• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.59-66
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• 1992

This Paper, first, tries to optimize the output specifications with uncertain characteristics. And then aims to solve the problem not only by making use of transformed multiple regression equation which can yield objective function of output characteristics but also by formulating developed multiple fuzzy goal programming using fuzzy set theory which can treat uncertainty easily, and the efficiency of these techniques, will be also demonstrated through a case study.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.169-186
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• 1994

This paper presents three mathematical programming approaches to solving transshipment problems with interval supply and demand requirements. A linear goal programming model was developed based on the data obtained from a nationwide retail firm. Three mathematical programming model results were compared and analyzed, and three separate hypotheses were examined by using the Wilcoxon signed-ranked test for the model applicability. The test results were analyzed and interpreted for decision making.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1177-1180
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• 1996

In this paper, we propose an algorithm that realizes cooperative behavior by construction of autonomous mobile robot system. Each robot is able to sense other robots and obstacles, and it has the rule of behavior to achieve the goal of the system. In this paper, to improve performance of the whole system, we use Genetic Programming based on Natural Selection. Genetic Programming's chromosome is a program of tree structure and it's major operators are crossover and mutation. We verify the effectiveness of the proposed scheme from the several examples.

• Journal of the Korean Institute of Navigation
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• v.20 no.4
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• pp.59-70
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• 1996

A berth assignment problem has a direct impact on assessment of charges made to ships and goods. In this paper, we concerned with of fuzzy mathematical programming models for a berth assignment problem to achieved an efficient berth operation in a fuzzy environment. In this paper, we focus on the berth assignment programming with fuzzy parameters which are based on personal opinions or subjective judgement. From the above point of view, assume that a goal and a constraint are given by fuzzy sets, respectively, which are characterized by membership functions. Let a fuzzy decision be defined as the fuzzy set resulting from the intersection of a goal and constraint. This paper deals with fuzziness in all parameters which are expressed by fuzzy numbers. A fuzzy parameter defined by a fuzzy number means a possibility distribution of the parameters. These fuzzy 0-1 integer programming problems are formulated by fuzzy functions whose concept is also called the extension principle. We deal with a berth assignment problem with triangular fuzzy coefficients and propose a branch and bound algorithm for solving the problem. We suggest three models of berth assignment to minimizing the objective functions such as total port time, total berthing time and maximum berthing time by using a revised Maximum Position Shift(MPS) concept. The berth assignment problem is formulated by min-max and fuzzy 0-1 integer programming. Finally, we gave the numerical solutions of the illustrative examples.

• Journal of Computing Science and Engineering
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• v.1 no.1
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• pp.56-73
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• 2007

Demands have been growing in safety-critical application fields for producing networked real-time embedded computing (NREC) systems together with acceptable assurances of tight service time bounds (STBs). Here a service time can be defined as the amount of time that the NREC system could take in accepting a request, executing an appropriate service method, and returning a valid result. Enabling systematic composition of large-scale NREC systems with STB certifications has been recognized as a highly desirable goal by the research community for many years. An appealing approach for pursuing such a goal is to establish a hard-real-time (HRT) component model that contains its own STB as an integral part. The TMO (Time-Triggered Message-Triggered Object) programming scheme is one HRT distributed computing (DC) component model established by the first co-author and his collaborators over the past 15 years. The TMO programming scheme has been intended to be an advanced high-level RT DC programming scheme that enables development of NREC systems and validation of tight STBs of such systems with efforts far smaller than those required when any existing lower-level RT DC programming scheme is used. An additional goal is to enable maximum exploitation of concurrency without damaging any major structuring and execution approaches adopted for meeting the first two goals. A number of previously untried program structuring approaches and execution rules were adopted from the early development stage of the TMO scheme. This paper presents new concrete justifications for those approaches and rules, and also discusses new extensions of the TMO scheme intended to enable further exploitation of concurrency in NREC system design and programming.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.421-434
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• 2005

Machine learning has been extensively studied in recent years as effective tools in pattern classification problem. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problem with two class sets, the idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. However, the idea of maximal margin separation is not quite new: in 1960's the multi-surface method (MSM) was suggested by Mangasarian. In 1980's, linear classifiers using goal programming were developed extensively. This paper proposes a new family of SVM using MOP/GP techniques, and discusses its effectiveness throughout several numerical experiments.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.23-35
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• 2005

This paper aims to develop a practical finite capacity MRP (FCMRP) system based on the needs of an automotive parts manufacturing company in Thailand. The approach includes a linear goal programming model to determine the optimal start time of each operation to minimize the sum of penalty points incurred by exceeding the goals of total earliness, total tardiness, and average flow-time considering the finite capacity of all work centers and precedence of operations. Important factors of the proposed FCMRP system are penalty weights and dispatching rules. Effects of these factors on the performance measures are statistically analyzed based on a real situation of an auto-part factory. Statistical results show that the dispatching rules and penalty weights have significant effects on the performance measures. The proposed FCMRP system offers a good tradeoff between conflicting performance measures and results in the best weighted average performance measures when compared to conventional forward and forward-backward finite capacity scheduling systems.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.77-90
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• 2009

Total profit level Increases if a company increase the cost for achieving R&D related goals of equipment productivity enhancement, production cost saving, or for achieving equipment scale target, sales volume goal. But how much money should be invested to achieve a certain level of profit? We formulated the model to set the optimal goal levels to minimize the investment cost under the constraint that certain level of total profit should be guaranteed. This model derived from a case of P steel company. We found that this should be considered in relation with the production sales planning (known as optimal product mix problem) to guarantee the profit. We suggested a nonlinear programming model, 3 valiant form of the p+roduct mix problem. We can find the optimal Investment level for the R&D related goals or sales volume goal, equipment scale target for the P steel company using the model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.191-196
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• 2008

This paper presents an evolutionary algorithm for goal programming with preemptive priority. To do this, an evolutionary strategy is suggested which search for the solution satisfying the goals in the order of the priority. Two-sided assembly line balancing problems with multiple goals are used to validate the applicability of the algorithm. In the problems, three goals are considered in the following priority order: minimizing the number of mated-stations, achieving the goal level of workload smoothness, and maximizing the work relatedness. The proper evolutionary components such as encoding and decoding method, evaluation scheme, and genetic operators, which are specific to the problem being solved, are designed in order to improve the algorithm's performance. The computational result is reported.

• IE interfaces
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• v.11 no.1
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• pp.183-190
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• 1998

This paper is concerned with the bicriteria vehicle routing problems with time window constraints(BVRPTW). The BVRPTW is to determine the most favorable vehicle routes that minimize the total vehicle travel time and the total customer wait time which are, more often than not, conflicting. We construct a linear goal programming (GP) model for the BVRPTW and propose a heuristic algorithm to relieve a computational burden inherent to the application of the GP model. The heuristic algorithm consists of a parallel insertion method for clustering and a sequential linear goal programming procedure for routing. The results of computational experiments showed that the proposed algorithm finds successfully more favorable solutions than the Potvin an Rousseau's method that is known as a very good heuristic for the VRPs with time window constraints, through the change of target values and the decision maker's goal priority structure.