• Industrial Engineering and Management Systems
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• v.12 no.4
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• pp.351-358
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• 2013

This paper proposes a multiobjective model describing the trade-offs involved in selecting the locations of drop-off boxes for collecting used products and transporting these products to designated locations. We assume the following reverse flow of used products. Owners of used products (cellular phones, digital cameras, ink cartridges, etc.) take them to the nearest drop-off box when the distance is reasonably short. We also assume that owners living closer to drop-off boxes dispose of more used products than do owners living farther from drop-off boxes. Different types of used products are collected, with each type requiring its own drop-off box. A transportation destination for each product is specified. Three objectives are considered: maximizing the volume of used products collected at drop-off boxes; minimizing the cost of transporting collected products to designated locations; and minimizing the cost of allocating space for drop-off boxes. We formulate the above model as a multiobjective integer programming problem and generate the corresponding set of Pareto optimal solutions. We apply the model to an area using population data for Chofu City, Tokyo, Japan, and analyze the trade-offs between the objectives.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.42-50
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• 2006

The multi-criteria group decision making (MCGDM) problem is to determine the best compromise solution in a set of competing alternatives that are evaluated under conflicting criteria by decision maker (DM)s. In this paper, we propose a mixed-integer programming (MIP) model to solve MCGDM. The existing method based on minimizing a distance measure such as Median Approach can not guarantee the best compromise solution because the element of median point vector is defined with respect to each criteria separately. However, by considering all criteria simultaneously, we generate median point that is better for locating the best compromise solution. We also utilize the concept of spatial dispersion index (SDI) to produce a threshold value, which is used as a guideline to choose either the Utopian Approach or the Median Approach. And we suggest using CBITP (Convex hull of individual maxima Based Interactive Tchebycheff Procedure) to provide DMs with various Pareto-optimal solutions so that DMs have broad range of selection.

• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.211-218
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• 2004

It was amazing that efficiency of bureaucratic system, when Marx Weber presented theory of bureaucracy. Now, Government and Bureaucratic organization are confronted with a forked road of change. The purpose of this paper is not so much to force choices among the alternative visions of governance but rather to make the choices available to governments more evident. Any choice of paradigms for government and administration is unlikely to be Pareto optimal, but we should be clear about what we receive and what we sacrifice when we make these judgements about governance. Today, government and public bureaucracy must be changed. There are needed an innovation of government and public bureaucracy. It must be changed concepts from government to governance. Governance is a national management.

• The KSFM Journal of Fluid Machinery
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• v.7 no.2 s.23
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• pp.7-13
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• 2004

In an attempt to solve multiobjective optimization problems, weighted sum method is most widely used for the advantage that a designer can consider the relative significance of each object functions by weight values but it can be highly sensitive to weight vector and occasionally yield a deviated optimum from the relative weighting values designer designated because the multiobjective function has the form of simple sum of the product of the weighting values and the object functions in traditional approach. To search the design solution agree well to the designer's weighting values, we proposed new multiobjective function which was the functional of each normalized objective functions and considered to find the design solution comparing the distance between the characteristic line and the ideal optimum. In this study, proposed multiobjective function was applied to design high efficiency and low noise axial flow fan and the result shows this approach is effective for the case that the quality of the design can be highly affected by the designer's subjectiveness represented as weighting values in multiobjective design optimization process.

• Industrial Engineering and Management Systems
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• v.10 no.2
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• pp.134-139
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• 2011

The problem of scheduling in permutation flowshops has been extensively investigated by many researchers. Recently, attempts are being made to consider more than one objective simultaneously and develop algorithms to obtain a set of Pareto-optimal solutions. Varadharajan et al. (2005) presented a multi-objective simulated-annealing algorithm (MOSA) for the problem of permutation-flowshop scheduling with the objectives of minimizing the makespan and the total flowtime of jobs. The MOSA uses two initial sequences obtained using heuristics, and seeks to obtain non-dominated solutions through the implementation of a probability function, which probabilistically selects the objective of minimizing either the makespan or the total flowtime of jobs. In this paper, the same problem of heuristically developing non-dominated sequences is considered. We propose an effective heuristics based on simulated annealing (SA), in which the weighted sum of the makespan and the total flowtime is used. The essences of the heuristics are in selecting the initial sequence, setting the weight and generating a solution in the search process. Using a benchmark problem provided by Taillard (1993), which was used in the MOSA, these conditions are extracted in a large-scale experiment. The non-dominated sets obtained from the existing algorithms and the proposed heuristics are compared. It was found that the proposed heuristics drastically improved the performance of finding the non-dominated frontier.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.171-180
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• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.57-66
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• 2012

The paper deals with the development of a model for group decision making under multiple criteria. The Multi-criteria group decision making (MCGDM) is the process to determine the best compromise solution in a set of competing alternatives that are evaluated by decision makers having their own preferences on conflicting objectives. For MCGDM, we propose a Mixed-Integer Programming (MIP) model that implements a revised median approach by noticing that the original median approach cannot consider the willingness to reject and the indifferent preference conditions. The proposed MIP model tries to select a common best Pareto-optimal solution by maximizing the overall desirability considering the willingness to reject and the indifferent preference that represent the tolerance measure of each decision maker. To evaluate the effectiveness of the proposed model, we compared the results of the proposed model with those of the median approach. The results showed that the proposed MIP model produces more realistic and better compromised alternative by incorporating the decision maker's willingness to reject and the indifferent preferences over each criteria.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.713-724
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• 2013

In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1103-1106
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• 2004

The objective of this study is to induce the design drought rainfall by the methodology of L-moment including testing homogeneity, independence and outlier of the data of annual minimum monthly rainfall in 57 rainfall stations in Korea in terms of consecutive duration for 1, 2, 4, 6, 9 and 12 months. To select appropriate distribution of the data for annual minimum monthy rainfall by rainfall station, the distribution of generalized extreme value (GEV), generalized logistic (GLO) as well as that of generalized pareto (GPA) are applied and the appropriateness of the applied GEV, GLO, and GPA distribution is judged by L-moment ratio diagram and Kolmogorov-Smirnov (K-S) test. As for the annual minimum monthly rainfall measured by rainfall station and that stimulated by Monte Carlo techniques, the parameters of the appropriately selected GEV and GPA distributions are calculated by the methodology of L-moment and the design drought rainfall is induced. Through the comparative analysis of design drought rainfall induced by GEV and GPA distribution by rainfall station, the optimal design drought rainfall by rainfall station is provided.