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Service-oriented manufacturing (SOM) is a new industrial model that combines manufacturing with service. To enhance the competitiveness of manufacturers in SOM systems, we investigate a coordinated production and delivery scheduling problem considering a third-party logistics (3PL) provider. The considered problem can be divided into two phases, including production phase and delivery phase. In the production phase, jobs are processed by multiple manufacturers located in different geographical zones. Jobs are with deteriorating processing time and set-up time. Several structural properties of the problem in the production phase are proposed, based on which a heuristic algorithm is designed to make decisions on job batching and batch sequencing. After being processed, the finished jobs are immediately delivered to a customer by manufacturers' own vehicles or the 3PL provider. A dynamic programming algorithm is developed to solve the problem in the delivery phase. The integrated scheduling problem is proved to be NP-hard. Hence, we develop a general variable neighbourhood search (GVNS) to solve the problem in a reasonable time. The basic variable neighbourhood descent (BVND) is used as a local search method in the GVNS. Finally, the computational experiments are conducted and the results verify the effectiveness and stability of the proposed approach. [ABSTRACT FROM AUTHOR]

Ship maintenance service optimisation is of great significance for improving the competitiveness of shipbuilding enterprises. In this paper, we investigate a ship maintenance service scheduling problem considering the deteriorating maintenance time, distributed maintenance tasks, and limited maintenance teams. The objective is to minimise the service span. First, we construct an initial mixed-integer programming model for the studied problem. Then, through the property analysis of the problem with a single maintenance team, an exact scheduling algorithm is proposed. In addition, the lower bound of the problem with multiple maintenance teams is derived. A scheduled rule is developed to obtain the lower bound for the problem. Based on the property analysis, the original mixed-integer programming model is simplified to an improved mathematical programming model. Since the studied problem is NP-hard, this paper proposes two heuristic algorithms and an integrated metaheuristic algorithm based on the variable neighbourhood search to obtain approximate optimal solutions in a reasonable time. In computational experiments, the two models can solve problems on small scale, while metaheuristics can find approximately optimal solutions in each problem category. Moreover, the computational results validate the performance of the proposed integrated metaheuristic in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

The development of sharing economy and new information technologies has promoted the emergence of third-party shared manufacturing platforms (TPSMPs). In the shared manufacturing context, one main challenge to TPSMPs is the matching of manufacturing enterprises with insufficient production capacity (capacity demanders) and those with overcapacity (capacity suppliers), where available capacities of capacity suppliers are within time ranges, and orders of capacity demanders can be split. In this capacity matching problem with capacity time windows and order splitting (CMPCTW-OS), each capacity demander's order needs to be delivered on time, while each capacity supplier can also match with multiple capacity demanders and fulfil orders of the capacity demanders by sequence. A mathematical model for the CMPCTW-OS is developed to maximise the total profit of the TPSMP. Then, we design a two-stage heuristic algorithm to solve this model. In the first stage, the inserting algorithm (IA) is used to obtain an initial feasible solution. In the second stage, the iterated local search (ILS) is applied to optimise and improve the initial feasible solution. Finally, in numerical simulation experiments, the effectiveness of IA-ILS has been verified by comparison with the GUROBI solver. [ABSTRACT FROM AUTHOR]

Over the last decades, scheduling theory has been put into practice so as to tackle production process as a combinatorial optimisation problem and specifically as a PFSP (Permutation Flowshop Scheduling Problem). Often, NP-completeness of PFSP leads numerous research towards suggesting heuristic algorithms. The objective is to propose an optimal or near-to-optimal order of n jobs processing on m machines with a minimum completion time of all jobs. In this paper, a two-phase heuristic algorithm will be presented, named IFRS (Improved FRS). FRS, within the first phase, is going to be used to find a superior order of jobs out of Taillard's instances; then the order of jobs will be improved again in phase 2. While running IFRS, a completely new idea, named Pixelation of time matrices, will be used for the very first time and provide a final pattern, which can be used in any heuristic algorithm with makespan criterion. After using the hard benchmark instances of Taillard, the superiority of IFRS over 20 algorithms is going to be shown by tables and statistical graphics. Due to its low makespan values, IFRS benefits flow production, as does NEH. [ABSTRACT FROM AUTHOR]

The container pre-marshalling problem (CPMP) aims to minimise the number of reshuffling moves, ultimately achieving an optimised stacking arrangement in each bay based on the priority of containers during the non-loading phase. Given the sequential decision nature, we formulated the CPMP as a Markov decision process (MDP) model to account for the specific state and action of the reshuffling process. To address the challenge that the relocated container may trigger a chain effect on the subsequent reshuffling moves, this paper develops an improved policy-based Monte Carlo tree search (P-MCTS) to solve the CPMP, where eight composite reshuffling rules and modified upper confidence bounds are employed in the selection phases, and a well-designed heuristic algorithm is utilised in the simulation phases. Meanwhile, considering the effectiveness of reinforcement learning methods for solving the MDP model, an improved Q-learning is proposed as the compared method. Numerical results show that the P-MCTS outperforms all compared methods in scenarios where all containers have different priorities and scenarios where containers can share the same priority. [ABSTRACT FROM AUTHOR]

Along with the rapid growth of online purchasing, more and more e-retailers tend to adopt the front warehouse mode to improve the performance of customer service by locating multiple front warehouses with limited space and storage capacity closer to consumers. In this context, order splitting and distribution become increasingly crucial decisions that might impact the efficiency of order fulfillment. These two issues are investigated in the related works commonly as two independent problems, although they are inherently coupled with each other. This study established an integrated optimization model for order splitting and distribution routing for the front warehouse mode e-retailing. The model considers practical features, including order splitting constraints based on product type and quantity, finite inventory, heterogeneous vehicle routing constraints, and time windows. A branch-price-and-cut algorithm is proposed to solve the problem. Two logic-based Benders cuts are designed to deal with the infeasible distribution routes. The efficiency of the proposed algorithm is verified in an experimental study by considering CPLEX and two heuristic algorithms as benchmark methods. The dominance of our proposed algorithm is observed, especially for large-scale cases. The impacts of overlapping inventory levels, the combination of heterogeneous vehicles, and the width of time windows are also examined. [ABSTRACT FROM AUTHOR]

We consider a single-machine scheduling problem with due-date assignment and stochastic processing times, where only the mean and support (i.e. an interval bounded with lower and upper values) of processing times are known to the decision maker. The objective is to jointly determine a scheduling policy and a set of due dates for all jobs, so as to minimise the total expected individually weighted costs of earliness, tardiness and due-date assignment. By identifying an upper bound with the robust optimisation approach and a lower bound, and using a linear function of them to approximate the studied objective function, we establish an approximated problem. Then, a branch-and-bound algorithm is proposed to find an optimal solution for the approximated problem. Finally, a series of computational experiments are conducted to examine the performance of problem approximation and two developed heuristic algorithms. HIGHLIGHTS Study due-date assignment scheduling problem with stochastic processing times. Apply the mean and support (interval data) to model stochastic processing times. Use a linear function of identified lower and upper bounds to make an approximation. Derive optimal due-date assignment and develop branch-and-bound algorithms. Evaluate the efficiency of branch-and-bound algorithm and heuristic algorithms. [ABSTRACT FROM AUTHOR]

A class of strong lower bounds on the solution value of a Linearised Integer Programming reformulation is introduced for the binary quadratic optimisation model to assign origin and destination nodes to strip and stack doors, resp., in a cross-dock infrastructure. The goal is to minimise the transportation cost of the commodities to be handled at the cross-dock. Strip- and stack-related decomposition submodels are developed by taking benefit of the Integer Linearisation Property that appears in the new model. A linear search heuristic is also provided for obtaining feasible solutions by exploiting the special structure of the problem. We present an extensive computational study on a testbed of 55 instances to show that the proposed joint scheme for lower bounding and feasible solution providing is very efficient. The results obtained by our proposal are similar to those obtained by CPLEX in the 50 instances taken from the literature, but usually the time is much smaller for the large instances. The comparison with CPLEX is particularly good for the new five largest instances, which make it very promising for its application in real-life cases. [ABSTRACT FROM AUTHOR]

We consider the context of machining systems design where both equipment selection and line balancing decisions have to be taken in order to minimise the total cost of the system. The designed flow line employs multi-positional machines with rotary tables where vertical and horizontal machining modules can be used for the realisation of machining processes. For this challenging optimisation problem in production research, we develop an innovative mathematical model based on a mixed-integer linear programme and a heuristic algorithm for an approximate solution. An extensive numerical experiment is conducted in order to evaluate the performances of the proposed mathematical model and the developed heuristic. The obtained results show that the decision makers can use the elaborated methods for solving efficiently even large-scale industrial problems. [ABSTRACT FROM AUTHOR]

We study a problem from a real-world application, in which a daily set of orders must be processed following two stages, consisting of preparing perishable products on benches and allocating them to conveyors to be packed in disposable trays. Daily decisions must be made regarding the number and start time of working shifts, the number of workers and their allocation to machines, and the scheduling of orders in a two-stage flexible flow shop environment. The flow shop environment of the studied problem is common in many industries of perishable products, making the problem very general. The problem involves a number of operational constraints, and three objective functions that are minimised in a lexicographic way. To solve the problem, we implement a constructive heuristic and embed it within three metaheuristics: a Random multi-start algorithm (MR), a Biased random key genetic algorithm (BRKGA), and a Variable neighbourhood search (VNS) based one. We perform computational experiments over a set of realistic instances, and present a lower bound obtained from a constraint programming model for the scheduling counterpart. The results of the experiments show that the BRKGA is the most effective in practice for the integrated problem of workforce allocation and scheduling. [ABSTRACT FROM AUTHOR]

We study a two-stage hybrid flow shop problem arising from a fine chemicals production facility, where the first stage is a continuous chemical reaction process and the second stage is a discrete filling-packaging process. The objective is to minimise the total weighted completion time through batching of the jobs and scheduling of the batches at first stage and scheduling of the jobs at second stage. We formulate the problem as a mixed integer programming model and develop a Lagrangian relaxation-based framework for solving it, where the original problem is decomposed into family-level subproblems, and each subproblem is transformed into a set-partitioning problem. The subproblems are solved to optimality via branch and price algorithm. We also propose two heuristic algorithms for reducing computational efforts without much loss of solution quality. Finally, we conduct computational experiments with both randomly generated and real data sets to test the performance of the three proposed algorithms and demonstrate that the algorithms perform efficiently. [ABSTRACT FROM AUTHOR]

In this paper, a practical relevant operational production planning problem in complex manufacturing systems is addressed. In this problem, lots are planned individually to provide a more detailed plan than approaches that only consider production quantities. A three-step approach, which is currently fully integrated and used in a Decision Support System, is then introduced. This work follows the one of Mhiri et al. [2018. "Heuristic Algorithm for a WIP Projection Problem at Finite Capacity in Semiconductor Manufacturing." IEEE Transactions on Semiconductor Manufacturing 31 (1): 62–75] who addressed this problem. We push the approach a step further by introducing new optimisation possibilities through new smoothing rules, whose performance is studied according to different indicators. Furthermore, we present the production planning process in which the decision support tool is embedded and how it bridges the gap between the upper and lower planning levels. [ABSTRACT FROM AUTHOR]

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between exploration and exploitation phases, and insufficient precision in global search. This paper proposes a novel adaptive PID control algorithm based on enhanced dung beetle optimizer (EDBO) and back propagation neural network (BPNN). Firstly, the diversity of exploration is increased by incorporating a merit-oriented mechanism into the rolling behavior. Then, a sine learning factor is introduced to balance the global exploration and local exploitation capabilities. Additionally, a dynamic spiral search strategy and adaptive -distribution disturbance are presented to enhance search precision and global search capability. The BPNN is employed to fine-tune both PID and network parameters, leveraging its powerful generalization and learning ability to model nonlinear system dynamics. In the simplified motor experiments, the proposed controller achieved the lowest overshoot (0.5%) and the shortest response time (0.012 s), with a settling time of 0.02 s and a steady-state error of just 0.0010. In another set of experiments, the proposed controller recorded an overshoot and response time of 0.7% and 0.0010 s, across five DC motor tests. These results demonstrate the proposed adaptive PID control algorithm has superior performance in optimizing control system parameters, as well as improving system robustness and stability. [ABSTRACT FROM AUTHOR]

With the increased construction reservoirs, hydropower systems are becoming larger and more complex, which brings challenges of optimal operation of large-scale reservoirs to improve the power generation. To address this efficiently, we propose an aggregation-decomposition method based on cascade reservoir drawdown rule. Based on a two-stage method, we analyze the monotonicity of power generation increment of cascade reservoirs and propose the drawdown rule, which we used to guide the drawdown order of cascade reservoirs. On this basis, we propose an aggregation-decomposition coupling drawdown rule and progressive optimal algorithm (ADDR-POA) method of large-scale reservoirs. To confirm the viability of the proposed approach, we selected 29 series–parallel-mixed reservoirs in the upper Yangtze River Basin in China as the study subjects and optimized them with the goal of maximizing the total power generation. Results show that compared to conventional mathematical optimization method and heuristic algorithm, ADDR-POA can effectively express the compensation effect between reservoirs and has a good performance in improving the total power generation of the basin and reducing iteration times, which presents a novel approach for solving the problem of drawdown operation of large-scale reservoirs. [ABSTRACT FROM AUTHOR]

Motivation: Genome assembly aims to reconstruct the whole chromosome-scale genome sequence. Obtaining accurate and complete chromosome-scale genome sequence serve as an indispensable foundation for downstream genomics analyses. Due to the complex repeat regions contained in genome sequence, the assembly results commonly are fragmented. Long reads with high accuracy rate can greatly enhance the integrity of genome assembly results. Results: Here we introduce GTasm, an assembly method that uses graph transformer network to find optimal assembly results based on assembly graphs. Based on assembly graph, GTasm first extracts features about vertices and edges. Then, GTasm scores the edges by graph transformer model, and adopt a heuristic algorithm to find optimal paths in the assembly graph, each path corresponding to a contig. The graph transformer model is trained using simulated HiFi reads from CHM13, and GTasm is compared with other assembly methods using real HIFI read set. Through experimental result, GTasm can produce well assembly results, and achieve good performance on NA50 and NGA50 evaluation indicators. Applying deep learning models to genome assembly can improve the continuity and accuracy of assembly results. The code is available from https://github.com/chu-xuezhe/GTasm. [ABSTRACT FROM AUTHOR]

While the proliferation of the Internet of Things (IoT) has revolutionized several industries, it has also created severe data security concerns. The security of these network devices and the dependability of IoT networks depend on efficient threat detection. Device heterogeneity, computing resource constraints, and the ever-changing nature of cyber threats are a few of the obstacles that make detecting cyber threats in IoT systems difficult. Complex threats often go undetected by conventional security measures, requiring more sophisticated, adaptive detection methods. Therefore, this study presents the Hybrid approach based on the Support Vector Machines Rule-Based Detection (HSVMR-D) method for an all-encompassing approach to identifying cyber threats to the IoT. The HSVMR-D employs SVM to categorize known and unknown threats using attributes acquired from IoT data. Identifying known attack signatures and patterns using rule-based approaches improves detection efficiency without retraining by adapting pre-trained models to new IoT contexts. Moreover, protecting vital infrastructure and sensitive data, HSVMR-D provides a thorough and adaptable solution to improve the security posture of IoT deployments. Comprehensive experiment analysis and simulation results compared to the baseline study have confirmed the efficiency of the proposed HSVMR-D. Furthermore, increased resilience to completely novel changing threats, fewer false positives, and improved accuracy in threat detection are all outcomes that show the proposed work outperforms others. The HSVMR-D approach is helpful where the primary objective is a secure environment in the Internet of Things (IoT) when resources are limited. [ABSTRACT FROM AUTHOR]

Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort. [ABSTRACT FROM AUTHOR]

We present a collection of simulations of the Edwards–Anderson lattice spin glass at T = 0 to elucidate the nature of low-energy excitations over a range of dimensions that reach from physically realizable systems to the mean-field limit. Using heuristic methods, we sample the ground states of instances to determine their energies while eliciting excitations through manipulating boundary conditions. We exploit the universality of the phase diagram of bond-diluted lattices to make such a study in higher dimensions computationally feasible. As a result, we obtain a variety of accurate exponents for domain wall stiffness and finite-size corrections, which allow us to examine their dimensional behavior and their connection with predictions from mean-field theory. We also provide an experimentally testable prediction for the thermal-to-percolative crossover exponent in dilute lattice Ising spin glasses. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motor (PMSM) drive systems are receiving increasing attention due to their superior control quality and efficiency. Optimizing the control parameters are key to achieve the high-performance operation of PMSMs. Although heuristic algorithms demonstrate excellent optimization outcomes in simulations, there are still challenges in deploying optimization schemes in practical drives. In this study, a real-time online deployable control parameter optimization scheme is proposed. The optimization effect is evaluated through the system step response performance, and a framework for deploying optimization algorithms within the driver is developed. A fault suppression mechanism is also designed to mitigate overshoot and vibration issues caused by suboptimal solutions. The proposed scheme is validated on a rapid prototyping control platform. Experimental results confirm that the scheme exhibits good optimization performances across various operating conditions. The honey badger algorithm employed in this paper shows faster convergence and more stable optimization effects than other optimization algorithms. The optimization effect is improved by 2.2% and its performance in terms of consistency across multiple optimization results has increased by 40%. [ABSTRACT FROM AUTHOR]

The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra's shortest path searching is utilized to accelerate the process of routing in large-scale multi-domain elastic optical networks (EONs). The layered-graph approach is adopted in the spectrum allocation stage. To this end, an efficient heuristic algorithm is proposed, called "Branch-and-Bound based Routing and Layered Graph based Spectrum Allocation algorithm (BBR-LGSA)", which is an integrated RSA algorithm. Notably, the significant reduction in algorithm time complexity is not only reflected in the pruning method used in the routing stage but also in the construction of auxiliary graphs during the spectrum allocation stage utilizing the Branch-and-Bound method. Simulation results show that the proposed BBR-LGSA significantly reduces the average running time by nearly 78% with higher spectrum utilization in large-scale multi-domain EONs, compared with benchmark algorithms. In addition, the impact of key parameters on performance comparisons of different algorithms is evaluated. [ABSTRACT FROM AUTHOR]

The paper presents the process of optimizing the duty cycle of a rotary crane. The minimization of the carried load's trajectory was chosen as the objective function. The research was conducted using the genetic algorithm and the particle swarm algorithm. The influence of particular algorithm parameters on the obtained optimal solution was characterized. For the obtained best case, the inverse kinematics problem was solved, allowing us to determine the control functions of individual crane members. The presented redundant system was solved with the use of an algorithm for temporarily limiting the movement of specific kinematic pairs. On the basis of the obtained results, it was determined which of the algorithms used is more favorable, taking into account the crane's operational safety and lifting capacity. [ABSTRACT FROM AUTHOR]

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method's effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm's efficacy for real-world applications. [ABSTRACT FROM AUTHOR]

Maintaining user privacy and security is a critical concern in vehicular ad hoc networks (VANETs). However, prior research has neglected the study of matrix recovery attack methods in VANETs and the challenge of reducing the number of roadside units (RSUs). In this article, we formulate a path recovery strategy using matrix recovery techniques from an adversarial view. Subsequently, the challenge of minimizing RSUs while monitoring all user vehicles in a region is converted into a set cover problem. We introduce a heuristic algorithm that utilizes clustering to address this issue. To minimize matrix recovery errors, a Kalman filter based method is integrated to enhance the performance. This paper also presents an initial deployment of path recovery attacks, maintaining effectiveness even with certain defense mechanisms in place. Furthermore, we conduct simulation experiments to evaluate the effectiveness of the proposed attack strategy. The simulation results demonstrate the performance across various dimensions. Finally, the results show that the success rate of our proposed counter‐defense strategy in overcoming user defenses surpasses 50%. [ABSTRACT FROM AUTHOR]

This study investigates the truck–cargo matching problem in less-than-truckload (LTL) logistics hubs, focusing on optimizing the three-dimensional loading of goods onto standardized pallets and assigning these loaded pallets to a fleet of heterogeneous vehicles. A two-stage hybrid heuristic algorithm is proposed to solve this complex logistics challenge. In the first stage, a tree search algorithm based on residual space is developed to determine the optimal layout for the 3D loading of cargo onto pallets. In the second stage, a heuristic online truck–cargo matching algorithm is introduced to allocate loaded pallets to trucks while optimizing the number of trucks used and minimizing transportation costs. The algorithm operates within a rolling time horizon, allowing it to dynamically handle real-time order arrivals and time window constraints. Numerical experiments demonstrate that the proposed method achieves high pallet loading efficiency (close to 90%), near-optimal truck utilization (nearly 95%), and significant cost reductions, making it a practical solution for real-world LTL logistics operations. [ABSTRACT FROM AUTHOR]

In order to solve the problem of autonomous vehicles driving safely on the road while searching for optimal paths to avoid traffic congestion and obstruction, this paper establishes an optimal path planning model based on image feature extraction. The model consists of four parts: the selection of key turning points, interpolation between turning points, definition of the evaluation function, and global optimal path search. First, based on the map information provided by the network, an image feature detection algorithm was proposed, and the key turning points of the map route were selected and stored in an open table. Then, based on mechanical analysis theory, the cubic interpolation between two key turning points of the optimal path was defined. This allows the car to drive along a continuous curve with a certain arc radius, enabling it to drive smoothly, effectively avoid obstacles, and ensure safety. Then, an evaluation function is defined as needed, and different evaluation functions are specified for different road sections to avoid excessive local deviations in planning and to reduce the impact of known obstacles on the path. Finally, the cost of the search path is calculated based on the evaluation function. According to the principle of minimum cost, a globally optimal heuristic search algorithm is proposed to find the globally optimal path. Experimental results show that the proposed global optimal path planning algorithm has excellent performance, with an average accuracy of 96.71% and a search speed of 2.78 ms. The model not only provides accurate path planning and fast path selection capabilities but also has strong robustness against interference. [ABSTRACT FROM AUTHOR]

To address the mission planning challenge for agile satellites in dense point target observation, a clustering strategy based on an ant colony algorithm and a heuristic simulated genetic annealing optimization algorithm are proposed. First, the imaging observation process of agile satellites is analyzed, and an improved ant colony algorithm is employed to optimize the clustering of observation tasks, enabling the satellites to complete more observation tasks efficiently with a more stable attitude. Second, to solve for the optimal group target observation sequence and achieve higher total observation benefits, a task planning model based on multi-target observation benefits and attitude maneuver energy consumption is established, considering the visible time windows of targets and the time constraints between adjacent targets. To overcome the drawbacks of traditional simulated annealing and genetic algorithms, which are prone to local optimal solution and a slow convergence speed, a novel Simulated Genetic Annealing Algorithm is designed while optimizing the sum of target observation weights and yaw angles while also accounting for factors such as target visibility windows and satellite attitude transition times between targets. Ultimately, the feasibility and efficiency of the proposed algorithm are substantiated by comparing its performance against traditional heuristic optimization algorithms using a dataset comprising large-scale dense ground targets. [ABSTRACT FROM AUTHOR]

With the widespread implementation of artificial intelligence techniques and self-driving systems in smart cars, providing excellent coverage of wireless sensor networks is critical for stable and effective tasks. Coverage control is an essential task for the design of wireless sensor networks. However, considering the influence of network resources and coverage features, several normal optimization methods are hard to carry out, yet heuristic iterative algorithms could generate an estimated ideal feasible solution for this issue. We present an artificial bee colony algorithm based on random dual strategies, called RDABC. Specifically, RDABC modifies the optimization direction through alternating between dual search techniques with the goal to find further excellent feasible solution. At the same time, through incorporating cross-mutation strategy to improve variety, and increase the algorithm's optimization efficiency. According to simulation experiments, RDABC outperforms four well-known algorithms in terms of coverage optimization. As a whole, RDABC optimizes the location and deployment of wireless sensors, enhances the overall performance and stability of intelligent transportation systems, and simplifies vehicle monitoring and traffic sign tasks. [ABSTRACT FROM AUTHOR]

Origin–destination matrix (ODM) is a key element in transportation studies. The emergence of new ITS technologies like Automatic Vehicle Identification (AVI) sensors makes the ODM observation problem more interesting in recent decades. However, sensors are subject to failure in reality which highlights the sensor failure phenomenon as a significant issue in real-case problems. This study intends to include the sensor failure phenomenon in AVI Sensor Location Model (SLM) for reliable observation of ODM and route flows. While reliability and cost are usually two conflicting objectives, we try to answer the following question "Is it possible to improve reliability without increasing the cost and only by changing sensor deployment?". In addressing this study question, first, it is shown that the solution of recent AVI SLMs are not unique. Second, we show that the reliability level of multiple optimal solutions is not the same. Third, two Mixed Integer Linear Programming (MILP) AVI SLMs for reliable observation and parital observation of ODM/route flows are developed considering the sensor failure phenomenon. The models are formulated such that their solutions are selected from the set of multiple optimal solutions. Fourth, a linear surrogate term for reliability is introduced and mathematically proven to be included in the proposed models. Finally, the applicability of the proposed models is examined on several middle-scale networks and a real-size network. Furthermore, a heuristic algorithm is customized to solve the models for the real-size network. The results suggest that there might be alternative sensor deployment strategies with the same number of sensors as in the optimal solution but with higher level of reliability for ODM/route flows observation. [ABSTRACT FROM AUTHOR]

In the construction process of construction engineering, there exist a complex construction site layout and multiequipment collaborative work. If a reasonable construction plan cannot be made quickly and accurately, the investment in construction costs will be increased. Aiming at the construction material distribution, this study proposed a construction plan planning method under the multinode and multivehicle scene. Firstly, a mathematical model of the material distribution planning problem in the multinode multivehicle scene was established. The basic assumptions, objective functions, and constraints were proposed. The triangular scalar variables and 'unitization' processing methods were incorporated into the mathematical model. In order to solve the problem of material distribution planning, a heuristic neural network algorithm was designed. A 'gene-chromosome-individual' representation method and a 'two-step' calculation concept were defined. The specific implementation steps of the algorithm were given. Fully considering the type and quantity of vehicles and the length of the path, a method of establishing the mapping relationship between the construction scheme and the construction cost was proposed. The feasibility of the proposed theoretical method was verified by a cable truss structure model experiment. A calculation method with high fitting degree and fast running speed was established. According to the experiment model, the best construction scheme was formed. The results showed that the optimal construction scheme had a high vehicle loading rate and a clear distribution path, which effectively saved the construction cost under the premise of ensuring smooth construction. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms, especially evolutionary algorithms, have been frequently used to find near optimal solutions to combinatorial optimization problems. The evaluation of such algorithms is often conducted through comparisons with other algorithms on a set of benchmark problems. However, even if one algorithm is the best among all those compared, it still has difficulties in determining the true quality of the solutions found because the true optima are unknown, especially in dynamic environments. It would be desirable to evaluate algorithms not only relatively through comparisons with others, but also in absolute terms by estimating their quality compared to the true global optima. Unfortunately, true global optima are normally unknown or hard to find since the problems being addressed are NP-hard. In this paper, instead of using true global optima, lower bounds are derived to carry out an objective evaluation of the solution quality. In particular, the first approach capable of deriving a lower bound for dynamic capacitated arc routing problem (DCARP) instances is proposed, and two optimization algorithms for DCARP are evaluated based on such a lower bound approach. An effective graph pruning strategy is introduced to reduce the time complexity of our proposed approach. Our experiments demonstrate that our approach provides tight lower bounds for small DCARP instances. Two optimization algorithms are evaluated on a set of DCARP instances through the derived lower bounds in our experimental studies, and the results reveal that the algorithms still have room for improvement for large complex instances. [ABSTRACT FROM AUTHOR]

In response to the increasing demand for individualized products in the 'small-lot, multi-variety' market, there is an urgent need to enhance the efficiency and intelligence level of assembly lines. To address this, a novel solution method for the mixed-model parallel two-sided assembly lines balancing problem (MMPTSALBP) is proposed. The first step is to define the type and layout of the parallel two-sided assembly lines. Next, a mathematical model is constructed with the objective of minimizing the number of workstations required for the MMPTSALBP, proposing an improved ant colony algorithm that utilizes double string representation for the initial solution encoding method and a double pheromone matrix update to solve the model. Its efficiency is tested on benchmark datasets according to some performance measures and classifications. A study validates its effectiveness on 25 classical arithmetic cases by comparison with the solutions of an heuristic algorithm and an artificial fish swarm algorithm that had been reported to perform well. [ABSTRACT FROM AUTHOR]

To improve the operational efficiency of automated container terminals and the coordination between multiple operations, this article studies the integrated scheduling optimization problem of automated guided vehicles (AGVs) and yard cranes. The impact of charging constraints on AGV task allocation and scheduling is considered. With the goal of minimizing the maximum completion time of all tasks, a mixed integer programming model is proposed. A solution method based on a dynamic programming algorithm is designed, where a heuristic algorithm is used to assign tasks to the yard cranes, and the dynamic programming method is used to assign tasks to the AGVs based on the task assignment results of the yard cranes. Finally, the validity of the model and algorithm is tested by numerical experiments. Furthermore, the influence of the quantity of AGVs on the terminal operational efficiency and the impact of AGV charging strategies on AGV scheduling are analysed. [ABSTRACT FROM AUTHOR]

The vehicle routing problem is common for drone delivery in urban areas, and the routing strategies can impact both transport efficiency and community noise. In this article, we propose an optimization method for drone routing to reduce the en route noise impact during the delivery process. A hybrid cost function is adopted to evaluate the quality of the routing strategies, considering both the noise impact and path length. The en route noise is evaluated based on an efficient flight simulation and noise assessment platform, which enables accurate sound source modeling for a practical delivery drone and sound propagation computation in a realistic urban environment. A three-phase heuristic algorithm is employed to optimize the routing strategy. Large-scale simulations conducted in a representative metropolitan area demonstrate significant reductions in both instantaneous and accumulated noise levels. This article highlights the importance of incorporating noise optimization strategies in urban drone delivery systems to improve public acceptance and sustainable air transport. [ABSTRACT FROM AUTHOR]

With increasingly diversified consumer demands, mixed-model assembly lines have been increasingly adopted by manufacturing enterprises. In recent years, more and more manufacturers adopted material supermarkets to enable a flexible and reliable Just-in-Time part supply of their mixed-model assembly lines. However, it is still a crucial challenge to ensure the implementation of Just-in-time part supply, and few research studies on the problem. Therefore, this article proposes the problem of collaborative optimizing route planning and material distribution scheduling with just-in-time principle. A mixed integer linear programming model is established with the objective of minimizing the total costs. Moreover, a dynamic programming based heuristic algorithm is developed to deal with large-sized problems. Computational experiments on different scales are carried out to test this algorithm. The computational results reveal the feasibility and effectiveness of the proposed algorithm. And considering different capacity of tow trains, results show the change of capacity affects the total cost by affecting the number of paths divided, which provides guidance for manufacturing enterprises. [ABSTRACT FROM AUTHOR]

The exponential growth of IoT devices and the surge in the data volume, coupled with the rise of latency-sensitive applications, has led to a heightened interest in fog computing to meet user demands. In this context, the service provisioning problem consists of dynamically selecting desirable fog computing nodes and routing user traffic to these nodes. Given that the fog computing layer is composed of heterogeneous nodes, which vary in resource capacity, availability, and power sources, the service provisioning problem becomes challenging. Existing solutions, often using classical optimization approaches or heuristic algorithms due to the NP-hardness of the problem, have struggled to address the issue effectively, particularly in accounting for the heterogeneity of fog nodes and uncertainty of the ad hoc fog nodes. These techniques show exponential computation times and deal only with small network scales. To overcome these issues, we are motivated to replace these approaches with deep reinforcement learning (DRL) techniques, specifically employing the proximal policy optimization (PPO) algorithm to understand the dynamic behavior of the environment. The main objective of the proposed DRL-based dynamic service provisioning (DDSP) algorithm is minimizing service provisioning costs while considering service delay constraints, the uncertainty of ad hoc fog nodes, and the heterogeneity of both ad hoc and dedicated fog nodes. Extensive simulations demonstrate that our approach provides a near-optimal solution with high efficiency. Notably, our proposed algorithm selects more stable fog nodes for service provisioning and successfully minimizes cost even with uncertainty regarding ad hoc fog nodes, compared to heuristic algorithms. [ABSTRACT FROM AUTHOR]

A closed-loop supply chain model was proposed to optimize the assignment and position of production and distribution centers, product warehouses, retailers, retailer centers, collection, repair, probabilistic customers, and disposal centers. The goal is to minimize environmental pollution and CO2 emissions by considering CO2 to O2 conversion in vehicle gas converters. Two strategies are explored to determine the best retailer locations based on the predicted movement type (Euclidean Square, Euclidean, Chebyshev, and Rectangular) and expected coverage (time and distance). To compare and select the best strategy, a bi-objective nonlinear programming model was introduced. The model simultaneously examines plans 1 and 2 and chooses the superior plan. Given the strategy selected, a heuristic algorithm is employed to determine the best retailer allocation and locations. Given that the problem is NP-hard in nature, it was solved using a meta-heuristic, the non-dominated sorting genetic algorithm. Finally, to validate the effectiveness, a numerical example is presented and solved using optimization software. The algorithm's findings demonstrate a strong correlation with meta-heuristic algorithms, indicating it as a promising starting point that can be further enhanced by incorporating such methods. For instance, the optimized suggestion algorithm resulted in a reduction of 739 units in carbon dioxide emissions, while the genetic algorithm achieved a reduction of 703 units. Furthermore, the cost computed by the algorithm stands at 7,484,935 units, a figure close to the output of 7,030,846 units generated by the genetic algorithms. [ABSTRACT FROM AUTHOR]

Plant species recognition is a primordial task that forms the basis of solving several plant-related computer-vision problems such as disease detection or growth monitoring. However, a lack of voluminous datasets that cover specific needs is observed in the digital agriculture community. Therefore, the EAGL—I system was developed to rapidly create massive labeled datasets of plants intended to be commonly used by farmers and researchers to create AI-driven solutions in agriculture. As a result, a publicly available plant species recognition dataset composed of 40,000 images with different sizes consisting of 8 plant species was created with the system in order to demonstrate its capabilities. This paper proposes a novel method, called Variably Overlapping Time—Coherent Sliding Window (VOTCSW), that transforms a dataset composed of images with variable size to a 3D representation with a common fixed size that is suitable for convolutional neural networks, and demonstrates that this representation is more informative than resizing the images of the dataset to a given size. We theoretically formalized the use cases of the method as well as its inherent properties and we proved that it has an oversampling and a regularization effect on the data. By combining the VOTCSW method with the 3D extension of a recently proposed machine learning model called 1-Dimensional Polynomial Neural Networks, we were able to create a model that achieved a state-of-the-art accuracy of 99.9% on the dataset created by the EAGL-I system, surpassing well-known architectures such as ResNet and Inception. We also demonstrated its use in a plant semantic segmentation application. In addition, we created a heuristic algorithm that enables the degree reduction of any pre-trained N-Dimensional Polynomial Neural Network and which compresses it without altering its performance, thus making the model faster and lighter. Furthermore, we established that the currently available dataset could not be used for machine learning in its present form, due to a substantial class imbalance between the training set and the test set. Hence, we created a specific preprocessing and a model development framework that enabled us to improve the accuracy from 49.23% to 99.9%. [ABSTRACT FROM AUTHOR]

The rapid growth of the e-commerce sector, particularly in Latin America, has highlighted the need for more efficient automated packing and distribution systems. This study presents heuristic algorithms to solve the online three-dimensional knapsack problem (OSKP), incorporating buffering and repacking strategies to optimize space utilization in automated packing environments. These strategies enable the system to handle the stochastic nature of item arrivals and improve container utilization by temporarily storing boxes (buffering) and rearranging already packed boxes (repacking) to enhance packing efficiency. Computational experiments conducted on specialized datasets from the existing literature demonstrate that the proposed heuristics perform comparably to state-of-the-art methodologies. Moreover, physical experiments were conducted on a robotic packing cell to determine the time that buffering and repacking implicate. The contributions of this paper lie in the integration of buffering and repacking into the OSKP, the development of tailored heuristics, and the validation of these heuristics in both simulated and real-world environments. The findings indicate that including buffering and repacking strategies significantly improves space utilization in automated packing systems. However, they significantly increase the time spent packing. [ABSTRACT FROM AUTHOR]

The development of network measurement technologies has greatly increased the speed of network scans, but it also poses risks for the stability of the scanned networks. How to reduce probing traffic and enhance the effectiveness of probing has become a new research issue. In this paper, we utilize network measurement and machine learning techniques, leveraging public interfaces from network mapping platforms to construct a dataset with 44 feature dimensions. By combining the categorical boosting (CatBoost) model with the particle swarm optimization (PSO) algorithm for heuristic optimization, we propose a host port openness prediction model that integrates the PSO algorithm and the CatBoost model. Through comparisons with various machine learning models, the effectiveness of our proposed model was validated. Using this model in network scanning can save approximately 65% of bandwidth on average, effectively reducing the impact on the probed network. [ABSTRACT FROM AUTHOR]

Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains challenging are unfamiliar with the job market, the demand for the required skills, and being in the proper placement in a major is not straightforward. Thus, an automatic recommendation system can be helpful for students to assist and guide them in the right decision. In this context, we developed a machine learning model to predict and recommend suitable specializations for undergraduate students according to the job market and student's academic history. Two hundred twenty-five records of students are considered to establish this work. The proposed approach encompasses four major steps, including data preprocessing to clean, scale, and prepare the data for training to avoid obtaining suboptimal results, accompanied by an oversampling process to equal the samples' distribution to prevent the model from being biased or poorly generalized. Furthermore, we conducted a feature selection step using Sequential Backward Selection (SBS) to extract the relevant features to improve the outcomes and reduce the risk of noise. The selected subset is used to train the model using the AdaBoost classifier. We deployed the Genetic algorithm to optimize the classifier's hyperparameters to maximize results. As a result, the findings of this study exhibit noticeable results compared to existing models, with an accuracy of 98.1%. The proposed model can be reliable in guiding undergraduate students through proper decisions regarding selecting their major. [ABSTRACT FROM AUTHOR]

The Maximally Diverse Grouping Problem is one of the well-known combinatorial optimization problems with applications in the assignment of students to groups or courses. Due to its NP-hardness several (meta)heuristic solution approaches have been presented in the literature. Most of them include the insertion of an item of one group into another group and the swap of two items currently assigned to different groups as neighborhoods. The paper presents a new efficient implementation for both neighborhoods and compares it with the standard implementation, in which all inserts/swaps are evaluated, as well as the neighborhood decomposition approach. The results show that the newly presented approach is clearly superior for larger instances allowing for up to 160% more iterations in comparison to the standard implementation and up to 76% more iterations in comparison to the neighborhood decomposition approach. Moreover, the results can also be used for (meta)heuristic algorithms for other grouping or clustering problems. [ABSTRACT FROM AUTHOR]

As telehealth utilization for ambulatory and home-based care skyrockets, there has been a paradigm shift to a decentralized and hybrid care delivery modality integrating both in-person and telehealth services provided at different layers of the care delivery network, i.e., central hospitals, satellite clinics, and patient homes. The operations of such care delivery systems need to take into consideration patients' mobility and care needs, and rely on multiple types of nurses who can support and facilitate telehealth (with hospital physicians) in clinics and patient homes. We formulate an optimization problem, aiming at operationalizing the proposed care delivery network. Decisions regarding the type of care delivered, the location of care delivered, and the scheduling of all kinds of nurses are determined jointly to minimize operating costs while simultaneously satisfying patients' care needs. We propose a bi-level approximation that exploits the structure of the hybrid telehealth system, and develop column generation-based heuristic algorithms to identify the joint decision rules for clinic selection, patient assignment, and visiting nurse routing problems. Numerical experiment results demonstrate our algorithm's capability to achieve high-quality solutions in reasonable computation time, and is capable of solving instances with large patient sizes and time windows. Our work supports the efficient and effective operation of the proposed hybrid telehealth systems to improve patient access to care. [ABSTRACT FROM AUTHOR]

In this paper, we consider a project-based organization that deals with an assignment problem in which a set of projects must be assigned to a group of project managers. This assignment is done based on the relative contributions of projects to the organizational mission and a matching score between each pair of a project and a project manager. We assume that some projects are deterministic, and the organization has signed their corresponding contracts while others are stochastic, i.e. the organization has submitted bids for these projects and may or may not win them in the future. Furthermore, we consider a finite planning horizon and presume a predetermined start time for each deterministic and stochastic project. We develop two models including a multi-stage stochastic integer programming model and a stochastic dynamic programming model to solve the problem. The latter shows better performance for small-size and less complex instances whereas the former gives better performance for more complex instances. We also developed a heuristic algorithm to solve large-size and more complex instances. Computational results indicate that the developed heuristic algorithm can reach near-optimal solutions in reasonable CPU run times and dominates the two other solution approaches particularly for large-size instances. [ABSTRACT FROM AUTHOR]

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution's complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas. [ABSTRACT FROM AUTHOR]

Background/Objectives: Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. Methods: This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). Results: The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. Conclusions: This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Addressing the imbalance between exploration and exploitation, slow convergence, local optima Traps, and low convergence precision in the Northern Goshawk Optimizer (NGO): Introducing a Multi-Strategy Integrated Northern Goshawk Optimizer (MINGO). In response to challenges faced by the Northern Goshawk Optimizer (NGO), including issues like the imbalance between exploration and exploitation, slow convergence, susceptibility to local optima, and low convergence precision, this paper introduces an enhanced variant known as the Multi-Strategy Integrated Northern Goshawk Optimizer (MINGO). The algorithm tackles the balance between exploration and exploitation by improving exploration strategies and development approaches. It incorporates Levy flight strategies to preserve population diversity and enhance convergence precision. Additionally, to avoid getting trapped in local optima, the algorithm introduces Cauchy mutation strategies, improving its capability to escape local optima during the search process. Finally, individuals with poor fitness are eliminated using the crossover strategy of the Differential Evolution algorithm to enhance the overall population quality. To assess the performance of MINGO, this paper conducts an analysis from four perspectives: population diversity, balance between exploration and exploitation, convergence behavior, and various strategy variants. Furthermore, MINGO undergoes testing on the CEC-2017 and CEC-2022 benchmark problems. The test results, along with the Wilcoxon rank-sum test results, demonstrate that MINGO outperforms NGO and other advanced optimization algorithms in terms of overall performance. Finally, the applicability and superiority of MINGO are further validated on six real-world engineering problems and a 3D Trajectory planning for UAVs. [ABSTRACT FROM AUTHOR]

Hazardous materials pose significant dangers during transportation due to their flammable and explosive properties. The consequences of accidents involving such materials are often severe and irreparable. A well‐designed hazardous materials transportation network can mitigate these risks. However, designing such a network presents two major challenges: quantifying the risk associated with hazardous materials transportation and addressing the hierarchical relationship between government and companies. To address these challenges, we enhance the accuracy of accident probability estimates and the comprehensiveness of accident consequence assessments, incorporating the uncertainty of accident outcomes. We propose a comprehensive risk assessment model and develop a bilevel programming model to reflect the hierarchical relationship. In this model, the government at the upper level aims to minimize the total risk, while companies at the lower level seek to minimize their total costs. The model is transformed using chance‐constrained programming and solved using heuristic algorithms. We apply the model to the highway network in Anhui province, China, to verify its validity. The results demonstrate that the model effectively manages the hierarchical relationship between government and companies, reduces the risk of hazardous materials transportation, and enhances the stability and safety of the transportation network. [ABSTRACT FROM AUTHOR]

We present a collection of simulations of the Edwards-Anderson lattice spin glass at T = 0 to elucidate the nature of low-energy excitations over a range of dimensions that reach from physically realizable systems to the mean-field limit. Using heuristic methods, we sample the ground states of instances to determine their energies while eliciting excitations through manipulating boundary conditions. We exploit the universality of the phase diagram of bond-diluted lattices to make such a study in higher dimensions computationally feasible. As a result, we obtain a variety of accurate exponents for domain wall stiffness and finite-size corrections, which allow us to examine their dimensional behavior and their connection with predictions from mean-field theory. We also provide an experimentally testable prediction for the thermal-to-percolative crossover exponent in dilute lattice Ising spin glasses. [ABSTRACT FROM AUTHOR]

This study addresses the need for enhanced algorithms learning through the utilization of comparative simulation and visualization. Motivated by the challenge of comprehending complex algorithms, we emphasize the efficacy of visualization tools. Our research explores the realtime rendering of algorithms in a visual format, facilitating a deeper understanding of their underlying mechanisms. In addition, we introduce a novel comparative simulation feature within our Algorithm Visualizer e-learning application, enabling learners to contrast the performance of diverse algorithms, discern their strengths and weaknesses, and evaluate their applicability to different data sets. Specifically, this application accommodates various algorithms, including Dijkstra's algorithm, DFS, BFS, Binary Search, and more. Learners can employ this tool to scrutinize algorithmic differences and efficiency, exemplified through scenarios like comparing Dijkstra's algorithm and A* algorithm for pathfinding on a map. Furthermore, this feature extends to the evaluation of sorting algorithms, such as Quick Sort and Merge Sort, allowing users to visualize their performance on large data sets. In conclusion, the Algorithm Visualizer e-learning application serves as a valuable resource for learners, enhancing algorithmic comprehension through comparative simulation and visualization techniques. [ABSTRACT FROM AUTHOR]

This article proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short‐term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this article, the model training is formulated as a bi‐level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower‐level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full connected Neural Network (FNN) to generate interval boundary. In upper‐level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper‐parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state‐of‐the‐art algorithms, achieving a 15% reduction in prediction error and a 20% decrease in computational time. [ABSTRACT FROM AUTHOR]