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101.
102.
Optimising decisions around the location and operation of tower cranes can improve the workflow in construction projects. Traditionally, the location and allocation problems involved in tower crane operations in the literature have been solved separately from the assignment of material supply points to demand points and the scheduling of the crane’s activity sequence across supply and demand points on a construction site. To address the gap, this paper proposes a binary integer programming problem, where location of the tower crane, allocation of supply points to material-demanding regions, and routing of hook of the crane based on activity sequencing of the hook across supply and material-demanding regions on site are optimised. The novelty in this work is in the way the crane’s activity scheduling is modelled via mathematical programming, based on routing the hook movement to meet material demand, through minimising tower crane operating costs. A realistic case study is solved to assess the validity of the model. The model is contrasted with results obtained from other solving algorithms commonly adopted in the literature, along with a solution proposed by an experienced practitioner. Results indicate that all instances can be solved when compared to other meta-heuristics that fail to achieve an optimum solution. Compared to the solution proposed by the practitioner, the results of the proposed model achieve a 46% improvement in objective function value. Planners should optimise decisions related to the location of the crane, the crane’s hook movement to meet service requests, and supply points’ locations and assignment to material-demanding regions simultaneously for effective crane operations. 相似文献
103.
This paper reports the use of a novel ultra-high speed scheme to release the distance relay to operate for a fault during a power swing in the series compensated line. In the scheme, in order to extract the fault induced voltage and current components, voltage and current samples are analyzed by the multi-resolution morphological gradient (MMG), first. Then, the fault initiated forward travelling wave is computed at the distance relay point. Next, Likelihood ratio [LR] test is utilized to detect a jump in the statistical mean of the calculated forward travelling wave. Finally, a support vector machine (SVM) classifier is employed to distinguish faults from other normal capacitor and switching transients. It is shown that in all of the simulated cases, our ultra-high speed algorithm was successful in fault detection across a wide range conditions including, fault type, fault resistance, fault location, pre-fault loading and fault inception time. Moreover, we found that using the proposed scheme significantly speeded the fault detection, in comparison with the existing phasor based methods. In addition, the improvements noted in our algorithm are achieved with a low computational burden. 相似文献
104.
Dealing with multi-objective combinatorial optimization, this article proposes a new multi-objective set-based meta-heuristic named Perturbed Decomposition Algorithm (PDA). Combining ideas from decomposition methods, local search and data perturbation, PDA provides a 2-phase modular framework for finding an approximation of the Pareto front. The first phase decomposes the search into a number of linearly aggregated problems of the original multi-objective problem. The second phase conducts an iterative process: aggregated problems are first perturbed then selected and optimized by an efficient single-objective local search solver. Resulting solutions will serve as a starting point of a multi-objective local search procedure, called Pareto Local Search. After presenting a literature review of meta-heuristics on the multi-objective symmetric Traveling Salesman Problem (TSP), we conduct experiments on several instances of the bi-objective and tri-objective TSP. The experiments show that our proposed algorithm outperforms the best current methods on this problem. 相似文献
105.
The single-robot search problem in an unknown environment is defined as the problem of finding a stationary object in the environment whose map is not known a priori. Compared to exploration, the only difference lies in goal selection as the objectives of search and exploration are dissimilar, i.e. a trajectory that is optimal in exploration does not necessarily minimize the expected value of the time to find an object along it. For this reason, in this paper we extend the preliminary ideas presented in Kulich et al. [1] to a general framework that accounts for the particular characteristics of the search problem. Within this framework, an important decision involved in the determination of the trajectory can be formulated as an instance of the Graph Search Problem (GSP), a generalization of the well-known Traveling Deliveryman Problem (TDP) which has not received much attention in the literature. We developed a tailored Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for the GSP, which generates good quality solutions in very short computing times and is incorporated in the overall framework. The proposed approach produces very good results in a simulation environment, showing that it is feasible from a computational standpoint and the proposed strategy outperforms the standard approaches. 相似文献
106.
We present CGO-AS, a generalized ant system (AS) implemented in the framework of cooperative group optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. Ant colony is a simple yet efficient natural system for understanding the effects of primary intelligence on optimization. However, existing AS algorithms are mostly focusing on their capability of using social heuristic cues while ignoring their individual learning. CGO can integrate the advantages of a cooperative group and a low-level algorithm portfolio design, and the agents of CGO can explore both individual and social search. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion. The presented CGO-AS is therefore especially useful in exposing the power of the mixed individual and social learning for improving optimization. The optimization performance is tested with instances of the traveling salesman problem (TSP). The results prove that a cooperative ant group using both individual and social learning obtains a better performance than the systems solely using either individual or social learning. The best performance is achieved under the condition when agents use individual memory as their primary information source, and simultaneously use social memory as their searching guidance. In comparison with existing AS systems, CGO-AS retains a faster learning speed toward those higher-quality solutions, especially in the later learning cycles. The leverage in optimization by CGO-AS is highly possible due to its inherent feature of adaptively maintaining the population diversity in the individual memory of agents, and of accelerating the learning process with accumulated knowledge in the social memory. 相似文献
107.
In this paper, nonlinear optimal control schemes are proposed to reduce transferring time, energy consumption, and residual vibration for the payload’s skew rotation process of crane systems. The main contribution of this paper is to reduce the energy consumption without trading-off the sub-optimal transferring time. The novel idea is to use an electro-mechanical clutch to intelligently disengage the connection between the motor and the payload during the motion such that the payload can continue rotating only by its own momentum. As a result, a switched optimal control problem must be realized. Two solutions, namely particular and general schemes are proposed in the paper, where physical constraints of the actuator including bounded velocity and bounded acceleration are explicitly taken into account. Both simulation and experimental results are provided to prove the effectiveness of the proposed optimal control systems. The established schemes can be directly applied to transfer the payload to a desirable skew orientation without any residual oscillation, or can be utilized as a sub-optimal-time reference trajectory planner of the skewing control module in either overhead or rotary crane systems. Furthermore, the hybrid rotation process presented in this paper, which is driven by the engaging/disengaging event of the clutch, can be served as a theoretical benchmark for any newly established switched optimal control method. 相似文献
108.
Differential evolution is primarily designed and used to solve continuous optimization problems. Therefore, it has not been widely considered as applicable for real-world problems that are characterized by permutation-based combinatorial domains. Many algorithms for solving discrete problems using differential evolution have been proposed, some of which have achieved promising results. However, to enhance their performance, they require improvements in many aspects, such as their convergence speeds, computational times and capabilities to solve large discrete problems. In this paper, we present a new mapping method that may be used with differential evolution to solve combinatorial optimization problems. This paper focuses specifically on the mapping component and its effect on the performance of differential evolution. Our method maps continuous variables to discrete ones, while at the same time, it directs the discrete solutions produced towards optimality, by using the best solution in each generation as a guide. To judge its performance, its solutions for instances of well-known discrete problems, namely: 0/1 knapsack, traveling salesman and traveling thief problems, are compared with those obtained by 8 other state-of-the-art mapping techniques. To do this, all mapping techniques are used with the same differential evolution settings. The results demonstrated that our technique significantly outperforms the other mapping methods in terms of the average error from the best-known solution for the traveling salesman problems, and achieves promising results for both the 0/1 knapsack and the traveling thief problems. 相似文献
109.
110.
在对船用起重机吊臂的结构分析的基础上,对船用起重机吊臂参数化设计方法进行了研究,利用ANSYS参数化设计语言APDL对某一船用起重机吊臂进行了参数化设计与分析,提出了起重机吊臂参数化建模分析方法,并给出了应用实例,实现了起重机系列化设计中吊臂系列数值模拟的参数化,缩短了产品开发周期。 相似文献