• Proceedings of the Korean Nuclear Society Conference
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• 1998.05b
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• pp.577-582
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• 1998

In nuclear power plant, it has been the important object to reduce the occupational radiation exposure (ORE). Recently, the optimization concept of management science has been studied to reduce the ORE in nuclear power plant. In optimization of the worker allocation, the collective dose, working time, individual dose, an total number of worker must be considered and their priority orders must be thought because the main constraint is necessary for determining the constraints variable of the radiological worker allocation problem. The ultimate object of this study s to look into the change of the optimal allocation of the radiological worker as priority order changes. In this study, the priority order is the characteristic of goal programming that is a kind of multi-objective linear programming. From a result of study using goal programming, the total number of worker and collective dose of worker have changed as the priority order has changed and the collective dose limit have played an important role in reducing the ORE.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.9-20
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• 2019

In this paper, we study the university timetabling problem, which consists of two subproblems, the university course timetabling problem and the examination timetabling problem. Given a set of classrooms, students, teachers, and lectures, the problem is to assign a number of courses (and examinations) to suitable timeslots and classrooms while satisfying the given set of constraints. We discuss the modeling and solution approaches to construct course and examination timetables for one of the largest Korean university. By using binary integer programming formulations, we describe these two complex real-world problems. Then, we propose a solution method, called NOGOOD, to solve the examination timetabling model. The computation results show that NOGOOD finds the optimal examination schedule for the given instance. Although we consider a specific instance of the university timetabling problem, the methods we use can be applicable to modeling and solving other timetabling problems.

• Journal of the military operations research society of Korea
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• v.18 no.1
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• pp.47-60
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• 1992

A class of allocation problems can be modeled in a linear programming formulation. But in reality, the coefficient of both the cost and constraint equations can not be generally determined by crisp numbers due to the imprecision or fuzziness in the related parameters. To account for this. a fuzzy version is considered and solved by transforming to a conventional non-linear programming model. This gives a solution as well as the degree that the solution satisfies the objective and constraints simultaneously and hence will be very useful to a decision maker. An attacker assignment problem for multiple fired targets has been modeled by a linear programming formulation by Lemus and David. in which the objective is to minimize the cost that might occur on attacker's losses during the mission. A fuzzy version of the model is formulated and solved by transforming it to a conventional nonlinear programming formulation following the Tanaka's approach. It is also expected that the fuzzy approach will have wide applicability in general allocation problems

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.98-101
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• 2004

Linear Programming(LP) is the term used for defining a wide range of optimization problems in which the objective function to be minimized or maximized is linear in the unknown variables and the constraints are a combination of linear equalities and inequalities. LP problems occur in many real-life economic situations where profits are to be maximized or costs minimized with constraint limits on resources. While the simplex method introduced in a later reference can be used for hand solution of LP problems, computer use becomes necessary even for a small number of variables. Problems involving diet decisions, transportation, production and manufacturing, product mix, engineering limit analysis in design, airline scheduling, and so on are solved using computers. This technique is called Sequential Linear Programming (SLP). This paper describes LP's problems and solves a LP's problems using the neural networks.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.145-170
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• 2005

This paper presents scheduling models for dispatching vehicles to accomplish a sequence of container jobs at the container terminal, in which the starting times as well as the order of vehicles for carrying out these jobs need to be determined. To deal with this scheduling problem, three mixed 0-1 integer programming models, Model 1, Model 2 and Model 3 are provided. We present interesting techniques to reformulate the two mixed integer programming models, Model 1 and Model 2, as pure 0-1 integer programming problems with simple constraint sets and present a lower bound for the optimal value of Model 1. Model 3 is a complicated mixed integer programming model because it involves a set of non-smooth constraints, but it can be proved that its solutions may be obtained by the so-called greedy algorithm. We present numerical results showing that Model 3 is the best among these three models and the greedy algorithm is capable of solving large scale problems.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.1
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• pp.29-35
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• 2004

Locomotive scheduling in railway systems experiences many difficulties because of the complex interrelations among resources, knowledge and various constraints. Artificial intelligence technology has been applied to solve these scheduling problems. These technologies have proved to be efficient in representing knowledge and rules for complex scheduling problems. In this paper, we have applied the CSP (Constraints Satisfaction Problems) programming technique, one of the AI techniques, to solve the problems associated with locomotive scheduling. This method is more effective at solving complex scheduling problems than available mathematical programming techniques. The advanced locomotive scheduling system using the CSP programming technique is realized based on the actual timetable of the Saemaul type train on the Kyong-bu line. In this paper, an overview of the CSP programming technique is described, the modeling of domain and constraints is represented and the experimental results are compared with the real-world existing schedule. It is verified that the scheduling results by CSP programming are superior to existing scheduling performed by human experts. The executing time for locomotive scheduling is remarkably reduced to within several decade seconds, something requiring several days in the case of locomotive scheduling by human experts.

• Industrial Engineering and Management Systems
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• v.11 no.2
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• pp.170-175
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• 2012

In this paper, a procedure using a genetic algorithm (GA) and a heuristic local search (HLS) is proposed for solving facility rearrangement problem (FRP). FRP is a decision problem for stopping/running of facilities and integration of stopped facilities to running facilities to maximize the production capacity of running facilities under the cost constraint. FRP is formulated as an integer programming model for maximizing the total production capacity under the constraint of the total facility operating cost. In the cases of 90 percent of cost constraint and more than 20 facilities, the previous solving method was not effective. To find effective alternatives, this solving procedure using a GA and a HLS is developed. Stopping/running of facilities are searched by GA. The shifting the production operation of stopped facilities into running facilities is searched by HLS, and this local search is executed for one individual in this GA procedure. The effectiveness of the proposed procedure using a GA and HLS is demonstrated by numerical experiment.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.9
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• pp.1779-1786
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• 2002

When the topology optimization is applied to the design of compliant mechanism, unexpected displacements of input and output port are generated since the displacement control is not included in the formulation. To devise a more precise mechanism, displacement constraint is formulated using the mutual potential energy concept and added to multi-objective function defined with flexibility and stiffness of a structure. The optimization problem is resolved by using Finite Element Method(FEM) and Sequential Linear Programming(SLP). Design examples of compliant mechanism with displacement constraint are presented to validate the proposed design method.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.1-6
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• 2011

This paper describes the issue of batch scheduling.In food production, the lead-time from produc-tion to sale should be decreased becausefreshness of the product is important. Products are shipped at diverse times depending on a demand of sellers, because the types of sellers has become diversified such as super-markets, convenience stores and etc. production of quantity demanded must be completed by time to ship it then. The authors consider a problem with due-dates constraints and construct the algorithm to find the opti-mal schedule that satisfy the due-dates constraint, batch size constraint, inventory time constraint and mini-mize total flow time.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.2
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• pp.236-247
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• 1992

Optimization has been developed to minimize the cost function while satisfying constraints. Nonlinear Programming method is used as a tool for the optimization. Usually, cost and constraint function calculations are required in the engineering applications, but those calculations are extremely expensive. Especially, the function and sensitivity analyses cause a bottleneck in structural optimization which utilizes the Finite Element Method. Also, when the functions are quite noisy, the informations do not carry out proper role in the optimization process. An algorithm called "Second-order Approximation Method" has been proposed to overcome the difficulties recently. The cost and constraint functions are approximated by the second-order Taylor series expansion on a nominal points in the algorithm. An optimal design problem is defined with the approximated functions and the approximated problem is solved by a nonlinear programming numerical algorithm. The solution is included in a candidate point set which is evaluated for a new nominal point. Since the functions are approximated only by the function values, sensitivity informations are not needed. One-dimensional line search is unnecessary due to the fact that the nonlinear algorithm handles the approximated functions. In this research, the method is analyzed and the performance is evaluated. Several mathematical problems are created and some standard engineering problems are selected for the evaluation. Through numerical results, applicabilities of the algorithm to large scale and complex problems are presented.presented.