Reconfigurable intelligent surfaces (RISs) are a promising technology to enable smart radio environments. However, integrating RISs into wireless networks also leads to substantial complexity for network management. T...
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Reconfigurable intelligent surfaces (RISs) are a promising technology to enable smart radio environments. However, integrating RISs into wireless networks also leads to substantial complexity for network management. This work investigates heuristic algorithms and applications to optimize RIS-aided wireless networks, including greedy algorithms, meta-heuristic algorithms, and matching theory. Moreover, we combine heuristic algorithms with machine learning (ML), and propose three heuristic-aided ML algorithms: heuristic deep reinforcement learning (DRL), heuristic-aided supervised learning, and heuristic hierarchical learning. Finally, a case study shows that heuristic DRL can achieve higher data rates and faster convergence than conventional deep Q-networks (DQNs). This work provides a new perspective for optimizing RIS-aided wireless networks by taking advantage of heuristic algorithms and ML.
Periodicity of elements is the basis of teaching and understanding inorganic chemistry. This review exemplifies simple rules and counting procedures as heuristic algorithms yielding often-dimensionless quantities that...
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Periodicity of elements is the basis of teaching and understanding inorganic chemistry. This review exemplifies simple rules and counting procedures as heuristic algorithms yielding often-dimensionless quantities that, as such or together with auxiliary parameters, allow us to predict not only the stoichiometry and bonding of compounds, but also some of their properties or reactions.
The design of intermodal hub networks is of paramount importance in logistic operations involving multiple transportation modes like trains and trucks. In this work, we consider two non-linear optimization models for ...
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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 thro...
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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.
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 a...
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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.
With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intellig...
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With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of 586.7 -1089ms over the benchmark algorithm, saving 18.3 -39% in energy consumption. The total latency of IMOA is reduced by 59.8 -68.5%, and the maximum latency is reduced by 48.8 -69.2%. The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption.
In a competitive market, technological advancements opens many opportunities for retailing businesses to increase their profitability through innovative strategies, including discount offers, pre-order programs, and o...
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In a competitive market, technological advancements opens many opportunities for retailing businesses to increase their profitability through innovative strategies, including discount offers, pre-order programs, and online payment services. However, this surge in competition has led to challenges in optimizing inventory decisions. This study addresses these multifaceted challenges by focusing on key decision variables such as selling price, replenishment schedules, and costs associated with advance sales strategies. The present study bifurcates the inventory replenishment cycle into two distinct sales phases: the two-phase for the advance sales period and the one-phase for the spot sale period. This segmentation enables a more refined examination of sale strategies and customer behaviours, encompassing the flexibility for customers to cancel orders and return products during the return period. We also have incorporated innovative strategies like discount offers, pre-order programs, and online payment services to enhance sales. One of the core contributions of this research lies in modelling demand in a fuzzy-stochastic environment, acknowledging the inherent uncertainties in customer demand influenced by factors such as selling price and advertisement costs. Through theoretical analysis, the proposed study formulates theorems that aid in developing objective functions in a fuzzy stochastic environment. The corresponding profit maximization problem is derived in this environment and employs the weighted sum method to defuzzify the maximization problem. Beyond profit maximization, this research delves into environmental considerations, assessing the impact of carbon emissions and associated taxes on inventory decisions. Subsequently, we solve it by applying three distinct optimization methods: Genetic Algorithm (GA), Particle Swarm Optimization (PSO-CO), and the Analytical Method (AM). Then, the proposed model is justified and validated through numerical examples invo
Since upgrading the single-band EON (SB-EON) to multi-band (MB-EON) is a staged and lengthy process, fibers with SB and MB transmission capacities may coexist for an extend period. This type of network is referred to ...
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This paper proposes an optimal allocation method of college physical education teaching resources based on heuristic algorithm. Using Apriori algorithm to cluster the teaching resources and realize the data mining of ...
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A novel energy-efficient clustering-based congestion-awareness routing mechanism has been developed for wireless sensor network (WSN). In the first stage, some set of sensor nodes are initialised in the WSN environmen...
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