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Regular Ship hull maintenance is an essential for sustainability. The maintenance work of ship hulls that involve human labor suffers from many shortcomings. Maintenance robots have been introduced for drydocks to eliminate these shortcomings. An energy-efficient Complete Coverage Path Planning (CCPP) is a crucial requirement from a ship hull maintenance robot. This paper proposes a novel energy-efficient CCPP method based on Glasius Bioinspired Neural Network (GBNN) for a ship hull inspection robot. The proposed method accounts for a comprehensive energy model for path planning. This energy model reflects the energy usage of a ship hull maintenance robot due to changes in direction, distance, and vertical position. Furthermore, the proposed method is effective for dynamic workspaces since it performs online path planning. These are the major contributions made to state of the art by the work proposed in this paper. The behavior and the performance of the proposed method have been compared against state of the art through simulations considering Hornbill, a multipurpose ship hull maintenance robot. The validation confirms the ability of the proposed in realizing a complete coverage of a given dynamic workspace. According to the statistical outcomes of the comparison, the performance of the proposed method significantly surpasses that of the state-of-the-art methods in terms of energy usage. Therefore, the proposed method contributes to the development of energy-efficient CCPP methods for a ship hull maintenance robot. • A novel coverage method based on Glasius Bioinspired Neural Network is proposed. • The proposed method is intended for a multipurpose ship hull maintenance robot. • A comprehensive energy model is utilized by the proposed method for path planning. • The proposed method is significantly efficient than the state of the art methods. • The paper contributes to the development of a ship hull maintenance robot. [ABSTRACT FROM AUTHOR]

To meet the growing need for customized products, flexible production systems are gaining widespread application in modern factories. The inherent complexity of these flexible systems poses new challenges for production management. In this paper, we intend to contribute an operation-level analytical model for such systems, thus providing an effective evaluation tool for productivity and energy consumption analysis. Specifically, a multi-type serial production line with machines obeying the geometric reliability model and intermediate buffers having limited capacity is considered, in which K batches of parts are processed sequentially in each shift. Analytical solutions for key operation metrics are first developed for small systems using a Markov model. For multi-machine lines, a semi-analytical method based on aggregation is proposed. The effectiveness of the proposed analysis method is verified through simulation experiments. Besides, system properties are analyzed to provide insights for continuous improvement and optimization. • Flexible production line with unreliable machine and limited buffer. • Generalized analytical model for both productivity and energy consumption. • New semi-analytical approach to speed up metrics calculation. [ABSTRACT FROM AUTHOR]

Energy efficiency is a complicated process in designing Wireless Sensor Networks (WSNs). Effective routing is a precise solution for the energy efficiency constraint in WSN owing to the higher sharing of energy consumption during communication. Thus, routing is an alternative way out to this issue along with hierarchy-based clustering techniques. The clustering in WSN is carried out by picking the Cluster Head (CH), which is assumed as the essential or leader node. They perform more tasks, and thus, more energy is consumed. The clustering classifies the nodes into groups to avoid the huge communication range that is deputized to the CH. Hence, this paper establishes a routing algorithm for saving energy using a new optimization strategy via solving the multi-function formulation of the developed protocol in WSN. Here, the cluster head selection is performed with the Adaptive Remora Optimization Algorithm (AROA) to derive the multi-function derivation. The multi-function formulation undergoes with known factors such as energy consumption, distance, throughput, Packet Delivery Ratio (PDR), and path loss. The experimental result indicates an enhanced network lifetime and saving the energy of the developed heuristic-based routing protocol in WSN. [ABSTRACT FROM AUTHOR]

This paper aims to present an autonomous intelligent system that improves the performance of conventional manufacturing processes to make them friendly to the environment. This improvement is made considering objectives that involve a reduction of supplies, use of tools, and low energy consumption; but maintaining desirable characteristics of machined parts in quality, mechanical properties, and dimensions. We present an architecture that integrates modeling with neural networks, validation using metrical statistics, and multi-objective optimization based on MOEA/D which takes experimental or historical data of the process and generates a set of parameters to satisfy all three criteria of sustainability, efficiency, and quality. Two cases for welding and machining process are presented to illustrate the use of the proposed approach which results in evidence of the feasibility of having green and intelligent manufacturing systems. In the welding process, the user can select a low amperage but increase lightly the cycles of operation to reduce energy consumption but satisfy the quality objective. In machining, the user can select a solution to avoid the use of cooling maintaining quality and efficiency in acceptable levels of operation. • Green and Intelligent Manufacturing Systems (GIMS) offer eco-efficient processes. • We propose an approach to convert conventional manufacturing processes to be GIMS. • Our approach allows a trade-off on economic, quality, and sustainability factors. • Efficiency is related to economic criteria, at once with green factors. • GIMS promotes new research on modeling and multi-objective optimization. [ABSTRACT FROM AUTHOR]

Robot comprehensive performance optimization is a high dimensional non-convex problem, which is of great significance in the practice of robotic intelligent manufacturing. Existing methods still face many challenges in performance evaluation, local optima, and planning efficiency. In this paper, a novel method based on accelerated gradient descent and directed-mutagenesis genetic algorithm (AGD-DGA) is proposed for efficient robotic motion planning and comprehensive performance optimization. Firstly, the time, energy consumption, and impact (T-E-I) are taken as the optimization objectives. In particular, the power loss sub-model is constructed, which is rarely considered in the previous research on motion optimization, and it is combined with the driving power sub-model to improve the evaluation accuracy. Next, a novel directed-mutagenesis genetic algorithm (DGA) is proposed to address the local optima problem. Compared with existing methods based on random sampling, the DGA further improves the success rate and efficiency by updating the probability model based on the adaptive Gaussian process. Moreover, a hierarchical weight updating mechanism is established to overcome the impact of additional performance indicators on the success rate of collision-free path planning. To improve planning efficiency, an incremental optimization mechanism and an adaptive fine-tuning strategy for population size are proposed to reduce unnecessary sampling, evaluation, and sorting. The simulation and experimental results in different scenarios demonstrate that the proposed AGD-DGA achieves better results than most of the mainstream algorithms in terms of optimization efficiency, success rate, and motion performance. In addition, it is also verified that the modified energy consumption model can achieve an average evaluation accuracy of more than 90% while maintaining the calculation efficiency. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) plays a vital role in achieving energy revolution and the goals of carbon peak and carbon neutrality. The optimal planning of IES is of great significance for improving the overall efficiency of the system and promoting its sustainable development. Focusing on this issue, this paper proposes a planning framework integrating multi-objective optimization with fuzzy multi-criteria decision making (MCDM). In this framework, IES planning is modeled as a multi-objective optimization problem that, for the first time, simultaneously minimizes energy consumption, carbon emissions, and economic costs. Thereafter, the optimization problem is solved by a multi-objective state transition algorithm based on decomposition (MOSTA/D), which generates a Pareto set that realizes multiple conflicting objective tradeoffs. Furthermore, to comprehensively evaluate the Pareto optimal solutions, an evaluation criteria system is established from various perspectives, and a novel MCDM approach is proposed. This approach combines the analytic network process-entropy weighting technique, which takes into account the correlation between criteria as well as subjective preference and objective information, with fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) for scientifically ranking and selecting solutions under uncertainty. The simulation results of an IES planning case study demonstrate that the optimal scheme determined by the proposed method achieves the best overall benefit for IES, with significant annual economic cost savings, primary energy savings, and carbon dioxide emission reduction rates of 2.27%, 40.36%, and 56.25%, respectively, proving the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Now-a-days, due to the increasing demand of the energy resources across the world, choosing the best sustainable biomass crop for the production of biofuels is a strategic decision-making problem. This is generally accepted that intuitionistic fuzzy sets (IFSs) are much more efficient in comparison with fuzzy sets at representing and processing the uncertainty in real-life problems. Proceeding on the same line, this paper attempts to introduce an extended combined compromise solution (CoCoSo) framework to analyze the sustainable biomass crop selection (SBCS) problem in an intuitionistic fuzzy environment. In this framework, we suggest a new integration function based on double normalization multiple aggregation approach to overcome the aggregation biases of the original CoCoSo approach and discuss its advantages with some numerical examples. We also develop a combined weighting strategy based on distance measure and decision experts' (DEs) opinions to evaluate the significance of criteria. For this, we propose a novel distance measure (DM) and establish its superiority through some numerical comparisons. Also, the rationality of the suggested measure over the extant measures is justified by the use of an algorithm based on the developed measure for pattern recognition issues. In this framework, the comparison issue of IFSs is resolved by proposing a new score function. Furthermore, a case study of the SBCS is presented for the implementation of the developed CoCoSo approach, which confirms the viability and effectiveness of the new methodology. The results of the sensitivity analysis demonstrate that option "Miscanthus" consistently achieves the highest rank and is independent of variations of trade-off parameter and balancing factor. Finally, a comprehensive comparison is carried out to ensure the steadiness and reliability of the introduced framework. • We propose a new distance measure and establish its validation. • We develop and visualize an improved score function for IFS with its basic properties. • A new integration function is used to overcome the biases of the original CoCoSo method. • We introduce an improved IFS-CoCoSo framework and apply it to multi-criteria decision-making. [ABSTRACT FROM AUTHOR]

This research develops a hybrid approach that integrates light gradient boosting machine (LightGBM) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the tunnel boring machine (TBM) performance during excavation. The TBM operational data are first extracted and meta -models are established to estimate the key TBM performance, including the penetration rate, over/under excavation ratio, and energy consumption. An optimization process is proposed by adopting NSGA-II and the technique for order preference by similarity to an ideal solution (TOPSIS) analysis to determine ideal operational parameters. The developed approach acts as a useful tool that assists tunnel construction automation and improves TBM performance under different in-situ conditions. Real data from a tunnel project in Singapore is utilized as a case study to examine the applicability and efficiency of the proposed approach. The results indicate that (1) The proposed meta -model provides reliable estimation with an average RMSE and MAE of 2.604 m m / m i n and 3.402 m m / m i n for TBM's penetration rate(O 1), 0.0211 and 0.0324 for over/under excavation ratio (O 2), and 15.512 k w h and 23.088 k w h for energy consumption (O 3), respectively. The prediction accuracy is better than the state-of-the-art methods; (2) The TBM's performance can be optimized by the proposed approach with an average improvement of 33.14 %, 1.32 %, and 17.95 % for O 1 to O 3 , respectively, and an overall improvement of 39.60 %; (3) The overall reliability of TBM operation improved after optimization with a significant reduction in data variance by 91.16 %, 76.92 %, and 97.35 % for O 1 to O 3 , respectively. This paper contributes to proposing a novel method that integrates LightGBM with NSGA-II in resolving the complex TBM operation problem by considering the major performance indexes including excavation efficiency, safety, and energy consumption during TBM operation. [ABSTRACT FROM AUTHOR]

The evaluation of urban energy internet (UEI) is crucial for the establishment of low-carbon cities. This paper aims to present a hybrid decision-making framework to address UEI evaluation problems from a sustainability perspective. First, the Fermatean fuzzy Aczel-Alsina power aggregation operators are proposed to aggregate evaluation information, and the remarkable properties of these operators are explored. Then, the combined weight of the criteria is determined through the decision-making trial and evaluation laboratory method and the Shannon entropy-based power weighted method within the Fermatean fuzzy circumstance. Moreover, the classic multiobjective optimization by ratio analysis plus the full multiplicative form method is extended using the proposed aggregation operators. Finally, an index system was constructed from a sustainability perspective, and the level of development of the four UEI projects is assessed using the proposed method. The experimental results demonstrate that the proposed method can provide beneficial guidelines for the construction and development of the UEI. Furthermore, the reliability and effectiveness of the proposed decision-making framework are validated through sensitivity analysis and a comparative study with existing MCDM methods. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a crucial method for assessing the efficiency of decision-making units (DMUs). In practice, it is common to encounter situations where the performance of a DMU at a certain stage is evaluated, especially in policy effect evaluation. The key to stage performance evaluation is to eliminate the influence of pre-stage inputs (outputs) on stage performance. This paper proposes stage ratio DEA models that consider two types of inputs and outputs: accurate ratio data and interval ratio data. The first form considers the evaluation of stage efficiency when accurate ratios of inputs and outputs are used at the beginning and end of the stage. The second form assesses stage efficiency using interval ratio data for interval inputs and outputs. To verify the validity of the proposed models, the numerical example validates the first form of stage ratio DEA models. And the second form is applied to evaluate the sustainable efficiency of 14 energy saving and environmental protection clean enterprises (ESEPCEs). The empirical results demonstrate that the proposed models provide a more accurate assessment of stage efficiency compared to the traditional DEA-CCR model. [ABSTRACT FROM AUTHOR]

The application of Machine learning and deep learning techniques for time series forecasting has gained significant attention in recent years. Numerous endeavors have been devoted to automating forecasting through the utilization of cutting-edge neural networks. Notably, the recurrent neural network (LSTM – Long Short-Term Memory) has emerged as a central concept in most research endeavors. Although LSTM was initially introduced in 1997 for sequence modeling, subsequent updates have primarily focused on language learning tasks. These updates have introduced various computational mechanisms within the LSTM cell, including the forget gate, input gate, and output gate. In this study, we investigate the impact of each computational component in isolation to analyze their effects on time series forecasting tasks. Our experiments utilize the Jena weather dataset and Appliance Energy Usage time series for evaluation. The experimental results reveal that variations of the LSTM model outperform the most popular LSRM cell format in terms of error rate and training time. Specifically, the variations identified in this paper demonstrate superior generalization capabilities and yield reduced forecasting errors. [ABSTRACT FROM AUTHOR]

Today, fog computing is an emerging technology to support resource-constrained Internet of Things (IoT) applications. The scalability, geographic distribution, and heterogeneity of edge computing nodes, as well as the diversity of users' expectations, have made optimal module placement, considering the maximum use of fog resources, a challenging optimization problem. This paper proposes a method based on distributed learning automata to reduce the search space by using the maximum capacity of fogs in a heterogeneous fog network. In this method, fog topology is mapped to a distributed learning automata. With the cooperation of the automaton in this DLA, the problem of module placement has been solved to reduce energy consumption and delay of applications. To evaluate the amount of energy consumption and the execution time of IoT applications, two single-objective cost functions for energy and delay and another single-objective function with simultaneous consideration of energy and delay have been used. The results indicate that the average efficiency of the proposed method is 15.99%, 18.21%, and 15.53%, respectively, compared to other methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach called F-CAPSO (Fuzzy Chaos Adaptive Particle Swarm Optimization) to enhance energy efficiency and security of data transmission in Mobile Ad hoc Networks (MANETs). MANETs are wireless networks comprising mobile nodes that encounter challenges such as limited battery power and mobility.Our approach incorporates routing and clustering processes in MANETs and leverages the Fuzzy-based CAPSO algorithm for selecting the optimal Cluster Head (CH). Additionally, we integrate Salp Swarm Algorithm (SSA) with Replacement Method and Sine Cosine algorithm (SCA) search (SSARM-SCA) algorithm to achieve optimal routing and secure data transmission. A trust-based encryption strategy is employed to establish a secure route.We evaluate the proposed approach using performance metrics such as throughput, network lifetime, end-to-end delay, packet delivery ratio, energy consumption, and routing overhead ratio. The simulation results demonstrate that our approach outperforms four state-of-the-art techniques (MARP-HO, ACO-HSA, GAHC, and HFAPSO) in terms of energy consumption and network lifetime, with improvements observed for up to 3500 rounds.Our proposed approach offers a comprehensive solution to address the challenges of energy efficiency and security in MANETs, making it beneficial for applications such as tracking and military environments. By integrating the Fuzzy-based CAPSO algorithm and the SSARM-SCA routing protocol, we optimize CH selection and routing paths, respectively, leading to better network performance and an extended network lifetime. [ABSTRACT FROM AUTHOR]

Ensuring a certain clustering of distributed sensor nodes is significant for energy efficiency of wireless sensor networks (WSNs). Recently, several energy efficient artificial intelligence based-methods that provide cluster scheduling schemes have been proposed. However, there are few methods providing balanced energy exhausting of the number of nodes in clusters. In this paper, we present a novel cluster scheduling scheme for WSNs using local gravitational method to adaptively determine the number of time-varying clusters based on a sequence of vectors. The main idea in this method is to be inspired by the physical features of gravity force among the mass units. These features are considered to be units of mass of nodes in vector space. It is based on the fact that with clustering, the highest gravitational force on moving the units of mass is applied by the area with feature vectors that are high-density. Cooperative and distributed clustering is formed and the number of clusters is found by sharing the gravitational force between neighboring nodes through a distribution-optimization scheme. The results of this study have been compared with the existing methods in various scenarios and with many performance criteria through the Matlab programming. Especially in dense networks, the proposed algorithm has increased the number of clusters and ensured the clustering balance as much as possible. The proposed method has scaled down the energy consumption, and enhanced the network lifetime. [ABSTRACT FROM AUTHOR]

The residual nodes energy consumption is a primary factor to extend the lifespan of network. Utilizing a sensor node's very low-power battery in Underwater Wireless Sensor Networks (UWSNs) is still a difficult task. To increase the network lifespan, this paper proposes an Energy Efficient Region based Source Distributed Routing Algorithm for Sink Mobility in Underwater Sensor Network (EERSDRA-GCOP-UWSN). The proposed work consists of three Phases that are initialization phase, Routing Phase and Data transmission. For initialization phase, sinks and the sensor nodes are initialized. For Routing phase, Energy EERSDRA with sink mobility is employed to identify the region of sensor nodes. For data transmission phase, Geographic and Cooperative Opportunistic Protocol is proposed to select the best relay node on the basis of depth fitness factor. The proposed EERSDRA-GCOP-UWSN is evaluated with the performance metrics. Then performance of the EERSDRA-GCOP-UWSN proposed method provides 18.409%, 13.843% and 23.768% high Network lifetime, 27.744%, 24.881% and 19.703% low energy consumption, 17.346%, 20.852% and 26.347% high throughput and 16.385%, 20.416% and 22.907% high PDR when compared to the existing methods such as, Energy-efficient regional based cooperative routing protocol with sink mobility for UWSN (EERCRP-UWSN), a safe energy efficient and cooperative routing protocol for UWSN (SEECRP-UWSN), geo-opportunistic routing protocols for improving the residual energy range (EEGORP-UWSN) methods respectively. Finally, the performance of the proposed method achieves better result while compared to the existing methods. [ABSTRACT FROM AUTHOR]

Predictive driving functions based on load profile prediction can make an important contribution to the urgently needed further reduction in energy consumption of road vehicles. In this context, this paper presents a method that precisely and efficiently predicts the driving resistances of an upcoming trip by means of online parameter estimation and system identification based on the driving route and weather data. A parameter estimation has been implemented that determines at the beginning of each trip different parameters such as the total vehicle mass, the dynamic rolling radius as well as the absolute and relative inclination of the vehicle relative to the road. It then performs an online calibration of the speed and acceleration calculation. In addition, a method was developed that allows the prediction of driving resistances based on the estimated parameters as well as environmental data such as road roughness and wind conditions. The developed functions were validated against real-world measurements under different environmental conditions and were able to reduce the prediction error in parameter estimation to a median of less than 2 %, while the prediction error of the driving resistances was reduced from sometimes more than 40 % to a few percentage points compared to conventional approaches. [ABSTRACT FROM AUTHOR]

The mature application of human–robot interaction technology provides a new operation method for remanufacturing waste products, especially in the disassembly process, which further exerts the respective advantages of humans and robots. Therefore, this paper proposes an innovative two-sided disassembly line balancing problem with human–robot interaction constraints (TDLBP-HRI). First, a mixed integer programming model is established for TDLBP-HRI to minimise the number of workstations and smoothness index while simultaneously optimising robot energy consumption and disassembly costs. Then, a discrete squirrel search algorithm (DSSA) is developed to efficiently solve large-scale problems caused by the NP-hard property of TDLBP-HRI. Since SSA is an algorithm that can only solve single-objective linear problems, this research attempts to discretise and improve it to solve multi-objective TDLBP-HRI. In addition, according to the characteristics of the problem, a two-layer decoding strategy is constructed to ensure the uniqueness of the solution, and a fast non-dominated sort is introduced to enhance the multi-objective optimisation capability of the DSSA. To verify the performance of the proposed DSSA, the benchmarks of two other DLBPs types are calculated, and the obtained results are found to be significantly better than those reported in the literature. The correctness of the MIP model and DSSA is verified by comparing the results of the GUROBI solver and DSSA in solving two small-scale TDLBP-HRI examples. Following this, DSSA is shown to outperform four other classical algorithms through different scales cases of TDLBP-HRI. Finally, the proposed model and algorithm are applied to a waste power battery disassembly example. The results show that the proposed model and method can improve the performance of the disassembly line and provide new insights into the application of human–robot interaction technology in the disassembly line. • Introduced human–robot interaction constraints for TDLBP. • Established a mixed integer programming model for TDLBP-HRI. • Developed a discrete squirrel search algorithm for TDLBP-HRI. • Applied the proposed model and algorithm to the waste battery module disassembly. [ABSTRACT FROM AUTHOR]

A bi-objective Fuel efficient Green Vehicle Routing Problem (F-GVRP) with varying speed constraint is discussed in this paper as an extension of Green Vehicle Routing Problem (G-VRP). F-GVRP is modelled to minimize both route cost and fuel consumption using goal programming. The problem is solved using Particle Swarm Optimization with Greedy Mutation Operator and Time varying acceleration coefficient (TVa-PSOGMO). The objective of this paper is to study the behaviour of F-GVRP under varying speed environment and its impact on the route cost and fuel consumption. Experiments are conducted with constant and varying speed constraints and it is observed that better routing plan with minimum fuel consumption can be achieved under varying speed environment. [ABSTRACT FROM AUTHOR]

Benefiting from the flexible, scalable and secure environment, hybrid cloud can overcome the shortage of limited resources in private cloud to simultaneously execute large-scale scientific workflows. In hybrid cloud, privacy-sensitive tasks are not allowed to be executed on public resources, while non-sensitive tasks are unrestricted. As an NP-Complete problem, it is extraordinarily challenging to schedule multiple workflows efficiently, economically and energy-savingly under quality-of-service constraints. This paper models the hybrid-cloud-based privacy-aware multi-workflow scheduling as a tri-objective optimization problem that optimizes workflow-oriented total tardiness, private-cloud-oriented total energy consumption, and public-cloud-oriented total monetary cost. To the best of authors' knowledge, few studies have been conducted on the tri-objective privacy-aware multi-workflow scheduling in hybrid cloud (PMWS-HC). To solve this problem, we dissect various factors involved during task scheduling and devise a novel Heuristic Scheduling Algorithm based on 9 Factors (HSA9Fs), which dynamically selects the workflows and tasks to be scheduled, and the corresponding VMs to execute them. To optimize the three conflicting objectives simultaneously, we propose a nested algorithm called MSIA, which first employs a M ulti-objective S alp swarm algorithm to explore for the Pareto solutions, and then uses an I terative greedy A lgorithm to perform a refined search on individuals to obtain high-quality solutions. Extensive Medium-Small-Scale and Large-Scale simulation experiments show that both HSA9Fs and MSIA outperform state-of-the-art scheduling algorithms in several multi-objective performance metrics. • Hybrid-cloud-based privacy-aware tri-objective multi-workflow scheduling is modeled. • A novel heuristic algorithm is devised by dissecting factors during task scheduling. • A nested algorithm is proposed to trade off Pareto optimality of three objectives. • Comprehensive experiments are conducted to evaluate the algorithms' performance. [ABSTRACT FROM AUTHOR]

A novel lane-changing trajectory control strategy in an iterative learning framework is proposed and its impact on fuel consumption of the off-ramp traffic system is analyzed in this paper. The iterative learning framework includes three layers. In the calibration layer, the lane-changing decision process is modelled by probabilistic neural network while a back-propagation neural network is designed to imitate the lane-changing trajectory. Moreover, we use a cost function and numerical simulation in the optimization layer to optimize the trajectory database and calibrate the proposed framework, respectively. In the application layer, simulation experiments are conducted to examine the fuel consumption of individuals and systems. The results indicate that connected automated vehicles (CAVs) can dissipate quickly after congestion is formed and complete lane-change and avoidance behavior in a more stable state after iterative learning. CAVs can reduce fuel consumption by 35% compared with human-driven vehicles. [ABSTRACT FROM AUTHOR]

• The lane-changing trajectory optimization is modeled as an optimal control model. • The original optimal control model is reconstructed to linearize all constraints. • The simplified optimal control model is transformed into NLP. • The effectiveness and sensitivity of the proposed model is verified and analyzed. The emerging autonomous vehicles (AVs) technologies provide a new opportunity to design a better lane-changing trajectory to reduce traffic congestion. The existing research on lane-changing trajectory planning mainly focuses on fitting trajectory curves, and the lane-changing trajectory is not optimized from the perspective of optimal control. To address this limitation, this paper proposes an optimal lane-changing trajectory planning model for AVs. First, the vehicle lane-changing process is modeled as an optimal control problem to minimize fuel consumption. The control variables are acceleration and steering angle. Second, to simplify the optimal control model, the speed and acceleration are decomposed in X and Y directions. The nonlinear constraints of the optimal control model are transformed into linear constraints. Then, we discretize the time of the optimal control model and simplify the problem into nonlinear programming (NLP). Finally, taking the NGSIM database and polynomial trajectory curve as the benchmark, we verify that the optimal lane-changing trajectory can significantly save energy consumption, with an average reduction of 37.52% and 32.48%. Sensitivity analysis indicates that (1) the minimum fuel consumption of lane-changing trajectory corresponds to optimal lane-changing duration and an optimal longitudinal displacement; (2) the more significant the speed difference (i.e., initial speed and final speed) before and after lane-changing, the greater the fuel consumption of lane-changing; (3) the fuel consumption of lane-changing increases with the increase of lateral displacement. [ABSTRACT FROM AUTHOR]

The classical distributed flexible job-shop scheduling problem (DFJSP) only considers static manufacturing environment and ignores operation inspection. However, in the real production, the manufacturing environment is normally dynamic and the operation inspection is very important to prevent unqualified products from being transported to the next manufacturing stage. In this paper, we first propose a dynamic distributed flexible job-shop scheduling problem considering operation inspection (DFJSPI). A modified memetic algorithm (MMA) is then designed to solve the DFJSPI aiming at to minimize the makespan and total energy consumption. In the MMA, a novel five-layer encoding method and an initialization method are developed to generate a high-quality initial population. A hybrid rescheduling method including three rescheduling strategies is developed to handle different inspection results. Some effective crossover operators, mutation operators and a local search operator are designed to expand the solution space of MMA and to accelerate its convergence speed. A total of 100 DFJSPI benchmark instances are constructed to test the performance of the MMA. Extensive experiments carried out to compare the proposed MMA with other three well-known algorithms demonstrate that the MMA has obvious superiority in almost all tested instances. This study will be beneficial for the production managers to deal with dynamical distributed manufacturing with operation inspection, especially providing reference for the manufacturing systems with operation reworking and scrapping. [ABSTRACT FROM AUTHOR]

• The paper is dedicated to proposed a cluster-based routing protocol for WBAN. • The paper utilizes the benefits of machine learning. • A novel approach, MGWOQL, is proposed for cluster head selection and updation. • The multi-objective function is designed to select the optimal cluster head node. • The result shows better network longevity, residual energy, and path loss. Recently, Wireless Body Area Networks (WBAN) have been developed to advance Internet-of-Things (IoT) that play an essential role in biomedical applications. While deploying these applications practically, there may arise associated issues. Among all the available problems, the primary concern is energy utilization among these resource-limited sensors during data communication. These sensors continuously sense the signal and send messages to other nodes. There is a need to optimize the energy utilization in WBAN. This paper proposes a cluster-based routing protocol for WBAN with the benefits of machine learning to predict energy wastage. A Modified Grey Wolf Optimization with Q-Learning (MGWOQL) is proposed for cluster head selection and updating. The proposed protocol used different objective functions to minimize the energy utilization of clusters by selecting the optimal cluster head (CH). The simulation was performed on the MATLAB platform under different conditions. The result analysis shows its efficiency in terms of energy for WBAN. [ABSTRACT FROM AUTHOR]

Due to its practicality, hybrid flowshop scheduling problem (HFSP) with productivity objective has been extensively explored. However, studies on HFSP considering green objective in distributed production environment are quite limited. Moreover, the current manufacturing mode is gradually evolving toward distributed co-production mode. Thus, this paper investigated a distributed hybrid flowshop scheduling problem (DHFSP) with objectives of minimization the makespan and total energy consumption (T E C). To address this problem, this paper designed a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG) by integrating the merits of genetic operator and iterated greedy heuristic. In this MOHIG, firstly, one cooperative initialization strategy is proposed to boost initial solutions' quality based on the previous experience and rules. Secondly, one knowledge-based multi-objective local search method is invented to enhance the exploitation capability according to characteristics of problem. Thirdly, an energy-saving technique is developed to decrease the idle energy consumption of machine tools. Furthermore, the effectiveness of each improvement component of MOHIG is assessed by three common indicators. Finally, the proposed MOHIG algorithm is compared with other multi-objective optimization algorithms, including SPEA2, MOEA/D, and NSGAII. Experimental results indicate that the proposed MOHIG outperforms its compared algorithms in solving this problem. In addition, this research can better guide practical production in some certain environments. • Considering green scheduling in distributed hybrid flowshop environment. • Designing a new energy-saving strategy into this problem. • Proposing a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG). • Evaluating performance of the proposed MOHIG by conducting comparison experiments. [ABSTRACT FROM AUTHOR]

Highlights • An innovative decision rule-based approach to tyre monitoring. • The approach relies on the Dominance-based Rough Set Approach. • Two analysis levels: pressure-oriented and temperature-oriented. • The approach has been validated within an important travelling company. • Real-world tests showed that the approach reduced substantially false alarms. Abstract Tyres are one of the most important safety components on a vehicle. Ignoring or failing to correctly set the tyre pressure may lead to accidents, and can affect the vehicle's fuel efficiency and tyre lifespan. Hence, there is a need for a Tyre Pressure Monitoring System (TPMS) that can effectively monitor the tyre condition. The current threshold-based TPMSs are characterised by a high number of false alarms. This is mainly due to: (i) the non-static and dynamic relationship between tyre pressure and temperature; and, (ii) the measurement error of the pressure/temperature sensors that are used for data collection. In this paper, we propose an innovative decision rule-based approach to tyre monitoring. This approach relies on the Dominance-based Rough Set Approach (DRSA), which is a well-known multicriteria classification and preference learning method. The DRSA takes a decision table as an input and it generates a collection of if-then decision rules as an output. The issue caused by the dynamic pressure/temperature relationship is solved by fixing one of the parameters and then generating the decision rules based on the other parameter. The problem of false alarms is solved by a discretisation of the scale of the fixed parameter. Based on these solutions, we designed two types of analysis levels: pressure-oriented analysis and temperature-oriented analysis. The proposed approach has been validated and implemented within an important travelling company that operates in the South of England. The real-world tests showed that the proposed approach has improved the current system and has led to a substantial reduction of false alarms. [ABSTRACT FROM AUTHOR]

Highlights • An approach for optimizing weighted matchings in wireless networks is proposed. • A distributed and asynchronous weighted matching algorithm is presented. • A tree-based efficient heuristic is provided for energy-efficient communication. • Time and message complexities are analyzed. • The effectiveness of proposed approach is shown by extensive experiments. Abstract Weighted graph matching is one of the most fundamental graph theoretical problems used in network design, especially in wireless network services such as radio resource allocation, physical layer security, energy-efficient partner selection and optimizing storage capacity. In this paper we present a distributed heuristic which provides nearly-optimal weighted matchings in polynomial time. We also propose an algorithm to decrease the number of transmitted messages to provide energy-efficient operation. Our approaches assume the asynchronous communication model and small messages having O (log (n)) bits. These assumptions directly fit the battery powered wireless networks such as wireless ad hoc and sensor networks. To the best of our knowledge, we propose the first distributed weighted matching algorithm which benefits from augmentation of augmenting paths having size larger than 3 for these networks. We also provide results of our simulations on synthetic geometric graphs to model wireless networks. Extensive simulations reveal that our algorithm improves the approximation performances of the other weighted matching algorithms and closes the gap between the approximation ratio and the optimum up to 32%. [ABSTRACT FROM AUTHOR]

Reducing emissions and energy consumption of autonomous vehicles is critical in the modern era. This paper presents an intelligent energy management system based on Reinforcement Learning (RL) for conventional autonomous vehicles. Furthermore, in order to improve the efficiency, a new exploration strategy is proposed to replace the traditional decayed ε-greedy strategy in the Q-learning algorithm associated with RL. Unlike traditional Q-learning algorithms, the proposed self-adaptive Q-learning (SAQ-learning) can be applied in real-time. The learning capability of the controllers can help the vehicle deal with unknown situations in real-time. Numerical simulations show that compared to other controllers, Q-learning and SAQ-learning controllers can generate the desired engine torque based on the vehicle road power demand and control the air/fuel ratio by changing the throttle angle efficiently in real-time. Also, the proposed real-time SAQ-learning is shown to improve the operational time by 23% compared to standard Q-learning. Our simulations reveal the effectiveness of the proposed control system compared to other methods, namely dynamic programming and fuzzy logic methods. [ABSTRACT FROM AUTHOR]

This paper proposes a method to resolve a strong coupling between temperature and relative humidity in drying room control systems. The coupling issue not only affects the control accuracy but also causes system instability, unnecessary adjustment, and extra energy consumption. A real-time temperature and relative humidity decoupling control method is reported in this paper. An improved aspirated psychrometer was used as a relative humidity measurement unit, which achieved relative humidity measurement accuracy of ±0.7%. The decoupling approach was developed based on a large real-time drying room, whose physical size was 22 × 15 × 3.5 m. A decoupling approach was developed using the adaptive neuro-fuzzy inference system for the control of temperature and relative humidity in the drying room. The simulated result shows that after the decoupling treatment, the relative humidity fluctuation was reduced from ±2.5% to ±0.6%. [ABSTRACT FROM AUTHOR]

In this paper, a smart home energy management system (SHEMS) is developed using a limited memory algorithm for bound constrained problems known as L-BFGS-B, along with time-of-use (ToU) pricing to optimize appliance scheduling in a 24-h period. The allocation of energy resources for each appliance is coordinated by a smart controllable load (SCL) device embedded in the household's smart meter. SCL guarantees automation of the proposed SHEMS and prevents manual participation of customers in demand response (DR) programs. The model is simulated on a population of 247 residential prosumers with solar PV systems based on 15-min interval electric load data from a residential community in Austin, TX. After clustering households based on their electricity profiles, the proposed optimization model is performed. Simulation results show that the proposed autonomous scheduling system reduces cumulative energy consumption for customers across the different clusters. In addition, when households are grouped based on their respective category according to the ToU pricing scheme, the simulation reports a notable decrease in total energy consumption, from 65.771 kWh to 44.295 kWh, as well as a reduction in the cumulative cost of energy, from $6.550 to $4.393 per day. Simulation results confirm that the proposed algorithm effectively improves the operational efficiency of the distribution system, reduces power congestion at key times, and decreases electricity costs for prosumers. [ABSTRACT FROM AUTHOR]

• Free calcium oxide content is an important indicator of cement quality. • The multi-objective optimization model for cement burning system is designed. • Electricity consumption and coal consumption weighted as a production target. • Add equipment condition, free calcium oxide content to the constraint space. • A two-cycle coupled energy consumption prediction control was established. A stable, high quality cement burning system producing clinker with low energy consumption is important for cement company. However, in the actual operation of the combustion system, there are contradictory indicators such as electricity consumption, coal consumption and clinker quality that are difficult to be jointly optimized, as well as dynamic and complex working conditions that are difficult to be artificially adjusted to the optimal state. In order to solve the above problems, a multi-objective optimal predictive control model is proposed in this paper. The prediction part of the model consists of a prediction model of cement burning system based on convolutional neural network. The optimization part, on the other hand, combines electricity and coal consumption into one energy target and uses cement clinker quality as a constraint to find the optimal production state. This modeling approach simplifies the problem and avoids the need to solve for the Pareto optimal solution. The control part is then based on the above-mentioned model with a double-cycle coupled cycle optimization to obtain set values of controlled variables that satisfy the changing operating conditions and finally control target to track the dynamic operating conditions. Experimental results show that the method described in this paper reduces the content of free calcium oxide after cement combustion, thus improving the quality of cement clinker, while reducing the fluctuations of the control variables in each production process and improving the stability of the combustion system. There is also a significant reduction in production energy consumption. [ABSTRACT FROM AUTHOR]

With the development of mobile augmented reality (MAR) technology, the demand for MAR applications is increasing. However, MAR is rarely used in mobile devices due to its high computational and energy consumption. In this paper, we study the task offloading and parameters optimization of MAR applied to mobile devices in mobile edge computing. Considering the influence of the MAR client energy consumption, service delay and detection accuracy in the task offloading and parameters optimization process, we design a function to evaluate MAR client energy efficiency. The problem of task offloading and parameters optimization is formulated to minimize energy efficiency function under the limitation of MAR task completion time and wireless bandwidth resources. To solve this problem, we propose a server selection and parameters optimization (SSPO) algorithm to realize client task offloading and parameters optimization. The SSPO algorithm first generates priority queue of tasks. Based on the order of priority queue, tasks are offloaded to appropriate mobile edge server according to the analytic hierarchy process. After that, the parameters are calculated and the tasks are redistributed according to the completion time until the energy efficiency function converges. Simulation results show that the proposed algorithm is better than the comparison algorithm. • A multi-MAR client and multi-server system is considered. • We design a function that balances energy, delay and detection accuracy. • We aim to minimize the designed function adjust MAR client parameters. • We design a server selection and parameter optimization algorithm. [ABSTRACT FROM AUTHOR]

• Unbalanced residential energy consumption data classification problem is tackled. • Urban vs rural energy consumption is classified in original and reduced feature space. • Monte Carlo under-sampling is used to obtain distribution of classification performance metrics. • Six family of classifiers were compared using 6 classification metrics for unbalanced data. • Hyperparameter tuning and optimal classifier pipeline were obtained using the genetic programming optimizer. Energy consumer locations are required for framing effective energy policies. However, due to privacy concerns, it is becoming increasingly difficult to obtain the locational data of the consumers. Machine learning (ML) based classification strategies can be used to find the locational information of the consumers based on their historical energy consumption patterns. The ML methods in this paper are applied to the Residential Energy Consumption Survey 2009 dataset. In this dataset, the number of consumers in the urban area is higher than the rural area, thus making the classification problem unbalanced. The unbalanced classification problem has been solved in original and transformed or reduced feature space using Monte Carlo based under-sampling of the majority class datapoints. The hyperparameters for each classification algorithm family is represented as an optimized pipeline, obtained using the genetic programming (GP) optimizer. The classification performance metrics are then obtained for different algorithm families on the original and transformed feature spaces. Performance comparisons have been reported using univariate and bivariate distributions of the classification metrics viz. accuracy, geometric mean score (GMS), F 1 score, precision, area under the curve (AUC) of receiver operator characteristics (ROC). The energy policy aspects for the urban and rural residential consumers based on the classification results have also been discussed. [ABSTRACT FROM AUTHOR]

Interest in smart green cities is growing, and IoT is an underlying driver of this trend. The integration of IoT in smart cities has altered the manner of working and living of society. IoT technologies do, however, come with a number of drawbacks, including vulnerability of highly secure devices, energy consumption, governance, etc. in smart cities. Governance in smart city is more citizen-center and the decisions of government are focused on citizens' demands. Government can identify influential spreaders from this huge voluminous population and take requisite preventive steps such as delaying the spread of rumor, identifying the information source, or blocking the source node. Hence, security of highly vulnerable IoT device and governance are the two main issues of the IoT environment of smart green city. This paper proposes a framework for identifying influential spreaders who play a key role in the governance of smart city as well as determining vulnerable IoT devices that needs to be protected from malware attacks. A framework named as Modified Dynamic Weighted Sum Method, is proposed for determining influential devices and spreaders in smart green cities. The connectivity between devices is considered as a network, then, distinct centrality measures are considered as the multi-attribute of the network, so as to take advantage of each centrality measure. Experiments are conducted on Santender testbed and it was found that static devices play an important role in IoT environment. Hence, they need to be protected from malware attacks. From future perspective, opportunities and challenges in smart city applications are discussed. • A MCDM based approach is proposed for identifying influential nodes. • This approach uses the SIR model for dynamic weight generation. • The connectivity between IoT devices is considered as a network. • Experiments are conducted on Santender testbed to validate efficiency. • It was found that static devices play an important role in IoT environment. [ABSTRACT FROM AUTHOR]

• The focus is on Shuttle-Lift-Crane Automated Storage and Retrieval Systems. • Heavy loads are moved and so reducing the total travelled distance is essential. • A dynamic operating framework (DOF) is presented. • It allows the system to autonomously react to changes in the operating conditions. • A set of allocation and interleaving policies is proposed to boost performance. Two very important aspects in automated storage and retrieval systems (AS/RS) are productivity (or performance) and maintenance costs. In the literature, as in industry, it is very difficult to find a solution that guarantees satisfactory results in terms of both. Moreover, all the solutions that the scientific community has proposed are static, i.e., the system's behavior does not change as boundary conditions change. In this paper, we propose an innovative solution known as a dynamic operative framework (DOF), which allows the system to react to changes in operating conditions and guarantees good results in terms of productivity, with a consistent reduction in the distance that handling machines travel and a consequent reduction in maintenance and energy consumption costs. To test the proposed solution, we focused on the shuttle-lift crane AS/RS, a common configuration used to store bundles of long metal bars, and we compared the DOF with four benchmark policies that exploit a class-based reorganization of the stock performed during non-working shifts. Many simulation runs' results indicated that the DOF ensures a throughput time aligned to that of the benchmarks, but without needing to reorganize the stock during nonworking shifts. In this way, it leads to consistent savings in terms of energy consumption and maintenance costs. [ABSTRACT FROM AUTHOR]

Multi-hop D2D communication has been proposed with the purpose of improving the coverage, quality of service (QoS), and flexibility and adaptability of single-hop D2D communication. However, multi-hop D2D communication often experiences obstacles caused by interference from the shared channel, which makes spectrum efficiency in multi-hop D2D networks an important issue to tackle. In this paper, we study the optimization of spectrum efficiency in multi-hop D2D communication underlaying cellular networks. First, we use iteration-based optimization techniques such as exhaustive search (ES) and gradient search (GS) with barrier function to find the global and local optimal solutions, respectively. More importantly, we propose two machine learning (ML) techniques, the unsupervised deep neural network (DNN) and deep Q-learning (DQL) algorithms and evaluate the performances of both algorithms compared to iteration-based optimization methods. The simulation results verify that both algorithms achieve near-global optimums compared to GS. Moreover, it is verified that the DQL outperforms the unsupervised DNN in terms of optimal spectrum efficiency, while the DQL algorithm has higher time complexity than the unsupervised DNN. • Evaluate energy efficiency of wireless 'multi-hop' D2D underlay cellular networks. • Build an optimization model for energy efficiency under given environment. • Design unsupervised DNN and deep Q-learning to solve the optimization model. • ML-based algorithms achieve near-global optimal solutions with lower time complexity. [ABSTRACT FROM AUTHOR]

The multi-objective gorilla troops optimizer (MOGTO) is a new version of the gorilla troops optimizer (GTO) proposed in this paper to address multi-objective optimization issues. The Pareto optimum solutions acquired by the GTO are saved in an external archive. In the multi-objective search region, the archive was used to mimic the gorilla groups' collective behavior. The suggested approach is evaluated statistically and qualitatively in solving various multi-objective issues using the congress on evolutionary computation (CEC) 2020 test bed. In large-scale wireless sensor networks, the proposed algorithm is also utilized to discover the minimal number of sink nodes with the lowest localization error, which will cap the whole network and increase the network lifespan. Meanwhile, the multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm version 2 (NSGA-II), multi-objective grey wolf optimizer (MOGWO), multi-objective whale optimization algorithm (MOWOA), multi-objective sine–cosine algorithm (MOSCA), multi-objective slime mould algorithm (MOSMA), multi-objective particle swarm optimization with ring topology and special crowding distance (MO_Ring_PSO_SCD), hybrid NSGAII-MOPSO, multi-objective evolutionary algorithm based on decomposition (MOEA/D), and improved multi-objective manta ray foraging optimization (IMOMRFO) are the ten familiar and strong optimization models, which are compared with the proposed algorithm. Simulation results in CEC'2020 test functions indicated that the proposed MOGTO can provide remarkable results than other optimization models in terms of Pareto set proximity (PSP), inverted generational distance in decision space (IGDX), and hyper volume (HV) indicators. Additionally, simulation results in large-scale wireless sensor networks show that the proposed algorithm can discover the smallest number of sink nodes and diminish the network's energy usage. • An efficient MOGTO algorithm is proposed based on NDS and CD techniques. • CEC 2020 test suite is utilized for verification of MOGTO performance. • MOGTO is proposed for minimizing energy consumption of LSWSNs. • MOGTO is analyzed using various analysis metrics. • The performance of the MOGTO is better than other competitor algorithms. [ABSTRACT FROM AUTHOR]

• Adaptive Optimization and Control module for residential appliances using IoT. • Maximizing the usage of the roof-top PV for prosumers. • Solutions to optimally use the local solar energy for off-grid & on-grid prosumers. • Forecast the PV output, load optimization and real-time control. The necessity to diversification in terms of energy sources, especially for residential consumers, becomes more stringent as the prices of electricity are going up. There are numerous incentives to generate electricity using roof-top Photovoltaic (PV) panels as it reduces the costs, grid dependency and partially replaces gas or other heating source. However, solutions to optimally use the local solar energy for off-grid and on-grid prosumers are incomplete and do not provide an efficient ready-to-use implementation schema. Thus, in this paper, we propose an Adaptive Optimization and Control (AOC) module for residential appliances using Internet of Things (IoT). It consists of forecasting the PV output, optimizing the schedule and controlling the prosumers' appliances in real-time to maximize the PV usage. This solution provides the layout concept and practical implementation in a real environment. For seasonal simulation, various prosumer profiles (on– and off-grid), with or without storage and different generation capacities are considered. The optimization process brings clear benefits for all types of prosumers, but the real-time monitoring and control algorithm is more valuable as it considers the current PV output and adapts the load in real-time so that to further enhance the Self-Sustainable Ratio (SSR). Furthermore, it protects the battery and prolongs its lifetime by reducing the number of charge/discharge cycles. On average, the SSR increases significantly by 12 or 13%. The SSR goes up to 93%, meaning that the consumption from grid is considerably reduced. More significantly, the impact on the environment and life quality will be consistent as the residential sector represents 27% of the total energy consumption, out of which 62.8% for heating. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs) to address the duality between the cost-effective energy efficiency and the reliable data delivery is a relevant issue. This paper presents a novel bio-inspired routing protocol, named CB-RACO, that combines the Ant Colony Optimization (ACO) meta-heuristic with the computationally cheap and distributed community detection technique Label Propagation (LP). CB-RACO creates communities in the WSNs and meets the balance of energy consumption by routing data inside-communities through swarm intelligence. As a consequence, CB-RACO demands low memory and overhead in construction and maintenance of routing paths. Additionally, CB-RACO achieves high data delivery reliability through a data retransmission strategy based on acknowledgments between communities. We simulated CB-RACO in large-scale scenarios according to the goodput, delivery delay and energy consumption metrics. The results have shown that the proposed approach may provide significant improvement in comparison to ant-based strategies that do not rely on community structures. [ABSTRACT FROM AUTHOR]

Crowdsourcing is a task assignment technology that has emerged in recent years, enabling companies to reduce costs and increase work efficiency. Most of the current crowdsourcing systems (BCSs) built on blockchain focus on solving privacy issues and use rigorous proof protocols for task and solution security. Such systems cannot be directly applied in mobile environments because it would lead to excessive computational overhead for the endpoint. To achieve confidentiality of crowdsourcing tasks during transmission while reducing the energy consumption of mobile devices and optimizing task assignment results, in this paper, we develop a blockchain-based crowdsourcing model. We first build a model structure consisting of basic components, such as users, smart contracts, and blockchain, and design contracts to implement various crowdsourcing operations, such as registering, publishing tasks, and acquiring solutions. Unlike before, the ciphertext of the task and the encrypted secret key are embedded into the contract. This, together with the three operations we designed for users to interact with the smart contracts, can effectively protect the crowdsourcing data from being stolen while using the address anonymity of Ethereum to ensure the privacy of users. During task assignment, we use the DBSCAN algorithm to delineate each valid service area and implement the established multiobjective planning model within each area to achieve as many benefit goals as possible in the best way. Finally, we implement our prototype on the Ethereum test network Ropsten. The experimental results show that the cost overhead of the designed model is acceptable to mobile users, and the security analysis establishes the confidentiality of the designed operations. • Blockchain-based model to achieve task security and task assignment. • Safe operation guarantees the confidentiality of user interaction. • Smart contract realizes crowdsourcing function. • Establish a multi-objective planning independent of task content. [ABSTRACT FROM AUTHOR]

This paper is based on the concept of dissonance, that is, gaps or conflicts existing in a specific knowledge base or among different knowledge bases. It presents a rule-based system that assists human operators in dissonance discovery and control by taking into account two kinds of dissonance, i.e., affordance to study conflicts of use, and inconsistencies to study conflicts of intention and action, through the analysis of cognitive behavior implemented in knowledge bases. This system elaborates the knowledge base composed of rules, and analyzes the knowledge content to discover new knowledge by creating additional rules, or to identify inconsistencies when conflicts between rules occur. The affordance discovery control process uses a deductive and an inductive reasoning algorithm of which the aim is to establish new rules using existing ones. The inconsistency discovery control process applies an abductive reasoning algorithm in order to determine contradictory rules when existing rules may result in opposite intentions being accomplished. Two groups of inconsistencies are addressed: interferences involving several decision makers, and contradictions involving the same decision maker. A knowledge acquisition control process facilitates the creation of the initial rules that contain parameters such as intentions relating to the goals to be achieved, actions to be performed to achieve these intentions, objects used to carry out these actions and the decision makers who execute these actions using the corresponding objects. A feasibility study taking into account five rule bases relating to the manual use of an Automated Speed Control System (ASCS), the automated control of the car speed by the ASCS, the manual control of aquaplaning, the manual control of the car speed, and the manual control of car fuel consumption is proposed to validate the rule-based support system. [ABSTRACT FROM AUTHOR]

• We study a multi-visit traveling salesman problem with multiple drones. • Drone energy consumption in all phases of flight is considered to plan drone trips. • We propose a subproblem to locate candidate non-customer retrieval nodes for drones. • MILP formulations are developed for the proposed subproblem and problem. • Effective heuristic and matheuristic solution approaches are provided for problems. This paper introduces the energy-constrained multi-visit traveling salesman problem with multiple drones considering non-customer rendezvous locations (EM-TSPDs). A ground vehicle equipped with multiple drones that can serve multiple customers per trip performs deliveries. Before solving the EM-TSPDs, the non-customer rendezvous location subproblem (NRLP) is addressed to locate candidate non-customer rendezvous nodes so that all drone customers can be covered by drones. The importance of this full coverage is that all drone customers can reap the benefits of drone delivery by receiving fast delivery or being served in areas that are inaccessible by road network. We propose MILP formulations for the NRLP and EM-TSPDs that can be solved with standard MILP solvers. Due to the NP-hard nature of the problem, heuristic algorithms are provided to solve medium and large-scale instances in a time-efficient manner. Numerical results show that using multiple drones that can serve multiple customers per trip leads to a reduction in the makespan. Taking into account the battery energy consumption at different phases of the drone flight leads to more accurate and realistic modeling of the drone endurance. In addition, the impact of adding non-customer rendezvous locations is investigated. [ABSTRACT FROM AUTHOR]

This paper aims to combine the hydraulic hybrid vehicle (HHV) architecture with optimum fuzzy logic control. Besides the expressive fuel savings of the HHV in urban routes, the engine intermittent operation with on–off control may delay the catalyst system warm-up, resulting in a higher level of emissions. Therefore, this study presents a multi-objective optimization in which the HHV drivetrain parameters are optimized, simultaneously with the fuzzy logic membership functions, defuzzification method, rules and weights, aiming to reach a trade-off among fuel savings and the minimization of the tailpipe emissions. To ensure feasible solutions, the engine simulation was based on the ADVISOR™ fuel converter block, which is a well-known tool applied in several similar works. The optimization procedure was carried out under the FTP-72 standard driving cycle, in which the HHV was able to reduce 13.07% H C , 10.55% C O , and 4.74% N O x emissions with 35.67% fuel savings, as compared to a conventional vehicle with similar characteristics and engine. Moreover, the optimized HHV was also evaluated under 3 different real-world driving cycles, resulting in fuel savings up to 43.06% and emissions below the conventional vehicle in all analyzed scenarios. [Display omitted] • Combination of the hybrid hydraulic architecture and optimized fuzzy logic control. • Multi-objective optimization of the hydraulic drivetrain and engine fuzzy control. • Decrease of 35.67% fuel, 13.07% HC, 10.55% CO, and 4.74% NOx under FTP-72 cycle. • Fuel savings up to 43% with lower levels of tailpipe emissions in real word cycles. [ABSTRACT FROM AUTHOR]

• Forecast electricity demand considering residents' psychological preferences. • Stationarity time series are established to avoid the impact of irregular factors. • Personalizes exogenous variables, combines correlations and importance. • A hybrid machine learning based model is built to improve forecasting performance. • Multiple implications are provided for the government and institutions. Faced with the explosive growth of population and rapid development of industrialization, household electricity demand (HED) has recently become an essential sector of energy demand. Very-short-term forecasting of HED is closely related to monitoring insecurity, detecting emergency and potential crisis. Consider challenges triggered by the characteristics of time series, firstly, a phase of data pre-processing with stationarity of time series is constructed to manage irregular trend factors. Subsequently, in the feature selection phase, a two-layer feature selection method is proposed with the combination of random forest and autocorrelation analysis to select the most relevant features, which simultaneously considers correlations and importance of input lagged variables as well as exogenous variables. After that, hybrid HED forecasting methods are constructed with incorporating multiple machine learning techniques in the forecasting phase, namely k-nearest neighbor, support vector regression , random forest and multilayer perceptron, using their respective advantages of managing different situations. As residents' psychological preferences and calendar variables highly affect future HED, this paper intends to add and test their effects on HED. Finally, the proposed methods are examined on a 10-min-ahead horizon application, whose forecasting results, comparison analyses and an extended experiment are given to illustrate their effectiveness, after which some implications are provided to show the insights gained from the results. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new decision making method for selection problems under uncertainty is proposed. • The method considers both the objective weights and the subjective preferences. • The method is applied to the selection of renewable energy technologies. • The best option varies depending on the coefficient factor of the subjective weight. • The method is applicable to other selection problems in different areas. The use of renewable energy technologies is a key factor for sustainable development but their selection from several alternatives is a difficult task that relies on the careful assessment of relevant criteria. While Multiple Criteria Decision Making (MCDM) methods have been used successfully in various renewable energy technology selection problems, the decision process becomes more challenging when preferential judgements are made on the basis of non-homogenous and imprecise input data, and when there is uncertainty due to disparities among decision makers. This paper presents a hybrid MCDM method capable of overcoming these problems by taking into account quantitative and qualitative data under a probabilistic environment in the context of group decision making. In this method, qualitative data is fuzzified and used along with quantitative data to develop a hybrid model. A coefficient factor allows decision makers to vary the weight of each quantitative model so that the resultant criteria weights and overall alternatives' scores consider both subjective considerations and objective information. An example is presented to showcase the usability of the method developed for ranking and evaluating renewable energy technologies in the mining industry. In addition, the impact of different coefficient factors on the final results was assessed by means of sensitivity analysis. The results indicate that the method developed is able to minimise the loss of valuable objective information, caused by the subjective bias of qualitative weights during the evaluations, by adjusting the coefficient factors of the hybrid model during the calculations. [ABSTRACT FROM AUTHOR]

Blockchain is one of the most disruptive technologies introduced in Bitcoin, which engaged great attention from the industry and academia and determined a rapid growth of other Distributed Ledger Technologies (DLTs). In the complex architecture of a DLT system, a consensus protocol plays a key role by ensuring that all participants agree on the data integrity without any central authority. A wide range of consensus protocols have been designed with different concepts and properties (e.g., lower energy consumption, better scalability, smaller latency, higher throughput, etc.). The key requirements for consensus protocols passing from one blockchain system to another often differ significantly, and there is no one-fit-all protocol. Therefore, selecting the most suitable consensus protocol for a particular DLT system is essential, but at the same time a challenging step, as decision-makers need to make a trade-off between conflicting requirements. This paper introduces a framework for selecting the most suitable consensus protocols depending on the identified criteria, priorities, and other requirements by incorporating Multi-Criteria Decision-Making (MCDM) techniques. We demonstrate its potential by identifying the preferable consensuses for the three most common types of existing blockchain systems and on an actual application for bike renting. Moreover, the collected data and tools are freely available, ensuring full replicability, reusability, and further development. • A comprehensive literature review dedicated to selecting consensus protocols. • A new open-source data collection reflecting 18 state-of-the-art consensus protocols. • The first MCDM-based framework to identify preferable consensus protocols. • Demonstration on three main types of blockchains and bike renting application. [ABSTRACT FROM AUTHOR]

• Landing sites selection for swarm UAVs using optimization models. • A coarse-to-fine-based manner is adopted to reduce the computation complexity. • Optimization landing model considering multiple constraints. • Three scenes demonstrate the effectiveness in normal and emergency situations. Autonomous selection of landing sites is an important capability of a mission when unmanned aerial vehicles (UAVs) face technical difficulties or adverse weather, especially in unknown environments. In order to build a better decision-making system for swarm UAVs landing, besides terrain safety, the global flight cost among individuals should be also considered during descent. However, the existing methods cannot solve these problems elegantly due to terrain uncertainty and optimization complexity. In this paper, we present an optimization method to tackle this issue based on point cloud integrating the coarse-to-fine manner and swarm intelligence model. Specifically, our method starts with a point cloud preprocessing module for sparse elevation estimation and robust path planning. In the coarse stage, a novel cost map is constructed by extracting only low-level features from the elevation map. Based on the optimum results of the coarse selection, the final landing sites are automatically generated and finely evaluated in terms of multiple 3D terrain factors. Finally, the landing model of swarm-UAVs is treated as an unbalanced assignment problem, which minimizes flight costs by incorporating terrain safety, fuel consumption, and path planning. Experimental evaluations on three different real-world scenario datasets demonstrate the effectiveness of our method in both normal operations and emergency situations. [ABSTRACT FROM AUTHOR]

• An energy-consuming attention-shifting mechanism (ASHEED) is proposed. • The ASHEED algorithm uses a competitive radius-based clustering method. • The comprehensive performance of ASHEED algorithm is much better than RUHEED. The sensing node clustering algorithm is a network topology control method that can effectively extend the lifetime of smart sensing systems (SSSMs). However, the traditional topology algorithms suffer from the excessively early death of cluster heads. Hence, the attention-shifting mechanism for energy consumption based on hybrid energy-efficient distributed clustering (HEED) is proposed in this paper, called attention-shifting hybrid energy-efficient distributed clustering (ASHEED). The energy consumption of the cluster heads are reduced by shifting data reception and fusion energy consumption of the cluster heads to other cluster member nodes (Agents) within its competitive radius. Agent selection is performed by communication energy consumption comparison to ensure the rationality of cluster heads and agent positions with the aim of reducing the communication energy consumption for re-clustering. Experiment results demonstrated that the proposed approach could maximize the balance of energy consumption of cluster heads and common nodes, maintain the integrity of the network, and extend the optimal operation time of SSSMs. [ABSTRACT FROM AUTHOR]

An adaptive and rule-based driving system is being developed that tries to improve the driving behavior in terms of the energy-efficiency and safety by giving recommendations. Therefore, the driving system has to monitor the adherence of driving rules by matching the rules to the driving behavior. However, existing rule matching algorithms are not sufficient, as the data within a driving system is changing frequently. In this paper a rule matching algorithm is introduced that is able to handle frequently changing data within the context of the driving system. 15 journeys were used to evaluate the performance of the rule matching algorithms. The results showed that the introduced algorithm outperforms existing algorithms in the context of the driving system. Thus, the introduced algorithm is suited for matching frequently changing data against rules with a higher performance, why it will be used in the driving system for the detection of broken energy-efficiency or safety-relevant driving rules. [ABSTRACT FROM AUTHOR]

While the auxiliary services required for the operation of power plants are not the main components of the plant, their energy consumption is often significant, and it can be reduced by implementing a series of improvement strategies. However, the cost of implementing these changes can be very high, and has to be evaluated. Indeed, a further economic analysis should be considered in order to maximize the profitability of the investment. In this paper, we propose a multiobjective optimization problem to determine the most suitable strategies to maximize the energy saving, to minimize the economic investment and to maximize the Internal Rate of Return of the investment. Solving this real-life multiobjective optimization problem with a decision maker presents several challenges and difficulties and we have developed a novel interactive procedure which combines three different approaches in order to make use of the main advantages of each method. The idea is to start with the approximation of the Pareto optimal set, in order to gain a global understanding of the trade-offs among the objectives, using evolutionary multiobjective optimization; next step is aiding the decision maker to explore the efficient set and to identify the subset of solutions which fits her/his preferences, for which interactive multiple criteria decision making methodologies are used; and finally we concentrate the search for new solutions into the most interesting part of the efficient set with the help of a preference-based evolutionary algorithm. This allows us to build a flexible scheme that is progressively adapted to the decision maker’s reactions until (s)he finds the most preferred solution. The interactive combined procedure proposed is applied in practice for solving the problem of the auxiliary services with a real decision maker, extracting interesting insights about the efficiency improvement of the auxiliary services. With this practical application, we show the usefulness of the interactive procedure proposed, and we highlight the importance of an understandable feedback and an adaptive process. [ABSTRACT FROM AUTHOR]

Blockchain, the underlying technology of Bitcoin, refers to the public ledger used in a distributed network. Because blockchain does not rely on a central authority, peers have to agree on the state of the ledger among themselves, i.e., they have to reach a consensus on the state of the transactions. The way nodes reach that consensus has gained incredible attention in the literature. Bitcoin uses the Proof-of-Work (PoW) mechanism, as did Ethereum at first. The latter decided to move from PoW to Proof-of-Stake (PoS) because of the high energy consumption required by PoW. To date, many other consensus protocols have been proposed to address the limitations of the seminal ones. In this paper, we inform researchers and practitioners about the current state of consensus protocols research. The aim is to provide an analysis of the research introducing new consensus protocols in order to enable a more unified treatment. To that end, we review 28 new consensus protocols and we propose a four-category classification framework: Origin, Design, Performance and Security. We demonstrate the applicability of the framework by classifying the 28 protocols. Many surveys have already been proposed in the literature and some of them will be discussed later in the paper. Yet, we believe that this work is relevant and important for two reasons. Firstly, blockchain being a fast evolving topic, new consensus protocols emerge regularly and improvements are also put forward on a regular basis. Hence, this work aims at reflecting the latest state-of-the-art in terms of consensus protocols. Secondly, we aim to propose a comprehensive classification framework, integrating knowledge from multiple works in the literature, as well as introducing classification dimensions that have not been proposed before. This work demonstrates that multiple consensus have been proposed in a short period of time, and highlights the differences between these protocols. Furthermore, it is suggested that researchers and practitioners who aim to propose consensus protocols in the future should pay attention to all the dimensions presented in the classification framework. • Blockchain refers to the digital public ledger used in a distributed network. • Peers in a blockchain have to reach a consensus on the state of the transactions. • The seminal consensus protocols (Proof-of-Work and Proof-of-Stake) face a number of limitations. • Many consensus protocols have been proposed to mitigate these limitations. • A classification framework can help the analysis and development of existing and new consensus protocols. [ABSTRACT FROM AUTHOR]