency, creating new stable structure and also decreases the time complexity of global searching procedure. We have used a public dataset to perform the prediction operation. To accuse our prediction efficiency, we have compared our outcomes to existing popular algorithms. The result shows that the proposed approach can predict secondary RNA structure better than other state-of-the-art algorithms.

Necessary prerequisites for evidence-based practice: results of investigating nurses’ informatics competency and information literacy skills

Abstract This study aimed to evaluate the correlation between nursing informatics (NI) competency and information literacy skills for evidence-based practice (EBP) among intensive care nurses. This cross-sectional study was conducted on 184 nurses working in intensive care units (ICUs). The study data were collected through demographic information, Nursing Informatics Competency Assessment Tool (NICAT), and information literacy skills for EBP questionnaires. The intensive care nurses received competent and low-moderate levels for the total scores of NI competency and information literacy skills, respectively. They received a moderate score for the use of different information resources but a low score for information searching skills, different search features, and knowledge about search operators, and only 31.5% of the nurses selected the most appropriate statement. NI competency and related subscales had a significant direct bidirectional correlation with information literacy skills for EBP and its subscales (P < 0.05). Nurses require a high level of NI competency and information literacy for EBP to obtain up-to-date information and provide better care and decision-making. Health planners and policymakers should develop interventions to enhance NI competency and information literacy skills among nurses and motivate them to use EBP in clinical settings.

CFD aided investigation of a three-blade propeller in multirotor UAV applications

In the recent years a rapid increase of multirotor UAVs in the commercial market is observed resulting in a large number of motor/propeller concepts and thrust architectures. The limited availability of data for the aerodynamic performance of the motor/propeller system often leads to a non-optimal operation on multirotor UAVs design points. Since experimental investigations are both cost- and time-demanding, the accurate CFD modeling of UAV propellers is crucial and highly supportive in the early design phases of multirotor UAVs. In the current study, a CFD framework is employed for the performance investigation of a small-scale three-blade propeller on a lightweight micro quadrotor UAV, designed for indoor search and rescue operations. More specifically, two widely implemented methods for propeller modeling are examined, namely the Multiple Reference Frame (MRF) and the Sliding Mesh (SM). Several operating points are investigated, corresponding to different propeller rotating speeds (RPM) and Reynolds numbers. The accuracy of each method is evaluated by comparing the CFD results with those obtained from literature experimental data. Finally, the uncertainty of the computational methods is quantified through Richardson’s extrapolation method.

Preliminary design of a multirotor UAV for indoor search and rescue applications

Multirotor UAVs have become an essential tool in a wider range of applications, including among others disaster management, and search and rescue (SAR) operations. Typically, these systems operate outdoors, with their guidance and positioning being based primarily on GPS. This work is focused on the design and optimization of a multirotor UAV specifically tailored for indoor SAR applications, where GPS signal is unavailable, and obstacles are prevalent. The design incorporates a lightweight frame structure, in order to increase the UAV’s payload capability. This is necessary, since the UAV requires multiple obstacle recognition and avoidance sensors, as well as thermal and optical cameras, to successfully accomplish its mission objectives in a GPS-denied environment. Towards this goal, various trade studies were conducted including different motor/propeller configurations and airframe FEM analyses. The aerodynamic performance of the UAV is evaluated also, using dedicated CFD analyses that incorporate the effect of propellers. Lastly, a prototype of the designed configuration is produced using additive manufacturing methods and initial flight tests of the UAV are performed.

Implementation of A Blockchain-based Searchable Encryption for Securing Contact Tracing Data

<p>We developed a blockchain-based multi-keyword searchable encryption scheme for securing COVID-19 contact tracing data. In this scheme, we used AES-GCM to encrypt the contact tracing data, guaranteeing that only authorized users can perform decryption. Our scheme employed a Ciphertext-Policy Attribute-based searchable encryption to encrypt the search index, ensuring only users with appropriate attributes can perform search operations. The scheme supports dynamic updates of the search index. The blockchain-based storage with smart contract ensures immutability and non-repudiation of storage and retrieval. Overall, the evaluation of the scheme shows that it works efficiently without compromising the security goals. This is one of the first works to implement a solution for secure storage and search of contact tracing data with blockchain-based searchable encryption. Compared to the existing searchable contact tracing schemes, it provides more features and maintains efficiency even if a large search index is used.</p> <p> </p>

Ensemble evolutionary algorithms equipped with Q‐learning strategy for solving distributed heterogeneous permutation flowshop scheduling problems considering sequence‐dependent setup time

AbstractA distributed heterogeneous permutation flowshop scheduling problem with sequence‐dependent setup times (DHPFSP‐SDST) is addressed, which well reflects real‐world scenarios in heterogeneous factories. The objective is to minimise the maximum completion time (makespan) by assigning jobs to factories, and sequencing them within each factory. First, a mathematical model to describe the DHPFSP‐SDST is established. Second, four meta‐heuristics, including genetic algorithms, differential evolution, artificial bee colony, and iterated greedy (IG) algorithms are improved to optimally solve the concerned problem compared with the other existing optimisers in the literature. The Nawaz‐Enscore‐Ham (NEH) heuristic is employed for generating an initial solution. Then, five local search operators are designed based on the problem characteristics to enhance algorithms' performance. To choose the local search operators appropriately during iterations, Q‐learning‐based strategy is adopted. Finally, extensive numerical experiments are conducted on 72 instances using 5 optimisers. The obtained optimisation results and comparisons prove that the improved IG algorithm along with Q‐learning based local search selection strategy shows better performance with respect to its peers. The proposed algorithm exhibits higher efficiency for scheduling the concerned problems.

Optimization and analysis of air-to-ground wireless link parameters for UAV mounted adaptable Radar Antenna Array

Unmanned Aerial Vehicles (UAVs) are rapidly used in a variety of applications such as surveillance, search and rescue operation, delivery, etc. These operations strongly depend on the performance of the air-to-ground (A2G) communication link. This link communicates vital information between the UAV and the ground station (GS). However, due to the UAV’s unpredictable mobility, the A2G link becomes unreliable, which limits the quality of data transfer. An Adaptable Radar Antenna Array (ARAA) can be used to solve this problem by effectively handling transmitted data through altering the antenna pattern using the beamforming process. This work proposes a new algorithm for selecting antenna patterns with proper beamforming by considering the effect of wobbling and mutual coupling (MC). In addition, the proposed algorithm is used to reduce the maximum sidelobe level (SLL) and optimize the distance between the interelement of ARAA, which directly controls the MC effect in a time-varying channel. Also, an analysis is done for various antenna configurations, such as Uniform Linear Array (ULA), Uniform Rectangular Array (URA), and Uniform Circular Array (UCA), while varying different UAV parameters like altitude, speed, location error, and beamforming capacity.