• Management Science and Financial Engineering
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• v.13 no.2
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• pp.1-27
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• 2007

This paper presents a fuzzy-goal programming(FGP) approach for Bi-Level Linear Multiple Objective Decision Making(BLL-MODM) problem in a large hierarchical decision making and planning organization. The proposed approach combines the attractive features of both fuzzy set theory and goal programming(GP) for MODM problem. The GP problem has been developed by fixing the weights and aspiration levels for generating pareto-optimal(satisfactory) solution at each level for BLL-MODM problem. The higher level decision maker(HLDM) provides the preferred values of decision vector under his control and bounds of his objective function to direct the lower level decision maker(LLDM) to search for his solution in the right direction. Illustrative numerical example is provided to demonstrate the proposed approach.

• Journal of Korea Water Resources Association
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• v.45 no.1
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• pp.15-27
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• 2012

SWMM (Storm Water Management Model) has been widely used in the world as a typical model for flood runoff analysis of urban areas. However, the calibration of the model is difficult, which is an obstacle to easy application. The purpose of the study is to develop an automatic calibration module of the SWMM linked with SCE-UA (Shuffled Complex Evolution-University of Arizona) algorithm. Generally, various objective functions may produce different optimization results for an optimization problem. Thus, five single objective functions were applied and the most appropriate one was selected. In addition to the objective function, another objective function was used to reduce peak flow error in flood simulation. They form a multiple objective function, and the optimization problem was solved by determination of Pareto optima. The automatic calibration module was applied to the flood simulation on the catchment of the Guro 1 detention reservoir and pump station. The automatic calibration results by the multiple objective function were more excellent than the results by the single objective function for model assessment criteria including error of peak flow and ratio of volume between observed and calculated flow. Also, the verification results of the model calibrated by the multiple objective function were reliable. The program could be used in various flood runoff analysis in urban areas.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Journal of the military operations research society of Korea
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• v.13 no.1
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• pp.116-124
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• 1987

This paper studies to develop the procedure which is combined by the progressive goals and progressive weights generation method. This procedure minimizes the number of questions the decision maker has to make, and also satisfies the generated minimum goal of each objective function. With the procedure developed, we are able to improve the previous multiple objective linear programming techniques in two points.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.825-835
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• 2017

Significant improvements to methodologies on structural damage detection (SDD) have emerged in recent years. However, many methods are related to inversion computation which is prone to be ill-posed or ill-conditioning, leading to low-computing efficiency or inaccurate results. To explore a more accurate solution with satisfactory efficiency, a PSO-INM algorithm, combining particle swarm optimization (PSO) algorithm and an improved Nelder-Mead method (INM), is proposed to solve multi-sample objective function defined based on Bayesian inference in this study. The PSO-based algorithm, as a heuristic algorithm, is reliable to explore solution to SDD problem converted into a constrained optimization problem in mathematics. And the multi-sample objective function provides a stable pattern under different level of noise. Advantages of multi-sample objective function and its superior over traditional objective function are studied. Numerical simulation results of a two-storey frame structure show that the proposed method is sensitive to multi-damage cases. For further confirming accuracy of the proposed method, the ASCE 4-storey benchmark frame structure subjected to single and multiple damage cases is employed. Different kinds of modal identification methods are utilized to extract structural modal data from noise-contaminating acceleration responses. The illustrated results show that the proposed method is efficient to exact locations and extents of induced damages in structures.

• Journal of Technology Innovation
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• v.7 no.2
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• pp.21-36
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• 1999

This paper presents a R&D investment model for Information and telecommunications (I&T) technology, which can be used by group decision makers, using multiple objective linear programming (MOLP). The MOLP model involves the simultaneous maximization of three linear objective functions associated with three criteria, which are social, technological, and economic criterion. This model is different from the traditional one which only involves the maximization of economic criterion. The presented problem in this model can be formulated as a problem of optimizing a linear function over an efficient set of MOLP. Its application to the National R&D Project in I&T Industry is also presented. In this application, the Analytic Hierarchy Process (AHP) is proposed to estimate the weights, which are used as the coefficients in each objective function of the MOLP model and in a linear decision function. By solving this problem, it yields a suitable R&D investment ratio to each technology field. It is showed that the MOLP model can be useful decision aid in formulating R&D investment plan in I&T industry which needs to be decided by group decision makers, not by an individual. It is expected that the MOLP model works as the basis for planning R&D investment strategy in I&T industry.

• Transactions of Materials Processing
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• v.9 no.5
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• pp.512-525
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• 2000

Designers are frequently faced with multiple quality issues in injection molded parts. These issues are usually In conflict with each other, and thus tradeoff needs to be made to reach a final compromised solutions. The objective of this study is to develop an automated injection molding design methodology, whereby part defects such as warpage and weld line are optimized. The features of the proposed methodology are as follows: first, Utility Function approach is applied to transform the original multiple objective problem into single objective problem. Second is an implementation of a direct search-based Injection molding optimization procedure with automated consideration of process variation. The Space Reduction Method based on Taguchi's DOE(Design Of Experiment) is used as a general optimization tool in this study. The computational experimental verification of the methodology was partially carried out for a can model of Cavallero Plastics Incorporation, U. S. A. Applied to production, this study will be of immense value to companies in reducing the product development time and enhancing the product quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.11
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• pp.938-943
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• 2013

In this paper, we propose a transmission parameter optimization scheme based on genetic algorithm for dynamic spectrum access systems. Specifically, we represent a multiple objective fitness function as a weighted sum of single objective fitness functions to optimize transmission parameters, and then, obtain optimized transmission parameters based on genetic algorithm for given transmission scenarios. From numerical results, we confirm that the transmission parameters are well optimized by using the proposed optimization scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.141-150
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• 1998

This paper presents a dynamical anti-reset windup (ARW) compensation method for saturating control systems with multiple controllers and/or multiloop configuration. By regarding the difference of controller states in the absence and presence of saturating actuators as an objective function, the dynamical compensator which minimizes the objective function is derived in an integrated fashion. The proposed dynamical compensator is a closed form of plant and controller parameters. The resulting dynamics of compensated controller reflects the linear closed-loop system. The proposed method guarantees total stability of the resulting system. The effectiveness of the proposed method is illustrated by applying it to a servo motor control system. The paper is an extension of the results in Park and Choi[1].

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.95-102
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• 2013

It is difficult to determine an appropriate discount price for daily perishable products to increase profit from a long-term standpoint. Even if the discount pricing is efficient to increase profit of the day, consumers memorize the sales price and they might hesitate to purchase the product at a regular price the following day. The authors discussed the inventory clearance pricing for a single period in our previous study by constructing a mathematical model to derive an optimal sales price to maximize the expected profit by considering the reference price effect of demand. This paper extends the discussion to handle the discount pricing for multiple periods. A mathematical analysis is first conducted to reveal the properties on an objective function, which is the present value of total expected profits for multiple periods. An algorithm is then proposed to derive an optimal price for asymmetric consumers. Numerical experiments investigate the characteristics of the objective function and optimal pricings.