• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.243-251
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• 2013

Cash management is a major concern for all contractors in the construction industry. It is arguable that cash is the most critical resource of all. A contractor needs to secure sufficient funds to navigate the project to the end, while keeping an eye on maximizing profits along the way. Past research attempted to address such topic via developing models to tackle the time-cost tradeoff problem, cash flow forecasting, and cash flow management. Yet, little was done to integrate the three aspects of cash management together. This paper, as such, presents a comprehensive model that integrates the time-cost tradeoff problem, cash flow management, and cash flow forecasting. First, the model determines the project optimal completion time by considering the different alternative construction methods available for executing project activities. Second, it investigates different funding alternatives and proposes a project-level cash management plan. Two funding alternatives are considered; they are borrowing and company own financing. The model was built as a combinatorial optimization model that utilizes ant colony search capabilities. The model also utilizes Microsoft Project software and spreadsheets to maintain an environment that incorporates activities, their durations, and other project data, in order to estimate project completion time and cost. Ant Colony Optimization algorithm was coded as a Macro program using VBA. Finally, an example project was used to test the developed model, where it acted reliably in maximizing the contractor's profit in the test project.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.2
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• pp.193-200
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• 2002

Simple spring-damper-mass models have been widely used to investigate whole-body vortical biodynamic response characteristics of the seated vehicle driver. Most previous models have not considered the effect of wobbling masses; i.e. heart, lungs, liver, intestine, etc. In this study, 4 -DOF seated driver model including one non-rigid mass representing wobbling visceral mass, 5-DOF model including intestine, and 10-DOF model including five lumbar vertebral masses were proposed. The model parameters were identified by a combinatorial optimization technique. simulated annealing method. The objective function was chosen as the sum of error between model response of seat-to-head transmissibility and driving point mechanical impedance and those of experimental data for subjects seated erect without backrest support. The model response showed a good agreement with the experimental response characteristics. Using a 10-DOF model, calculated resonance frequency of lumbar spine at 4Hz was matched well with experimental results of Panjabi et al.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.471-477
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• 2005

This paper proposes an efficient constraint-based optimization model for the design of 3G mobile networks, such as universal mobile telecommunications system (UMTS). The model concerns about finding a set of sites for locating radio network controllers (RNCs) from a set of pre-defined candidate sites, and at the same time optimally assigning node Bs to the selected RNCs. All these choices must satisfy a set of constraints and optimize an objective function. This problem is NP-hard and consequently cannot be practically solved by exact methods for real size networks. Thus, this paper proposes a hybrid search strategy for tackling this complex and combinatorial optimization problem. The proposed hybrid search strategy is composed of three phases: A constraint satisfaction method with an embedded problem-specific goal which guides the search for a good initial solution, an optimization phase using local search algorithms, such as tabu algorithm, and a post­optimization phase to improve solutions from the second phase by using a constraint optimization procedure. Computational results show that the proposed search strategy and the model are highly efficient. Optimal solutions are always obtained for small or medium sized problems. For large sized problems, the final results are on average within $5.77\%$ to $7.48\%$ of the lower bounds.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.196-197
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• 2015

In tall building construction, time for transporting form affects formwork duration as plan size has become larger and quantity of inputted form has been increased. Thus, necessity of systematic dismantlement sequence of form has been increased to reduce the duration of formwork. Tabu search has been efficiently applied to solve problem of combinatorial optimization by using tabu list which can improve combination values. Therefore, this study proposes optimization model of dismantlement sequence of table form which has been preferred in tall building construction, to reduce the formwork duration by minimizing time for transporting form.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.337-345
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• 2009

Deregulation in power industry has made the reactive power ancillary service management a critical task to power system operators from both technical and economic perspectives. Reactive power management in power systems is a complex combinatorial optimization problem involving nonlinear functions with multiple local minima and nonlinear constraints. This paper proposes a practical market-based reactive power ancillary service management scheme to tackle the challenge. In this paper a new model for voltage security and reactive power management is presented. The proposed model minimizes reactive support cost as an economic aspect and insures the voltage security as a technical constraint. For modeling validation study, two optimization algorithm, a genetic algorithm (GA) and particle swarm optimization (PSO) method are used to solve the problem of optimum allocation of reactive power in power systems under open market environment and the results are compared. As a case study, the IEEE-30 bus power system is used. Results show that the algorithm is well competent for optimal allocation of reactive power under practical constraints and price based conditions.

• Management Science and Financial Engineering
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• v.18 no.2
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• pp.13-17
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• 2012

Set covering has widely been accepted as a staple tool for feature selection in data mining. We present a generalized version of this classical combinatorial optimization model to make it better suited for the purpose and propose a surrogate relaxation-based procedure for its meta-heuristic solution. Mathematically and also numerically with experiments on 25 set covering instances, we demonstrate the utility of the proposed model and the proposed solution method.

• Journal of Korean Association for Spatial Structures
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• v.1 no.2 s.2
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• pp.59-66
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• 2001

This paper describes three modules for development of the Space Frame Integrated Design System(SFIDS). The Control Module is implemented to control the developed system. The Model Generation Module based on PATRAN user interface enables users to generate a complicated finite element model for space frame structures. The Optimum Design Module base on a branch of combinatorial optimization techniques which can realize the optimization of a structure having a large number of members designs optimum members of a space frame after evaluating analysis results. The Control Module and the Model Generation Module Is implemented by PATRAN Command Language(PCL) while C++ language is used in the Optimum Design Module. The core of the system is PATRAN database, in which the Model Generation Module creates information of a finite element model. Then, PATRAN creates Input files needed for the analysis program from the information of the finite element model in the database, and in turn, imports output results of analysis program to the database. Finally, the Optimum Design Module processes member grouping of a space frame based on the output results, and performs optimal member selection of a space frame. This process is repeated until the desired optimum structural members are obtained.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.8
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• pp.122-129
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• 1997

In this paper, we propose a Hopfield model for solving the part-matching in case that is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and total path of part-connections. Therefore, this kind of problem is referred to as a combinatiorial optimization problem. First of all, we review the theoretical basis for Hopfield model and present two optimal algorithms of part-matching. The first algorithm is Traveling Salesman Problem(TSP) which improved the original and the second algorithm is Wdighted Matching Problem (WMP). Finally, we show demonstration through com- puter simulation and analyze the stability and feasibility of the generated solutions for the proposed con- nection methods. Therefore, we prove that the second algorithm is better than the first algorithm.