• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.29-43
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• 2021

In this paper, a new approach to solve the constrained dynamic programming is proposed by using the constraint programming. While the conventional dynamic programming scheme has the state space augmented with states on constraints, this approach, without state augmentation, represents states of constraints as domains in a contraining programming solver. It has a hybrid computational mechanism in its computation by combining solving the Bellman equation in the dynamic programming framework and exploiting the propagation and inference methods of the constraint programming. In order to portray the differences of the two approaches, this paper solves a simple version of the long-term car rental financing problem. In the conventional scheme, data structures for state on constraints are designed, and a simple inference borrowed from the constraint programming is used to the reduction of violation of constraints because no inference risks failure of a solution. In the hybrid approach, the architecture of interface of the dynamic programming solution method and the constraint programming solution method is shown. It finally discusses the advantages of the proposed method with the conventional method.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.803-811
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• 2003

Multiple sequence alignment is a useful tool to identify the relationships among protein sequences. Dynamic programming is the most widely used algorithm to obtain multiple sequence alignment with optimal cost. However, dynamic programming cannot be applied to certain cost function due to its drawback and cannot be used to produce optimal multiple sequence alignment. We propose sub-alignment refinement algorithm to overcome the problem of dynamic programming. Also we show proposed algorithm can solve the problem of dynamic programming efficiently.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.192-199
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• 2007

This paper proposes a new printed circuit board (PCB) assembly planning method for multi-head surface mounters. We present an integer programming formulation for the optimization problem, and propose a heuristic method to solve the large NP-complete problem within a reasonable time. A dynamic programming technique is then applied to the feeder arrangement optimization and placement sequence optimization to reduce the overall assembly time. Comparative simulation results are finally presented to verify the usefulness of the proposed method.

• 전기의세계
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• v.21 no.1
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• pp.13-16
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• 1972

Dynamic Programming is regarded as a very powerful tool for solving nonlinear optimization problem subject to a number of constraints of state and control variables, but has definite disadvantages that it requires much more computing time and consumes much more memory spaces than other technigues. In order to eliminate the above-mentioned demerits, this paper suggests a news technique called Multiple Dynamic Programming. The underlying principles are based on the concept of multiple passes that, instead of forming fin lattices in time-state plane as adopted in the conventional Dynamic Programming, the Multiple Dynamic Programming constitutes, at the first pass, coarse lattices in the feasible domain of time-state plane and determines the optimal state trajectory by the usual method of Dynamic Programming, and at the second pass again constitutes finer lattices in the narrower domain surrounded by both the upperand lower edges next to the lattice edges through which the first pass optimal trajectory passes and determines the more accurate optimal trajectory of state, and then at the third pass repeats the same processes, and so on. The suggested technique insures remarkable curtailment in amounts of computer memory spaces and conputing time, and its applicability has been demonstrated by a case study on the hydro-thermal power coordination in Korean power system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.12
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• pp.591-596
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• 2002

This Paper Presents a new methodology for Direct Load Control(DLC) and Unit Commitment. To consider economical effect of DLC, we developed 3-Dimensional dynamic programming which can combine unit commitment and DLC. Traditional dynamic programming has 2-Dimensional which consist of state and stage, but newly developed dynamic programming has DLC state, U.C. state, and stage. As a result, economical DLC and unit commitment schedule of the power system is possible. This method is applied to the test system, and the usefulness of the method is verified.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.8
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• pp.726-733
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• 2004

This paper presents a method to segment object ID(identification) marks on poor quality images under uncontrolled lighting conditions of automated inspection process. The method is based on dynamic programming using multiple templates and normalized gray-level correlation (NGC) method. If the lighting condition is not good and hence, we can not control the image quality, target image to be inspected presents poor quality ID marks and it is not easy to identify and recognize the ID characters. Conventional several methods to segment the interesting ID mark regions fail on the bad quality images. In this paper, we propose a multiple template method, which uses combinational relation of multiple templates from model templates to match several characters of the inspection images. To increase the computation speed to segment the ID mark regions, we introduce the dynamic programming based algorithm. Experimental results using images from real factory automation(FA) environment are presented.

• Journal of Korean Institute of Industrial Engineers
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• v.4 no.2
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• pp.77-81
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• 1978

The assortment problem with deterministic demand has been formulated so that a dynamic programming can be applied to find optimal production lengths that will minimize the sum of cutting losses. The original minimization problem can be reformulated as the maximization problem with a different objective function. This problem can be solved by the dynamic programming technique. A numerical example illustrates this approach. The ratio of computation amount of emumeration method to that of this dynamic programming is approximately n to 1.

• Korean Management Science Review
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• v.20 no.2
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• pp.33-43
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• 2003

In this paper we applied dynamic programming to determining work schedule type. In dynamic programming formulation, each day during a planning horizon represents a stage for which a decision is made. The alternatives are given by work schedule types that combine regular time, overtime, additional shift, and so on. In this case, their associated return function is labor cost. The state is defined as the amount of work time allocated to stage 1, stage 2,…, and current stage. A case study for a real manufacturing company was performed to apply dynamic programming to scheduling daily work hours during a week. The case study showed that total cost of our solution derived from dynamic programming decreased by about 6％ as compared with the solution obtained from the previous method.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.156-161
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• 1994

In this paper, we consider an optimal control problem of a nonlinear stochastic system. Dynamic programming approach is employed for the formulation of a stochastic optimal control problem. As an optimality condition, dynamic programming equation so called the Bellman equation is obtained, which seldom yields an analytical solution, even very difficult to solve numerically. We obtain the numerical solution of the Bellman equation using an algorithm based on the finite difference approximation and the contraction mapping method. Optimal controls are constructed through the solution process of the Bellman equation. We also construct a test case in order to investigate the actual performance of the algorithm.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.