• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.19-30
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• 2013

Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochastic optimal control problems cannot be solved exactly except in very simple cases, approximations are required in most practical problems to obtain good suboptimal policies. In this paper, we present a procedure for finding a suboptimal solution to the constrained index tracking problem based on approximate dynamic programming. Illustrative simulation results show that this procedure works well when applied to a set of real financial market data.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.278-281
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• 2008

Linear motor (LM) based shuttle cars will play an important role in the future transportation systems of marine terminals to cope with increasing container flows. These systems are known as agile port terminals because of their significant advantages. However, routing for multiple shuttle cars is still an open issue. We present a network model of a container yard and propose constrained dynamic programming (DP) for its routing strategy with collision avoidance. The algorithm is a modified version of typical DP which is used to find an optimal path for a single traveler. We evaluate the new algorithm through simulation results for three shuttle cars in a mesh-type container yard.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1064-1072
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• 2017

The power of controlled generators in microgrids randomly fluctuate because of the stochastic volatility of the outputs of photovoltaic systems and wind turbines as well as the load demands. To address and dispatch these stochastic factors for daily operations, a dynamic economic dispatch model with the goal of minimizing the generation cost is established via chance-constrained programming. A Monte Carlo simulation combined with particle swarm optimization algorithm is employed to optimize the model. The simulation results show that both the objective function and constraint condition have been tightened and that the operation costs have increased. A higher stability of the system corresponds to the higher operation costs of controlled generators. These operation costs also increase along with the confidence levels for the objective function and constraints.

• 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 Korean Institute of Industrial Engineers
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• v.19 no.3
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• pp.71-79
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• 1993

Dynamic production planning problems are to determine the optimal production times and production quantities of product for discrete finite periods. In previous many researches, the solutions for these problems have been developed through the algorithms using dynamic programming. The purpose of this research is to suggest the new algorithm using linear programming. This research is to determine optimal production quantities of product in each period to satisfy dynamic for discrete finite periods, minimizing the total of production cost and inventory holding cost. Cost functions are concave, and no backlogging for product is allowed. The new algorithm for capacity constrained problem is developed.

• IE interfaces
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• v.12 no.3
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• pp.362-373
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• 1999

Resource Constrained Dynamic Multi-Projects Scheduling (RCDMPS) is intended to schedule activities of two or more projects sequentially arriving at die shop under restricted resources. The aim of this paper is to develop a new problem solving method for RCDMPS to make an effect schedule based by constraint programming. The constraint-based scheduling method employs ILOG Solver which is C++ constraint reasoning library for solving complex resource management problems and ILOG Schedule which is a add-on library to ILOG Solver dedicated to solving scheduling problems. And this method interfaces with ILOG Views so that the result of scheduling displays with Gantt chart. The scheduling method suggested in this paper was applied to a company scheduling problem and compared with the other heuristic methods, and then it shows that the new scheduling system has more preference.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.160-163
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• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.582-587
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• 1997

This paper deals with the control problem under nonlinear boiler systems with noise, and input constraints. MCMBPC(Multivariable Constrained Model-Based Predictive Controller) proposed by Wilkinson et al.[10,11] is used and nominal model is modified in this paper in order to applied to nonlinear boiler systems with feed-forward terms. The solution of the cost function optimization constrained on input and/or output variables is achieved using quadratic programming, via singular value decomposition(SVD). The controller designed is shown to satisfy the constraints and to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.3
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• pp.20-33
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• 2006

Because of rapid expansion of third party logistics, fierce competition in the transportation industry, and the diversification and globalization of transportation channels, an effective transportation planning by means of multimodal transport is badly needed. Accordingly, this study aims to suggest an optimal transport algorithm for the multimodal transport in the international logistics. As a solution for this problem, first of all, we have applied a pruning algorithm to simplify it, suggesting a heuristic algorithm for constrained shortest path problem to find out a feasible area with an effective time range, which has been applied to the Label Setting Algorithm, consequently leading to multiple Pareto optimal solutions. Also, in order to test the efficiency of the algorithm for constrained shortest path problem, this paper has applied it to the actual transportation path from Busan port of Korea to Rotterdam port of Netherlands.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.57-63
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• 2012

Software release planning model of software product lines was formulated as a precedence-constrained multiple 0-1 knapsack problem. The purpose of the model was to maximize the total profit of an entire set of selected features in a software product line over a multi-release planning horizon. The solution approach is a dynamic programming procedure. Feasible solutions at each stage in dynamic programming are determined by using backward dynamic programming approach while dynamic programming for multi-release planning is forward approach. The pre-processing procedure with a heuristic and reduction algorithm was applied to the single-release problems corresponding to each stage in multi-release dynamic programming in order to reduce the problem size. The heuristic algorithm is used to find a lower bound to the problem. The reduction method makes use of the lower bound to fix a number of variables at either 0 or 1. Then the reduced problem can be solved easily by the dynamic programming approaches. These procedures keep on going until release t = T. A numerical example was developed to show how well the solution procedures in this research works on it. Future work in this area could include the development of a heuristic to obtain lower bounds closer to the optimal solution to the model in this article, as well as computational test of the heuristic algorithm and the exact solution approach developed in this paper. Also, more constraints reflecting the characteristics of software product lines may be added to the model. For instance, other resources such as multiple teams, each developing one product or a platform in a software product line could be added to the model.