• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.81-94
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• 1986

A shortest path dynamic programming formulation is proposed and attemped to solve an uncapacitated expansion sequencing problem. It is also compared with the Extended Binary State Space approach with total capacity. Difficulties and merits associated with the formulation are discussed. The shortest path dynamic programming lacks the separability condition and an optimal solution is not guaranteed. However it has other merits and seems to be the practical solution procedure for the expansion sequencing problem in a sense that it finds near optimal solution with less state evaluations.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.120-128
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• 2015

Quality function deployment (QFD) is a useful method in product design and development to maximize customer satisfaction. In the QFD, the technical attributes (TAs) affecting the product performance are identified, and product performance is improved to optimize customer requirements (CRs). For product development, determining the optimal levels of TAs is crucial during QFD optimization. Many optimization methods have been proposed to obtain the optimal levels of TAs in QFD. In these studies, the levels of TAs are assumed to be continuous while they are often taken as discrete in real world application. Another assumption in QFD optimization is that the requirements of the heterogeneous customers can be generalized and hence only one house of quality (HoQ) is used to connect with CRs. However, customers often have various requirements and preferences on a product. Therefore, a product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. To overcome these problems, this paper proposes an optimization approach to find the optimal set of TAs under multi-segment market. Dynamic Programming (DP) methodology is developed to maximize the overall customer satisfaction for the market considering the weights of importance of different segments. Finally, a case study is provided for illustrating the proposed optimization approach.

• Journal of Korean Port Research
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• v.14 no.1
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• pp.37-45
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• 2000

Centralized safety stock in a periodic replenishment system which consists of one central warehouse and m regional warehouse can reduce backorders allocating the centralized safety stocks to regional warehouse in a certain instant of each replenishment cycle. If the central warehouse can not monitoring inventories in the regional warehouse, then we have to predetermine the instant of allocation according to demand distribution and this instant must be same for all different replenishment cycle. However, transition of inventory level in each cycle need not to be same, and therefore different instant of the allocation may results reduced shortage compare to the predetermined instant of allocation. In this research, we construct a dynamic model based on the assumption of monitoring inventories in the regional warehouse everyday, and develop an algorithm minimize shortage in each replenishment cycle using dynamic programming approach.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.83-90
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• 1999

Centralized safety stock in a periodic replenishment system which consists of one central warehouse and m regional warehouse can reduce backorders allocation the centralized safety stocks to regional warehouse in a certain instant of each replenishment cycle. If the central warehouse can not monitoring inventories in the regional warehouse, then we have to predetermine the instant of allocation according to demand distribution and this instant must be same for all different replenishment cycle. However, transition of inventory level in each cycle need not to be same, and therefore different instant of the allocation may results reduced shortage compare to the predetermined instant of allocation. In this research, we construct a dynamic model based on the assumption of monitoring inventories inventories in the regional warehouse everyday, and develop an algorithm minimize shortage in each replenishment cycle using dynamic programming approach.

• IE interfaces
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• v.24 no.2
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• pp.112-118
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• 2011

This paper introduces one type of prize collecting travelling salesman problem with time windows (PCTSPTW), proposes a mixed integer programming model for the problem, and shows that the problem can be reduced to the elementary shortest path problem with time windows and capacity constraints (ESPPTC). Then, a new dynamic programming algorithm is proposed to solve ESPPTC quickly. Computational results show the effectiveness of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.514-523
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• 2002

Feeder arrangement is an important element of process planning for printed circuit board assembly systems. This paper newly proposes a feeder arrangement method for multihead-gantry chip mounters. The multihead-gantry chip mounters are very popular in printed circuit board assembly system, but the research has been mainly focused on single-head-gantry chip mounters. We present an integer programming formulation for optimization problem of multihead-gantry chip mounters, and propose a heuristic method to solve the large NP-complete problem in reasonable time. Dynamic programming method is then applied to feeder arrangement optimization to reduce the overall assembly time. Comparative simulation results are finally presented to verify the usefulness of the proposed method.

• Earthquakes and Structures
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• v.3 no.3_4
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• pp.533-548
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• 2012

Dynamic analysis of concrete gravity dam-reservoir systems is an important topic in the study of fluid-structure interaction problems. It is well-known that the rigorous approach for solving this problem relies heavily on employing a two-dimensional semi-infinite fluid element. The hyper-element is formulated in frequency domain and its application in this field has led to many especial purpose programs which were demanding from programming point of view. In this study, a technique is proposed for dynamic analysis of dam-reservoir systems in the context of pure finite element programming which is referred to as the wavenumber approach. In this technique, the wavenumber condition is imposed on the truncation boundary or the upstream face of the near-field water domain. The method is initially described. Subsequently, the response of an idealized triangular dam-reservoir system is obtained by this approach, and the results are compared against the exact response. Based on this investigation, it is concluded that this approach can be envisaged as a great substitute for the rigorous type of analysis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.180-187
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• 2001

This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning(GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming(EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, new algorithm is presented to enhance the efficiency of the EP algorithm for solving the GEP problem. By a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can yield a kind of trend in the cost value. To validate the proposed approach, this algorithm is tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a resonable computational time compared with conventional EP and dynamic programming.