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We consider the problem of designing lightweight, load-bearing planar frame structures for additive manufacturing, which can be formulated as a nonlinear, non-convex mathematical programming problems. Even using state-of-the-art commercial solvers, exact methods are only capable of solving small unrealistic instances (with very few variables). In this paper, we develop a heuristic method which is fast and capable of solving the design problem for larger-scale, weight-optimized, planar frame structures for additive manufacturing. The approach explicitly considers manufacturability constraints stemming from the use of additive manufacturing technology and leverages the problem structure imposed by these constraints. The proposed heuristic method is based on iteratively resolving a relaxed master problem on a reduced ground structure. The approach differs from the existing methods in two important aspects. First, we consider planar frame optimization master problem directly (instead of simpler but less relevant truss optimization). Secondly, we employ both element and node addition, which allows us to enforce additive manufacturability constraints without using binary variables (and hence, avoiding the need for computationally expensive integer programming). [ABSTRACT FROM AUTHOR]

In the present era, energy is progressively turning into the major limitation in designing multicore chips. However, power and performance are the primary segments of energy, which are contrarily correlated in multicore architectures. This research primarily focused on optimizing energy level of multicore chips using parallel workloads by utilizing either power or execution advancement based on machine learning computation on dynamic programming. To do as such, the novel dynamic machine learning‐based heuristic energy optimization (DML‐HEO) algorithm has been designed and developed in this research on application‐specific controllers to optimize energy‐level on multicore architecture. Here DML‐HEO is implemented on the controller to maximize the execution inside a fixed power spending plan or to limit the expended capacity to accomplish a similar pattern execution. The controller is additionally scalable as it does not bring about critical overhead due to the increase in quantity of cores. The strategy has been assessed utilizing controllers on a full‐framework test system at lab‐scale analysis. The experimental results demonstrate that our proposed DML‐HEO system shows improving performance than the traditional system. [ABSTRACT FROM AUTHOR]

Demand for hydrogen vehicles is steadily increasing due to sustainable development and strengthening of policies to support the hydrogen industry. However, due to the lack of hydrogen charging infrastructure, continuous operation is limited, which is an obstacle to the expansion of the hydrogen car market. To solve this problem, steady expansion of fixed hydrogen charging stations (HCSs) is underway, but the use of mobile hydrogen charging stations (MHCSs) is also actively considered. MHCSs can compensate for the shortcomings of expanding charging infrastructure due to the establishment cost of fixed HCSs and the selection of sites and contribute to resolving hydrogen charging blind spots. In this study, optimal operation of a number of MHCSs was conducted. To derive the optimal operation schedule, a mathematical optimization model and an alternative solution approach, a Multiple Vehicle and Multiple Trip Routing Algorithm for Selective Use and Split Service (MVMTRA-SUSS) was developed. Case study, sensitivity analysis, and numerical experiments have verified the practical applicability of the proposed model. [Display omitted] • The utilization of MHCSs is proposed to expand and improve charging infrastructure. • A mathematical optimization model is developed to derive optimal operation plan. • Multi-vehicle, multi-trip, split service and selective use of vehicle are explored. • A heuristic algorithm(MVMTRA-SUSS) is proposed to address computational complexity. • The practicality of the model and algorithm is verified via numerical experiments. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic time, educational institutions have really played a good role in imparting online education to students. Their career and academic tenure were not affected as contrary to the past pandemics throughout world history. All this has been possible through long sessions of classes, quizzes, assignments, discussions, chat interactions, and examinations through online video-based learning using computer interactive measures. The students were privileged to utilize digital technologies for longer durations for learning purposes. However, these long stretches have adversely affected their body postures, and physical and mental health as they majorly remain confined to chairs with restricted levels of physical activities. Thus, there is a need to have a model which can act as an insight for parents, doctors (pediatricians), and academic policymakers to decide on maximum hours for online teaching and related activities during future pandemics. The novel model proposed in this work helps to predict the impact of enhanced students' computer interactions on their physical and mental health. The method proposed uses a novel model which is advanced and computationally strong. The model follows a two-step methodology, where at the first level, a variant of already existing machine learning algorithm is proposed and at the next level, it is optimized further using a hybrid bio-inspired optimization algorithm. The model consists of proposing a variant of XGBoost model (step1 optimization) followed by a hybrid bio-inspired algorithm (step2 optimization). The work considers a humongous dataset with varied age groups of students with more than 10 attributes. The proposed model is highly efficient in making predictions with 98.07% accuracy level and 98.43% F1-score. The time complexity of the model obtained is also of order of "n" where "n" depicts the number of input variables. Strong empirical results for other parameters also like specificity (95.63%) and sensitivity (96.74%) ascertain the enhanced predictive power generated using the proposed model. An extensive comparative study with other machine learning models ascertains the elevated accuracy and predictive power using the proposed model. Till now none of the researchers have proposed any such pioneering tool for parents, doctors, and academicians using advanced machine learning algorithms. [ABSTRACT FROM AUTHOR]

In maritime container terminals, yards have a primary role in permitting the efficient management of import and export flows. In this work, a mixed 0/1 linear programming model and a heuristic approach are proposed for defining storage rules in order to minimize the space used in the export yard. The minimization of land space is pursued by defining the rules to allocate containers into the bay-locations of the yard, in such a way as to minimize the number of bay-locations used and the empty slots within them. The main aim of this work is to propose a solution approach for permitting the yard manager to compare yard storage strategies for different transport demands, in such a way to be able to evaluate and, eventually, modify the storage strategy when the characteristics of the transport demand change. Computational experiments, based on both real instances and generated ones, are presented. All instances are derived by a case study related to an Italian terminal. [ABSTRACT FROM AUTHOR]

At present, the cloud computing environment (CCE) has emerged as one of the significant technologies in communication, computing, and the Internet. It facilitates on-demand services of different types based on pay-per-use access such as platforms, applications and infrastructure. Because of its growing reputation, the massive requests need to be served in an efficient way which gives the researcher a challenging problem known as task scheduling. These requests are handled by method of efficient allocation of resources. In the process of resource allocation, task scheduling is accomplished where there is a dependency between tasks, which is a Directed Acyclic Graph (DAG) scheduling. DAG is one of the most important scheduling due to wide range of its applicable in different areas such as environmental technology, resources, and energy optimization. NP-complete is a renowned concern, so various models deals with NP-complete that have been suggested in the literature. However, as the Quality of Service (QoS)-aware services in the CCEplatform have turned into an attractive and prevalent way to provide computing resources emerges as a novel critical issue. Therefore, the key aim of this manuscript is to develop a novel DAG scheduling model for optimizing the QoS parameters in the CCEplatform and validation of this can be done with the help of extensive simulation technique. Each simulated result is compared with the existing results, and it is found that newly developed algorithm performs better in comparison to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The multi-path progression of an arterial signal model, generally, is applied to arterial traffic scenarios with large turning flows. However, existing methods generally fail to capture traffic flow uncertainty, which leads to high sensitivity to fluctuations in traffic flow. To bridge this gap, in this study, a heuristic approach for multi-path signal progression is proposed to deal with the uncertainties of flow fluctuation by using distributionally flow scenarios. The model varies the phase sequence and the offsets of each intersection to achieve optimal progression with weighting of efficiency and stability. The preference degree of the efficiency and stability of the model is selected by adjusting the efficiency stability coefficient and solved by using a genetic algorithm. A case study and comparison experiment with benchmark models is presented and analyzed to prove the advantages of the proposed model. The results show that the standard deviation of the proposed model decreases by 45% as compared with conventional methods. It indicates that the model proposed in this paper can reduce congestion due to uncertainties, and can significantly improve stability, on the premise of ensuring that the efficiency index maintains a better value. [ABSTRACT FROM AUTHOR]

In this work, landslide assessment of the Doboj City area was modeled by combining machine learning and heuristic tools. The machine learning part was used to map the Morphometric factor. i.e. probability of landslides based on relation between the magnitude of events and morphometric parameters: elevation, distance to streams, slope, profile curvature, and aspect. The Random Forest and Support Vector Machines algorithms were implemented in the learning protocol, which included several strategies: balancing of the training/testing set size, algorithm optimization via cross-validation, and cross-scaling. The best performing Morphometric factor ap was created by learning on 50 m and testing on 25 m dataset. The heuristic part was used for modeling of Lithological factor and Land Cover factor maps, by expert-driven scoring of their units, within 0-1 range of values. The final Susceptibility map was obtained by multiplying all three factor maps resulting in a high-performing model with AUC=0.97 and acc=92%. [ABSTRACT FROM AUTHOR]

Production systems have been growing exponentially in size in recent decades. One of the problems in the manufacturing system is that a need of effective and efficient materials transportation between the workstations. Auto-mated guided vehicles (AGV's) can provide a satisfactory solution to this issue. Because of this, AGV's is a one of the most important material handling tool used in flexible manufacturing system (FMS). The designing, scheduling and routing of the AGV's are the key issues for the reason that performance of AVG's in flexible manufacturing system depends on these parameters. The failure of AGVs may significantly impact the operation and efficiency of the entire system, and it is a critical issue. In this paper the aims to answer How the reliability of individual AGVs in the system assessed?, and How AGVs affects the performance of the whole FMS system?. It was also discussed that optimizing techniques to improve the performance of AGV in FMS. This paper discussed the details studies of approaches and optimization techniques used for optimizing AGV's in FMS including design and their control. [ABSTRACT FROM AUTHOR]

This paper studies the multiple-choice knapsack problem with setup (MCKS) which is a generalization of the knapsack problem with setup (KPS) where the items can be processed in multiple periods. The Integer Linear Programming (ILP) of the MCKS shows its limitations when solved using CPLEX 12.7 solver, and the existing algorithms VNS&IP and VND-LB from the literature outperform the ILP and provide the best-known solutions but solve to optimality only 29 out of 120 instances, respectively. This paper is dedicated to exposing an Iterative Integer linear programming-based heuristic (IILP-H) that deals with the MCKS. The IILP-H has been experimented on the MCKS benchmark instances. A sensitivity analysis of the MCKS parameters is provided. A comparison of the IILP-H with the upper bound obtained by the CPLEX 12.7 solver and the best state-of-the-art algorithms has been conducted. The numerical results show that the IILP-H outperforms all the existing solving techniques when it comes to solution quality and computation time. It reaches 120 out of 120 best solutions, including 77 out of 120 optimal and 69 new best solutions. The complexity of the MCKS and the nature of the solutions obtained by the IILP-H are studied regarding the number of periods to which a family is assigned. [ABSTRACT FROM AUTHOR]

The primary goal of the binary model in this study was to understand the convergence pattern of the Pheidole latinoda ants. Forager and scout ants on the hunt for food use path integration. When they find a food source, they leave a trail pheromone to alert other nest mates. Every ant starts following that trail and reinforces it on their way back home. To investigate the ant convergence pattern, binary and ternary bridges of varying lengths are used. Each bridge is built in such a way that one end is connected to a food source whilst the other end is connected to the nest. The food source is surrounded by water-filled islands. The Pheidole latinoda ant's convergence pattern has been observed following the successful installation of a bridge near the ants' nest. This species took between 1 and 3 and 3–4 min to find the shortest possible path. Numerous studies looking for optimal solutions, such as those addressing the challenges of travelling salesmen, routing in communication networks, etc., may use this convergence or path optimization as their new starting point. • Optimal Foraging Behavior : Ants' shortest path convergence inspires ACO's powerful heuristic approach. • Efficient ACO Algorithm: ACO's pheromone strategy solves problems like defense robots, salesmen, and power control. • Modified Binary Bridge Model: Reveals ants' traffic patterns with food on an island. • Superorganism Concept for Algorithms : Ants prioritize pheromones, inspiring advanced algorithms for TSP and cell-tracking. [ABSTRACT FROM AUTHOR]

Software-Defined Vehicular Networks (SDVNs) have been a vital addition to the design of intelligent vehicular networks. SDVNs elevate the constraints of static hardware network devices by using programmable units, providing a global view of the network status, and standardizing the interface between different wireless access technologies. However, the static deployment and assignment of switches to control units do not consider vehicular network's rapid mobility changes and diverse densities. In this article, we propose a mobility-based heuristic switch migration scheme for software-defined vehicular networks. The proposed approach utilizes vehicles' mobility among switch-enabled roadside units to efficiently migrate selected switches between different controllers. The proposed scheme efficiently migrated switch-enabled roadside units between distributed control units while maintaining low vehicles' migration delay and cost. We have evaluated the proposed method under different environments and scenarios and reported its migration cost and delay performance. [ABSTRACT FROM AUTHOR]

Learning science is the most exciting part of being a high school student. The fun learning through exploration and laboratory experiments is the best part of science. This study aimed to test the effectiveness of the heuristic approach with the teacher-made worksheets as a teaching innovation in distance learning in helping the respondents to understand the topics of General Biology 2. This research employed a mixed-method; the qualitative data were evaluated using the phenomenological technique, while the quantitative data were processed using the Statistical Package for Social Sciences version 25 (SPSS v.25). The researcher used a pretest, five teacher-made worksheets, a posttest, and a delayed posttest as tools. Also, focus group discussion was considered to find out the common problems encountered by the students and the researcher himself. All the gathered data was recorded digitally and through field notes and discreetly handled. There are 10 enrolled students in Science, Technology, Engineering, and Mathematics (STEM) during the school year 2020--2021, and they were all chosen purposefully. This study involved the five-week topics in the second semester of the 3rd quarter. Results revealed an increase in the respondents' learning performance based on the mean percentage scores of the posttest. Based on the increase in the mean of the delayed posttest, the innovation promotes retention for the students. The researcher and the students had technical problems because of a power outage and unstable internet connections. Still, respondents face the challenges of output management and helping their parents first before learning. There is a significant difference in the respondents' performance before and after implementing the innovation. The heuristic approach to teaching improved the performance of the STEM students at San Felipe High School under the subject General Biology 2. It is recommended that teachers use the heuristic approach to teach students how to work in a digital world. [ABSTRACT FROM AUTHOR]

• A graph-based heuristic approach for multiple kernel learning (MKL). • Assigns sample-specific kernel weights based on contribution to graph modularity. • Classification performance comparable to state-of-the-art MKL algorithms. • Outperforms other heuristic approaches and has low time complexity. Multiple kernel learning (MKL) algorithms exploit information from multiple feature representations by assigning weights to each representation in the kernel space, and later combining them. However, this ignores the fact that data points exhibit locally varying characteristics. To address this problem, localized MKL algorithms learn locality-specific kernel weights which determine each base kernel's influence in the locality under consideration. Here, we relate the problem of determining the relevance of base kernels for classification to that of quantifying community structure in graphs. Next, we derive sample-specific kernel weights using graph modularity. Through experiments on publicly available datasets, we show that the proposed approach offers a viable alternative to state-of-the-art MKL approaches while being computationally inexpensive. [ABSTRACT FROM AUTHOR]

The increasing need for rapid data transmission in optical networks has made the effective allocation of spectrum resources a crucial necessity. This study presents a new spectrum allocation approach that incorporates path selection and careful consideration of cross-talk (XT) limitations. We propose a novel fragmentation coefficient and dynamic core-changing-based routing, spectrum, and core allocation (FC-DCC-RSCA) technique. The algorithm begins by searching for the shortest paths using the Dijkstra algorithm. It then proceeds to evaluate fragmentation coefficients (FC) using the Continuous Aligned Slot Ratio (CASR) model. Subsequently, the FC undergoes a filtration process wherein only those falling inside predetermined fragmentation ranges (min and maxFC) are retained. Further spectrum allocation has been done based on XT constraints. The algorithm has better proficiency in dynamic flexibility, enabling it to effectively handle varying traffic loads. By conducting a series of rigorous simulations, our proposed approach exhibits a notable decrease in bandwidth blocking probability (BBP) while also opening the door for considerable improvements in spectrum utilization. The algorithm's proficiency in adapting to changing network circumstances signifies its importance as a helpful thing in enhancing the allocation of resources within optical networks. It is well-positioned to address the developing requirements of contemporary communication systems. The spectrum utilization of the FC-DCC-RSCA increases by 12.5% and 6.25%, exhibits a reduction in its FR by 13.5% and 2.7%, and has a BBP of 0.051 for the traffic load of 450 Erlang, compared to SBP-PMC and FMDE-RSCA. The algorithm that has been provided provides opportunities for more investigation and enhancement. Potential areas for future research include the examination of strategies to improve energy efficiency, the development of scalable solutions for bigger network infrastructures, and the incorporation of machine learning methodologies to facilitate dynamic decision-making processes. Furthermore, it is worth investigating the potential of the method to be applied in developing optical communication technologies, such as space-division multiplexing. These endeavors hold the potential to enhance the current level of knowledge and practices in the field of optical network management and optimization. • Proposal of a novel fragmentation coefficient dynamic core changing scheme (FC-DCC-RSCA) in EON-SDM. • Combination of fragmentation-aware routing and dynamic core adaptation to improve network performance. • Flexibility of ranges of FC (maxFC & minFC) to handle the issue of fragmentation while ensuring the crosstalk threshold. • A proactive strategy for effective utilization of the spectrum. • Promoting the scalability and sustainability of future EON. [ABSTRACT FROM AUTHOR]

This study considers Two Echelon Location Routing Problem with Simultaneous Pickup and Delivery (2E/LRP-SPD). In a two-echelon distribution network consisting of factories, warehouses and customers, the aim is to determine which facilities will be opened in which candidate regions and routing activities to be carried out among them. Routing activities include distributing and collecting activities. While distributing activities are performed from primary facilities (factory) to secondary facilities (depots) and secondary facilities to customers, collecting activities are done from customers to the secondary facilities and from secondary facilities to primary facilities. We propose a two-index node based mixed integer programming formulation for the 2E-LRPSPD. As the problem is in NP-Hard problem class, a constructive heuristic algorithm based on Clarke-Wright algorithm is developed to solve medium- and large-size problems. The performance of the heuristic approach is investigated on test instances derived from literature. Computational results show that heuristic algorithm gives good quality solutions for medium- and large-size instances in a very short computation time. Thus, the contribution of this study to the literature is to present an efficient mathematical model for solving small-size problems and to develop a constructive heuristic algorithm that produces very fast and high-quality solutions for medium and large-size problems. [ABSTRACT FROM AUTHOR]

Summary: Vehicular ad hoc network (VANET) is a part of the intelligent transportation system (ITS) that provides safety and nonsafety applications. The high mobility of vehicles and the wireless communication environment in VANET makes it vulnerable to various attacks. One among them is the Sybil attack, where a Sybil attacker creates multiple fake identities called Sybil nodes that disrupt the functionality of VANET. Most of the existing solutions in the literature discuss identifying the Sybil nodes (virtual); very few works exist to determine the Sybil attacker (source node that generates Sybil nodes). In this paper, we propose a computation less heuristic approach that focuses on detecting the Sybil attacker and its Sybil nodes using signal strength measurements and Euclidean distance as the detection parameters. The central VANET server, Road Side Units (RSUs), and vehicles collaborate in the detection process, which improves the accuracy of our approach. The core of the approach is a reward‐based system, where the vehicle rewards are determined by collecting RSUs' feedback about the vehicle behavior. From simulation experiments, it is evident that our proposed approach achieves a maximum detection rate of 99.89% and a false positive rate of 0.012% than the existing techniques. [ABSTRACT FROM AUTHOR]

This paper presents a 0-1 Mixed Integer Programming Model and a heuristic approach based on restricted enumeration and machine grouping for solving k-stage Hybrid Flexible Flow Shop Problems with Sequence-Dependent Setup Times (HFFS_SDST) with Cmax. As presented in this paper in detail, recent reviews show that there is less research on flowshop with SDST than on regular flowshops, although Permutation Flow Shop (PFS) problems is extensively considered in the literature. Mathematical model presented in this work can solve the problems with five stages, eight machines, and six jobs in an acceptable time and this size is relatively bigger than of the ones previously have been solved by mathematical models for defined problems. The heuristic algorithm, based on sub-flow shops via forming machine groups in different clusters, is suggested to give good solutions for large-size problems and can to reach the optimal or near-optimal solutions for some instances and the computational time of the algorithm is shorter than solution time of the mathematical model as expected. In terms of proposed heuristic, to the best of our knowledge, this type of grouping algorithm to form flow shops in the machine environment is not introduced before and provides more efficient solutions by restricting the enumerations. There are no test problems for the problem under consideration. Therefore, real manufacturing environment instances are used. Computational results show that the proposed model and the heuristic algorithm are superior to the current scheduling approach in the company. The heuristic approach also provides a userfriendly environment and efficient scheduling. [ABSTRACT FROM AUTHOR]

Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population‐based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching–learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1°. [ABSTRACT FROM AUTHOR]

The economic and emission dispatch (EED) problem addresses to minimize the fuel cost as well as the emission from the thermal power plants referring the equality and inequality constraints. Thus, the multi-objective EED problem optimizes the contradicting objectives concurrently. The non-smooth and non-convex fuel cost function such as valve point loading (VPL) effect acts as additional impediment for EED problem. These limitations drive the EED problem to be a highly nonlinear and a multimodal optimization problem. In this article, a new heuristic approach, Coulomb's and Franklin's laws based optimization (CFLBO) algorithm is bestowed to solve the nonconvex economic and emission dispatch problem. The proposed EED considers the non-smooth and nonconvex cost characteristics to ape the VPL effects. The CFLBO approach is concocted from the Coulomb's and Franklin's theories, and comprises attraction /repulsion, probabilistic ionization and contact stages. Applying these CFLBO stages has inflicted in upgrading the robustness and search proficiency of the approach, and substantially lessening the number of generations required to accomplish the optimal solution. The fuel cost and the environmental emission functions are viewed as objective functions and developed as a bi-objective EED problem. The bi-objective EED problem is tackled after converting EED problem to a solitary objective function optimization issue by weighted sum approach with price penalty factors. A fuzzy based concessive approach is employed to choose the best compromised solution from the non-dominated solution sets. To demonstrate its competence, the proposed CFLBO algorithm is employed to 10 and 40-units test systems with nonconvex characteristic. The simulation results signify that the CFLBO algorithm affords the best concessive solution and outruns the other compared state-of --the-art approaches. [ABSTRACT FROM AUTHOR]

• The paper focuses on a pick-up routing problem with a three-dimensional (3D-PRP) loading constraint. • The paper combines genetic algorithm for pick-up routing with machine learning for three-dimensional loading problem. • K-means algorithm is developed for clustering phase. • The paper includes four expert systems. • Results of a real dataset are highly promising. This paper presents a combinatorial problem called a pick-up routing problem with a three-dimensional (3D-PRP) loading constraint, clustered backhauls at the operational level, and train loading at the tactical level for an intermodal transportation network. A two-phase approach, called clustering first, packing-routing second, is proposed for use during the first stage. The clustering of backhauls is carried out using the k-means algorithm. A hybrid approach is provided, which combines the packing of orders by first solving a 3D loading problem for each cluster using machine learning with a best-fit-first strategy, with routing using a genetic algorithm. During the second stage, the train-loading problem is solved using a mixed integer programming approach to minimise the total costs by incorporating various cost types, in which detention and demurrage costs are taken into account. All solution approaches are computationally evaluated on real-world data provided by an international logistics firm and new randomly generated instances. Comparisons are carried out using both exact solution methods and heuristic approaches, and the proposed approach was shown to be more effective for real-world problems. [ABSTRACT FROM AUTHOR]

The design and feasibility of electronically controlled Marine Cycloidal Propeller (MCP) by implementing heuristic-based approach is demonstrated. Adding closed-loop control enhances performance. This is shown by simulation. The chances of improving efficiency are more in electronically controlled MCP, as pitch angle of each blade can be independently controlled. Multiple configurations of MCP can be anchored within the same model without changing hardware. Simulations are used to finalize the technical specifications of the system. Experimental results demonstrate the acceptable trajectory generation (using online feedback) and synchronizations of all instruments. [ABSTRACT FROM AUTHOR]

ICAR-Directorate of Cashew Research is the nodal agency for conducting cashew research and manages the largest field gene bank in India. Cashew is a perennial tree and needs more land and other resources to maintain accessions. Conservation through seeds is not feasible because of cross-pollination. Tissue culture efforts to regenerate plants from mature explants are not successful. Therefore, efficient management of the filed gene bank particularly utilization requires designation of the core collection representing the spectrum of diversity present in the entire collection. In this study, a relatively new technique, the advanced M strategy with heuristic approach was deployed to develop the core collection. Sixty-eight morphometric characters of 478 accessions were subjected to analysis resulting in the core collection of 49 accessions. Further, another core collection of same number was constituted by K -Means clustering to compare the efficiency of two approaches. The validation parameters like mean difference, variance difference, coincidence rate, variable rate and class coverage among others were employed for comparative analysis. The results of these parameters revealed that the core collection designated by heuristic approach was better able to efficiently represent and retain the diversity of the entire collection compared with the core identified by clustering approach. Future conservation and breeding efforts will be focused on establishing a separate block in the field gene bank having 49 accessions of cashew core collection. [ABSTRACT FROM AUTHOR]

Well-known solution methods of classical transportation problems have become insufficient for today's dynamic transportation problems. Mixed constrained transportation problems take an important place among frequently encountered optimization problems in real business-life. By its nature, the mathematical formulation of these problems is very similar to the classical one, except additional inequality constraints that make capacity planning more flexible for supply and demand centers. Many new solution methods are proposed in different studies because of their huge importance for the firms' long-term success. Unfortunately, still there is no consensus on the solution method in the literature. For that reason, this study is focused on a recently proposed solution method by employing two examples. By using this solution method, decision makers will not make a great effort to obtain a solution, because it does not require complex mathematical calculations. These characteristics may make the method preferable in real-life problems encountered in firms. [ABSTRACT FROM AUTHOR]

Efficient algorithms for solving the routing and spectrum assignment (RSA) problem in flex‐grid optical networks are of paramount importance to leverage network efficiency. Exact approaches based on integer linear programming (ILP) models are known to be computationally hard and hence, difficult to solve for realistic models while heuristics have inherent downsize of local optimal trapping. In addressing those issues, this paper firstly presents a novel and efficient heuristic based on the genetic algorithm (GA) to provide (near‐) optimal solutions to the RSA problem. The effectiveness of the GA‐based algorithm is benchmarked against typically used reference heuristics for a set of network scenarios and it is shown that in addition to the better performance than conventional heuristics in the literature, the proposed GA technique indeed could achieve optimal solutions in most cases while the running time is still manageable. Secondly, we propose to make use of efficient solutions from the heuristic approaches to set the upper‐bound and warm‐start point for ILP formulation. This combination has been shown very productive in enhancing running time performance and increasing the capability of achieving an optimal solution and/or improving the solution quality in a given time span compared with traditional ways for solving ILP models. [ABSTRACT FROM AUTHOR]

Present-day power systems are mostly equipped with conventional meters and intended for the installation of highly accurate phasor measurement units (PMUs) to ensure better protection, monitoring and control of the network. PMU is a deliberate choice due to its unique capacity in providing accurate phasor readings of bus voltages and currents. However, due to the high expense and a requirement for communication facilities, the installation of a limited number of PMUs in a network is common practice. This paper presents an optimal approach to selecting the locations of PMUs to be installed with the objective of ensuring maximum accuracy of the state estimation (SE). The optimization technique ensures that the critical locations of the system will be covered by PMU meters which lower the negative impact of bad data on state-estimation performance. One of the well-known intelligent optimization techniques, the genetic algorithm (GA), is used to search for the optimal set of PMUs. The proposed technique is compared with a heuristic approach of PMU placement. The weighted least square (WLS), with a modified Jacobian to deal with the phasor quantities, is used to compute the estimation accuracy. IEEE 30-bus and 118-bus systems are used to demonstrate the suggested technique. [ABSTRACT FROM AUTHOR]

Purpose-The purpose of this paper is to find a representative subset from large-scale online reviews for consumers. The subset is significantly small in size, but covers the majority amount of information in the original reviews and contains little redundant information. Design/methodology/approach-A heuristic approach named RewSel is proposed to successively select representatives until the number of representatives meets the requirement. To reveal the advantages of the approach, extensive data experiments and a user study are conducted on real data. Findings-The proposed approach has the advantage over the benchmarks in terms of coverage and redundancy. People show preference to the representative subsets provided by RewSel. The proposed approach also has good scalability and is more adaptive to big data applications. Research limitations/implications-The paper contributes to the literature of review selection, by proposing a heuristic approach which achieves both high coverage and low redundancy. This study can be applied as the basis for conducting further analysis of large-scale online reviews. Practical implications-The proposed approach offers a novel way to select a representative subset of online reviews to facilitate consumer decision making. It can also enhance the existing information retrieval system to provide representative information to users rather than a large amount of results. Originality/value-The proposed approach finds the representative subset by adopting the concept of relative entropy and sentiment analysis methods. Compared with state-of-the-art approaches, it offers a more effective and efficient way for users to handle a large amount of online information. [ABSTRACT FROM AUTHOR]

Web literacy refers to the skills and competencies people need in order to function in societies connected through the Internet. Many of the frameworks for understanding the components of web literacy are limited in value because they rely on conceptual definitions. They do not take into consideration the social practices governing the use and writing on the web. Nor do these frameworks take into account the open and participative nature of the Internet. With the aim of moving beyond this theoricist vision, we present an analysis of the relationship between the social practices of a group of university students in open learning environments and the acquisition of web skills. We proposed an alternative approach that is rooted in an understanding of social practices. In order to "operationalize" and facilitate an study of web skills, we relied on a specific type of analysis that allowed us to observe the consistency between the practices observed and the behavior reflected in the heuristic framework of web skills. The main elements of this alternative framework are explained, as is the link between the social practices of the students and the skills acquired. We also discuss other contributions to the field of Web Literacy and to the even larger field of Digital Literacy. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy multi-objective based heuristic algorithm for network reconfiguration of distribution systems considering distributed generations (DGs). The objectives of reduction of real power loss, branch current carrying capacity limit, maximum and minimum voltage constraints, and feeder load balancing are considered for performance enhancement of the distribution system. Since these objectives are non-commensurable and difficult to solve simultaneously using conventional approaches, they are converted into fuzzy domain and a fuzzy multi-objective function is formulated. A sensitivity analysis based on voltage profile improvement and real power loss reduction is used for obtaining optimal locations of DGs and genetic algorithm is used for optimal sizing of DGs. The proposed reconfiguration algorithm is implemented in two stages, initially in the first stage without incorporating DGs and in the second stage incorporating DGs for obtaining an optimal distribution system network reconfiguration. The advantage of the proposed method is demonstrated through a seventy node four feeders and a sixteen node three feeders distribution systems. [ABSTRACT FROM AUTHOR]

The type-2 U-shaped assembly line balancing problem is important for many just-in-time manufactures, but an efficient algorithm is not available at present. Thus, in this study, a novel heuristic approach based on multiple rules and an integer programming model is proposed to address this problem. In the proposed approach, three rules are systematically grouped together, i.e., task selection, task assignment, and task exchange rules. The sufficient conditions for implementing the exchange rules are proposed and proved. Thirteen small or medium scale benchmark issues comprising 63 instances were solved, where the computational results demonstrate the efficiency and effectiveness of the proposed method compared with integer programming. The computational results obtained for 18 examples comprising 121 instances demonstrate that the task exchange rules significantly improve the computational accuracy compared with the traditional heuristic. Finally, 30 new standard instances produced by a systematic data generation process were also solved effectively by the proposed approach. The proposed heuristic approach with multiple rules can provide a theoretical basis for other local search algorithms, especially for addressing issues such as the U-Shaped assembly line balancing problem. [ABSTRACT FROM AUTHOR]

Physical Protection System (PPS) is essential for each nuclear power plant to safeguard its nuclear materials and nuclear facilities from theft, robbery, illegal transport and sabotage. This paper presents a novel method (HAPPS) combined with Estimate of Adversary Sequence Interruption (EASI) method and heuristic approach (Ant Colony Optimization, ACO) for analyzing and evaluating the PPS effectiveness of NPPs. Import 2-D engineering drawings into the analysis application, identify the information contained in the model, and use the HAPPS method as search algorithm to seek the vulnerable adversary intrusion and escape path under certain conditions. The results of PPS effectiveness analysis will provide a detailed technical feedback for redesigning PPS. [ABSTRACT FROM AUTHOR]

The developments of wearable devices such as Body Sensor Networks (BSNs) have greatly improved the capability of tele-health industry. Large amount of data will be collected from every local BSN in real-time. These data is processed by embedded systems including smart phones and tablets. After that, the data will be transferred to distributed storage systems for further processing. Traditional on-chip SRAMs cause critical power leakage issues and occupy relatively large chip areas. Therefore, hybrid memories, which combine volatile memories with non-volatile memories, are widely adopted in reducing the latency and energy cost on multi-core systems. However, most of the current works are about static data allocation for hybrid memories. Those mechanisms cannot achieve better data placement in real-time. Hence, we propose online data allocation for hybrid memories on embedded tele-health systems. In this paper, we present dynamic programming and heuristic approaches. Considering the difference between profiled data access and actual data access, the proposed algorithms use a feedback mechanism to improve the accuracy of data allocation during runtime. Experimental results demonstrate that, compared to greedy approaches, the proposed algorithms achieve 20%–40% performance improvement based on different benchmarks. [ABSTRACT FROM AUTHOR]

Healthcare service is a critical aspect of our daily lives. Enabled by technologies such as wearable devices and wireless sensor networks, tele-health has becoming a promising new field in IT industry. Wearable devices, which detect real-time human body conditions, form body sensor networks (BSNs) for patients. In a cloud-enabled tele-health ecosystem, health data are collected by the BSN and sent to mobile devices such as smart phones and tablets. These embedded devices process the data and forward them to remote data centers. Due to the energy and time constraints of embedded systems, the effectiveness of storage systems become a critical issue. For years, memory technologies such as SRAMs and DRAMs have been widely used in computer systems. SRAMs are fast while DRAMs have high density. However, SRAMs have the disadvantage of power leakage and low density. DRAMs are slower in read and write operations. New memory technology for embedded tele-health is needed. In the paper, we propose a hybrid memory system for embedded tele-health. We combine phase-change memory PCM with flash memory to meet energy and latency requirement while reducing capital expenditure. Moreover, the data allocation and storage on server side is also a challenging problem in tele-health. Effective storage system designs are desired to efficiently store and manage health care data from users. Therefore, in the paper, we design a ecosystem for tele-health including the memory storage for embedded devices and data storage for tele-health data centers. To fully utilize the proposed ecosystem, we design several resource allocation algorithms with dynamic programming and heuristics. The experiments show that our approaches can achieve up to 30% performance enhancement compared to greedy approaches. [ABSTRACT FROM AUTHOR]

In a deregulated energy market, voltage security (VS) is one of the major concerns, as it improves the loadability limits, loading profiles while concurrently reducing power loss. Further, voltage dependent loads can operate at high efficiency at rated voltage. Therefore, power utilities have to explore options of reconfiguration to secure the voltage level under frequently changing operating conditions. In this study, an efficient heuristic and meta‐heuristic (harmony search) approach for reconfiguration is proposed by imposing VS. Two indices namely node voltage security index (NVSI) and node voltage quality index are developed for aggregated improvement in node voltages. Moreover, efficient methodologies for identification of new tie‐lines, network radiality and the load flow solution are also presented. The effectiveness of the proposed index and the methodology is tested for reconfiguration of various distribution systems and the results are obtained with reduced computational efforts. [ABSTRACT FROM AUTHOR]

• An integrated-control strategy is proposed to improve building energy performance. • It has an integrated neural network, a decision-making method, and adaptive training. • Indoor environmental quality is predicted using the integrated neural network. • This strategy can reconcile decision conflicts in multi-objective HVAC operations. • Adaptive training can improve control performance in response to disturbances. In South Korea, school buildings require significant energy inputs for heating and air-conditioning, and the majority of the occupants are adolescent students, whose health and cognitive performance are vulnerable to poor indoor air quality (IAQ) and thermal discomfort. Using field measurements, some previous studies have reported that some Korean schools have poor IAQ and thermal conditions. Thus, it is necessary to develop effective heating, ventilation, and air-conditioning (HVAC) control strategies to improve the indoor environment and reduce energy consumption. Therefore, this study proposes an intelligent HVAC integrated control strategy that can improve indoor environmental quality (IEQ) and reduce energy consumption in school buildings. The proposed strategy utilizes an integrated neural network prediction model for IEQ and a heuristic method that can optimize control objectives (i.e., the predicted mean vote [PMV], carbon dioxide [CO 2 ], particulate matter with diameters of 10 and 2.5 μ m [PM 10 and PM 2.5 , respectively], and HVAC energy consumption). To evaluate the control performance of the proposed strategy, the present study employs two base algorithms (i.e., a rule-based and a non-adaptive control approach) under non-disturbance and forcing disturbance scenarios. The control failure period for PMV is found to be 1.6420% and 9.4773% of the total occupancy period under the non-disturbance and forcing disturbance scenarios, respectively, while CO 2 control failure does not occur under either scenario. The control failure periods for PM 10 and PM 2.5 were 5.1676%, and 7.1844%, respectively, under forcing disturbance. Under the non-disturbance scenario, the proposed strategy consumed 2,467.07 kWh and 870,26 kWh for heating and cooling, respectively, representing 91.1% and 84.08% of that for the rule-based algorithm. The proposed strategy can thus effectively improve the IEQ of a building and has the potential for use in the development of integrated environmental management solutions for buildings. [ABSTRACT FROM AUTHOR]

This paper deals with a vegetable production-planning problem in which a farmer has to simultaneously determine the crop rotations and harvest schedules for a number of vegetables in his farmlands. The crop rotation decisions include the determination of crop sequence, cropping times, and cropping size in each farmland. The harvest decisions include the collection times and harvesting size for each planted crop. By taking into account the growing season and technical-ecological constraints, this paper formulates a mixed integer linear programming model to develop vegetable production decisions for organic farmers. Due to the complexity of the model, we proposed a two-phase heuristic approach to solve the model. We applied the proposed model to analyze an organic farm in Taiwan. Computational results show that the proposed heuristic is superior to CPLEX in terms of solution quality and computational times for large scale problems. In addition, some sensitivity analyses were also conducted to evaluate the impact of parameters in the model. [ABSTRACT FROM AUTHOR]

Background: Despite several previous attempts at subtyping schizophrenia, a typology that reflects neurobiological knowledge and reliably predicts course and outcome is lacking. We applied the system-specific concept of the Bern Psychopathology Scale (BPS) to generate a course typology based on three domains: language, affectivity, and motor behaviour. Sampling and Methods: A cohort of 100 patients with schizophrenia or schizoaffective disorders according to DSM-IV criteria underwent psychopathological assessment, and all their available medical records were retrospectively analysed on the basis of the BPS. Results: Overall, 39% of the patients showed dominant abnormalities in only one domain, 37% in two domains, and 24% in all three domains. The motor domain was affected in the majority of patients (76%), followed by affectivity (63%) and language (46%). Eighty-six percent of patients showed a bipolar course pattern in at least one domain. Conclusions: In a retrospective analysis of 100 patient records we described system-specific course patterns of schizophrenia by using a neurobiologically informed psychopathological assessment. The results showed a surprisingly high proportion of bipolar courses and a pattern of pure and mixed subtypes, which speaks for an overlap of domains with regards to psychopathological symptoms. A limitation of this heuristic and retrospective approach is that it was largely based on clinical judgement. Prospective studies with more rigorous threshold definitions are needed to clarify the neurobiological and clinical implications of the proposed reorganization of psychotic disorders. [ABSTRACT FROM AUTHOR]

The paper presents a method for estimating and mapping – at detailed scale (1:5000) – the thickness of pyroclastic cover beds resting on calcareous bedrock. This method, tested in the study area of Mount Albino (Campania region, southern Italy), makes use mainly of information gathered fromin situinvestigations, managed and processed in a geographical information system environment via a geostatistical interpolation technique (i.e. ordinary kriging) and finally integrated and amended by adopting a heuristic approach. Given its easy applicability and affordable costs, the proposed method can be used in similar geological contexts where knowledge of the spatial distribution of pyroclastic cover beds is a requirement for understanding and predicting slope instability processes. [ABSTRACT FROM AUTHOR]

In this paper, the problem of microgrid optimisation with storage has been addressed in an unaccounted way rather than confining it to loss minimisation. Unitised regenerative fuel cell (URFC) systems have been studied and employed in microgrids to store energy and feed it back into the system when required. A value function-dependent on line losses, URFC system operational cost and stored energy at the end of the day are defined here. The function is highly complex, nonlinear and multi dimensional in nature. Therefore, heuristic optimisation techniques in combination with load flow analysis are used here to resolve the network and time domain complexity related with the problem. Particle swarm optimisation with the forward/backward sweep algorithm ensures optimal operation of microgrid thereby minimising the operational cost of the microgrid. Results are shown and are found to be consistently improving with evolution of the solution strategy. [ABSTRACT FROM AUTHOR]

We concentrate on characteristics of minima XNfrom samples of iid lognormals X1i∼ 2pLND. We demonstrate that the distribution of XNfor 2 ≤ N ≤ 1,000 may be fitted more accurately by 2pLND than by the limiting Gumbel distribution. An extended power model is established to represent the quotients CN/C1, where CNis the mean, standard deviation, or p-quantile of XNand C1is the corresponding characteristic of X1i. Our empirical comparisons show that this model provides not only more accurate estimates than alternating approximations but it is also much simpler than its competitors. [ABSTRACT FROM AUTHOR]

In this paper, strategic energy management in a micro grid is proposed incorporating two types of storage elements viz. unitised regenerative fuel cell (URFC) and electric vehicle (EV). Rather than a simple approach of optimizing micro grid operation to minimize line loss in the micro grid, this paper deals with multi objective optimization to minimize line loss, operational cost and maximize the value of stored energy of URFC and EV simultaneously. Apart from URFC, two operation strategies are proposed for EV enabling V2G operation to reduce overall system cost of operation. To address the complexity, non-linearity and multi dimensionality of the objective function, particle swarm optimization-a heuristic approach based solution methodology along with forward and back sweep algorithm based load flow solution technique is developed. Combined with particle swarm optimization (PSO), forward and backward sweep algorithm resolves the complexity and multi dimensionality of the load flow analysis and optimizes the operational cost of micro grid. The simulation results are presented and discussed which are promising with regard to reduction in line loss as well as cost of operation. Scheduling strategy of the micro grid with both URFC and EV enabling V2G operation presents a promising approach to minimize line loss and cost of operation. [ABSTRACT FROM AUTHOR]

Early vigour of rice, defined as seedling capacity to accumulate shoot dry weight (SDW) rapidly, is a complex trait. It depends on a genotype propensity to assimilate, store, and/or use non-structural carbohydrates (NSC) for producing large and/or numerous leaves, involving physiological trade-offs in the expression of component traits and, possibly, physiological and genetic linkages. This study explores a plant-model-assisted phenotyping approach to dissect the genetic architecture of rice early vigour, applying the Genome Wide Association Study (GWAS) to morphological and NSC measurements, as well as fitted parameters for the functional–structural plant model, Ecomeristem. Leaf size, number, SDW, and source-leaf NSC concentration were measured on a panel of 123 japonica accessions. The data were used to estimate Ecomeristem genotypic parameters driving organ appearance rate, size, and carbon dynamics. GWAS was performed based on 12 221 single-nucleotide polymorphisms (SNP). Twenty-three associations were detected at P <1 × 10–4 and 64 at P <5 × 10–4. Associations for NSC and model parameters revealed new regions related to early vigour that had greater significance than morphological traits, providing additional information on the genetic control of early vigour. Plant model parameters were used to characterize physiological and genetic trade-offs among component traits. Twelve associations were related to loci for cloned genes, with nine related to organogenesis, plant height, cell size or cell number. The potential use of these associations as markers for breeding is discussed. [ABSTRACT FROM AUTHOR]

Recently a design-driven heuristic approach named guided stochastic search (GSS) technique has been developed by the authors as a computationally efficient method for discrete sizing optimization of steel trusses. In this study, an extension and reformulation of the GSS technique are proposed for its application to problems from discrete sizing optimization of steel frames. In the GSS, the well-known principle of virtual work as well as the information attained in the structural analysis and design stages are used together to guide the optimization process. A design wise strategy is employed in the technique where resizing of members is performed with respect to their role in satisfying strength and displacement constraints. The performance of the GSS is investigated through optimum design of four steel frame structures according to AISC-LRFD specifications. The numerical results obtained demonstrate that the GSS can be employed as a computationally efficient design optimization tool for practical sizing optimization of steel frames. [ABSTRACT FROM AUTHOR]

Identification of a small core germplasm set representing the available genetic diversity is essential for its proper evaluation and subsequent utilization in rice improvement programmes. For constituting a small diverse mini-core panel of Indian rice germplasm, a representative set of 6912 accessions drawn based on their geographic origin from the whole rice germplasm collection available in the National Gene Bank was genotyped using 36 microsatellite markers. Automated fragment analysis of amplicons yielded a total of 435 alleles, with an average 12.4 and range of 3-29 alleles per locus. Polymorphism information content ( PIC) ranged from 0.08 ( RGNMS190) to 0.86 ( RM552) with an average of 0.528. Based on genotyping data, a mini-core consisting of 98 genotypes was identified. Ninety-four per cent of the alleles present in the core set were present in the mini-core. The identified small but diverse panel will be useful for further intensive trait-specific evaluation and utilization in allele mining. [ABSTRACT FROM AUTHOR]

• Human impacts contribute to biodiversity decline globally. • Global biodiversity modelling approaches focus on direct anthropogenic pressures. • Consideration of terrestrial and aquatic ecosystems as separate parts can be a challenge. • Global biodiversity modelling should consider interconnections of human interventions. • Models often lack recommendations on conservation strategies. Human impacts contribute to the reduction of biodiversity. To describe biodiversity loss and identify its causes, biologists (in particular ecologists) use a wide range of models. Globalised human interventions cause most biodiversity losses. Therefore, global biodiversity is taken as the level of observation. This also allows for the appreciation of the world as an environmental entity. Because humans play a dual role in the context of biodiversity loss, their "parameterisation" in such models is particularly interesting. On the one hand human influences can be categorised as disruptive factors because they transcend the biological needs of the individual human. On the other hand humans are themselves part of the biosphere, so their impact on the environment and biodiversity must also be qualified as a natural phenomenon. What at first glance appears contradictory can very well be embraced by models and modelling approaches. Consequently, we review ten different (global) biodiversity models on how they grasp human influences that consist of stressors (pressures) and also drivers (societal needs). This work's main expectation is that the reviewed models make certain assumptions, include some aspects and exclude others. In our review, we regard them as heuristic models. Thus, it is not the detailed algorithms we are interested in, but how human activities are included and depicted within the models. The model review also deploys a human ecology perspective, which includes an evolutionary and societal angle and consciously investigates (1) human adaptions to the environment and (2) human alterations of the environment. While our review clearly shows the widely shared understanding for the need of evidence-based countermeasures to act responsibly, very few models offer concrete recommendations but instead mostly limit their advice to the technical level of modelling. We conclude that policy advice, science communication, and concrete measures need to be improved in biodiversity models. [ABSTRACT FROM AUTHOR]

Pipelining a combinational logic data‐path without introducing pipeline stage delay imbalance is a key requirement for efficient digital processing. Balanced pipelining is easily achieved by simple logical effort optimisation when the interconnect and fan‐out delay overhead is small and homogeneously distributed. When the target micro‐architecture design includes isolated long interconnects, such as buses, optimal pipeline stage balancing becomes a tricky problem. This work formalises the solution based on the logical effort paradigm and evidences its advantage with respect to a heuristic approach. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new method called guided stochastic search (GSS) is proposed for sizing optimization. [•] GSS is a design-driven approach based on the principle of virtual work. [•] GSS is computationally efficient in discrete sizing optimization of steel trusses. [ABSTRACT FROM AUTHOR]

Distribution system planning is undergoing a change in paradigm in the context of deregulation and with penetration of distributed generation (DG) sources into distribution networks. This study presents a new heuristic approach based on a back‐propagation algorithm combined with cost–benefit analysis to comprehensive multi‐year distribution system planning. The planning problem incorporates various energy supply options for local distribution companies such as DG sources, substations and feeders and determines the optimum capacities, locations and upgrade plans. Test results comprising a 32 bus system and the IEEE 69 bus system are presented to illustrate the performance of the proposed method. The results are compared with an optimisation solution, and it is demonstrated that the heuristic approach can achieve better performance and has higher computational efficiency. [ABSTRACT FROM AUTHOR]

The voltage and frequency of three‐phase self‐excited induction generator (SEIG) depend upon various factors such as prime mover speed, capacitance value, load etc. The poor voltage and frequency regulation are the main constraints for poor performance of SEIG. The improved performance can be achieved through short shunt SEIG configuration. In this study, the utilisation of SEIG is transformed into a number of novel functions. The combination of speed, series and shunt capacitances as design parameters are selected for short shunt SEIG in order to provide optimum values for voltage regulation (VR), frequency regulation (FR), loadability, voltage/frequency regulation and to provide optimum performance, defined as a weighted combination of VR, FR and loadability. The effect of weights, speed and power factor on designed parameters is also assessed for a generic application on SEIG. Correspondingly, an optimisation is proposed using particle swarm optimisation, a heuristic approach considering various equality and inequality constraints related to SEIG. This study is carried out for resistive and inductive loads. The simulated results are validated with test results. [ABSTRACT FROM AUTHOR]

One of the important design elements for a good production system is material handling. In cases where it is not well-designed, it can be the bottleneck in the system. Moreover, it can cause a lot of wastes such as waiting time, idle time, and excessive transportation and cost. In this study, material handling in lean-based production environments is taken into account. Depending on the lean structure of the production systems such as being pull-based, smooth, and repetitive, delivering the materials to the stations periodically becomes important. At this point, milk-run trains are highly used in real applications since they enable the handling of required amount of materials on a planned basis. With this study, it is aimed to develop a specific model for milk-run trains which travel periodically in the production environment on a predefined route in equal cycle times with the aim of minimizing work-in-process and transportation costs. Since the milk-run trains having equal cycle times start their tours at the same time intervals, it becomes simple to manage them. For this reason, they are used in lean production systems where level scheduling is performed. The developed model is based on mixed-integer linear programming, and since it is difficult to find the optimum solution due to the combinatorial structure of the problem, a novel heuristic approach is developed. A numerical example is provided so as to show the applicability of the mathematical model and the heuristic approach. [ABSTRACT FROM AUTHOR]