• Structural Monitoring and Maintenance
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• v.4 no.1
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• pp.85-105
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• 2017

Monitoring is the most important part of the construction and operation of the embankment dams. Applied instruments in these dams should be determined based on dam requirements and specifications. Instruments selection considered as one of the most important steps of monitoring plan. Competent instruments selection for dams is very important, as inappropriate selection causes irreparable loss in critical condition. Lack of a systematic method for determining instruments has been considered as a problem for creating an efficient selection. Nowadays, decision making methods have been used widely in different sciences for optimal determination and selection. In this study, the Multi-Attribute Decision Making is applied by considering 9 criteria and categorisation of 8 groups of geotechnical instruments. Therefore, the Analytic Hierarchy Process and Multi-Criteria Optimisation and Compromise Solution methods are employed in order to determine the attributes' importance weights and to prioritise of instruments for embankment dams, respectively. This framework was applied for a rock fill with clay core dam. The results indicated that group decision making optimizes the selection and prioritisation of monitoring instruments for embankment dams, and selected instruments are reliable based on the dam specifications.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.67-78
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• 1992

MODM (multiobjective decision-making) problem is very complex system for the analyst. The problem is more complex if the goals of each of the objective functions are expressed imprecisely. It requires suitable MODM method to deal with imprecisions. Therefore, we present a new interactive fuzzy decision making method for solving multiobjective nonlinear programming problems by assuming that the decision maker (DM) has imprecise goals that assume fuzzy linguistic variable for each of the objective functions. The imprecise goals of the DM are quantified by eliciting corresponding membership functions through the interactive with the DM out of six membership functions. After determining membership functions, in order to generate the compromise or satisficing solution which is .lambda.-pareto optimal, .lambda.-max problem is solved. The higher degree of membership is chosen to satisfy imprecise goals of all objective functions by combining the membership functions. Then, the values are the compromise or satisficing solution. On the basis of the proposed method, and interactive computer programming is written to implement man-machine interactive procedures. Our programming is a revised version of sequential unconstrained minimization technique. Finally, a numerical example illustrates various aspects of the results developed in this paper.

• Management Science and Financial Engineering
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• v.13 no.2
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• pp.29-46
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• 2007

This paper makes an attempt to compare the two important methods for finding solutions of multi-item inventory problem with more than one conflicting objectives. Panda et al.[9] discusses a distance-based method to find the best possible compromise solution with variation of priority under the given weight structure. In this paper, the problem in [9] is revisited through the Pareto-optimal front of genetic algorithm with the help of a situation of retail stocking of FMCG business. The advantages of using the solutions from the perspective of the decision maker obtained through multi-objective optimization are highlighted in terms of population search, weighted goals and priority structure, cost, set of compromise solutions along with prevention of stock-out situation.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1279-1281
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• 1999

The learning automaton is an automaton to update systematically the strategy for enhancing the performance in response to the output results, and several schemes of learning automata have been presented. In this paper, S-model learning automata are applied to achieving a best compromise solution between an optimal solution for economic operation and an optimal solution for stable operation of the power system under the circumstance that the loads vary randomly. It is shown that learning automata are applied satisfactorily to the multiobjective optimization problem for obtaining the best tradeoff among the conflicting economy and stability objectives of power systems.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.4
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• pp.185-194
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• 2000

The learning automaton is an automaton to update systematically the strategy for enhancing the performance in response to the output results, and several schemes of learning automata have been presented. In this paper, S-model learning automata are applied in order to achieve the best compromise solution between an optimal solution for economic operation and an optimal solution for stable operation of the power system under the circumstance that the loads vary randomly. It is shown that learning automata are applied satisfactorily to the multiobjective optimization problem for obtaining the best tradeoff among the conflicting economy and stability objectives of power systems.

• Journal of the Korean Society of Safety
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• v.13 no.4
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• pp.240-247
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• 1998

This paper proposed a method for solving the nonlinear integer programing problem to get easily the best compromise solution while holding a nonlinear property by using the genetic algorithms. Also, this paper reported that the optimization problem of systems reliability as was solved by using the preposed method, and the numerical comparison experiments between the 0-1 LP/0-1 NP formulations were demonstrated, and from the quantitative evaluation the efficiency of the proposed method was demonstrated.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.61-68
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• 2007

There has been a lot of research on and release of commercial systems that enable evaluation and visualization of construction methods. These have enabled the selection of good construction plans. However, the process in which engineers build 3D geometry, formulate a schedule and eventually synchronize them is still a time-consuming process. Changing any aspect of the geometry or the schedule and re-linking them is also time-consuming. Therefore, the engineers may compromise on getting the best solution. This paper describes a technique to automate the generation of multiple sets of schedules, quantity takeoffs and 4D visualization from a single 3D model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.4 s.175
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• pp.963-971
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• 2000

Gerotor is a planar mechanism consisting of a rotor and lobes which form a closed space, namely a chamber. As active contact points between a rotor and lobes are subjected to very high contact stresses, wear in one or both of the rotor and lobe cannot be avoided. Therefore, in the design of Gerotor used in hydraulic motors a compromise between high torque output and contact stress is of great importance and a thorough analysis of design parameters should be conducted to achieve this compromise. In this study, a contact point is modelled as a linear spring in consideration of equivalent curvature to analyze the contact stress. As the contact stress calculation in this problem is a statically indeterminate type, a numerical iterative scheme has been adopted to obtain the solution. To fully understand the influence of design parameters on the contact stress, the relationship between pressure force, equivalent curvature, contact force and contact stress are analyzed. It is shown that the equivalent curvature of the contact point is a dominant factor that affects the maximum contact stress.

• Journal of Korean Society of Forest Science
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• v.83 no.3
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• pp.271-279
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• 1994

This paper descbibes a fuzzy multiobjective linear programming, which is a relatively new approach in forestry in solving forest management problems. At first, the fuzzy set theory is explained briefly and the fuzzy linear programming(FLP) and the fuzzy multiobjective linear programming(FMLP) are introduced conceptionally. With the information obtained from the study area in Thailand, a standard linear programming problem is formulated, and optimal solutions (present net worth) are calculated for four groups of timber price by this LP model, respectively. This LP model is reformulated to a fuzzy multiobjective linear programming model to accommodate uncertain timber values and with this FMLP model a compromise solution is attained. Optimal solutions of four objective functions for four timber price groups and the compromise solution are compared and discussed.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.1-9
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• 2014

In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.