• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1088-1095
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• 1999

This paper presents short-term hydro scheduling method for hydrothermal coordination by genetic algorithms. Hydro scheduling problem has many constraints with fixed final reservoir volume. In this paper, the difficult water balance constraints caused by hydraulic coupling satisfied throughout dynamic decoding method. Adaptive penalizing method was also proposed to handle the infeasible solutions that violate various constraints. In this paper, we proposed GA to solve hydrothermal scheduling with appropriate decoding method and dynamic penalty method. The effectiveness of the proposed method is demonstrated in the case study.

• ETRI Journal
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• v.29 no.2
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• pp.201-211
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• 2007

This paper focuses on the scheduling problem with the objective of maximizing system throughput, while guaranteeing long-term quality of service (QoS) constraints for non-realtime data users and short-term QoS constraints for realtime multimedia users in multiclass service high-speed uplink packet access (HSUPA) systems. After studying the feasible rate region for multiclass service HSUPA systems, we formulate this scheduling problem and propose a multi-constraints HSUPA opportunistic scheduling (MHOS) algorithm to solve this problem. The MHOS algorithm selects the optimal subset of users for transmission at each time slot to maximize system throughput, while guaranteeing the different constraints. The selection is made according to channel condition, feasible rate region, and user weights, which are adjusted by stochastic approximation algorithms to guarantee the different QoS constraints at different time scales. Simulation results show that the proposed MHOS algorithm guarantees QoS constraints, and achieves high system throughput.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.39-51
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• 2006

This study addresses how to plan purchasing and leasing of containers to satisfy the demand on containers. The problem can be further decomposed into the long-term planning and the short-term scheduling. The long-term plan specifies the composition of owned containers, long-term leasing containers, and short-term containers. The short-term plan considers the seasonality of demand and determines the time of leasing and the amount of the short-term and the long-term leasing containers. The length of the planning horizon is 10-20 years for the long-term planning, while it is one year for the short-term planning. The time unit is one year for the long-term planning, while it is one month for the short-term planning. This study discusses how to estimate the demand of containers and proposes deterministic models for scheduling purchasing and leasing of containers.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1882-1890
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• 2014

This paper proposes an augmented Lagrange Hopfield network (ALHN) based method for solving multi-objective short term fixed head hydrothermal scheduling problem. The main objective of the problem is to minimize both total power generation cost and emissions of $NO_x$, $SO_2$, and $CO_2$ over a scheduling period of one day while satisfying power balance, hydraulic, and generator operating limits constraints. The ALHN method is a combination of augmented Lagrange relaxation and continuous Hopfield neural network where the augmented Lagrange function is directly used as the energy function of the network. For implementation of the ALHN based method for solving the problem, ALHN is implemented for obtaining non-dominated solutions and fuzzy set theory is applied for obtaining the best compromise solution. The proposed method has been tested on different systems with different analyses and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is very efficient for solving the problem with good optimal solution and fast computational time. Therefore, the proposed ALHN can be a very favorable method for solving the multi-objective short term fixed head hydrothermal scheduling problems.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.289-291
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• 1998

This paper presents short-term hydro scheduling method for hydrothermal coordination by genetic algorithm. Hydro scheduling problem has many constraints with fixed final reservoir volume. In this paper, the difficult water balance constraints caused by hydraulic coupling satisfied throughout dynamic decoding method. Adaptive penalizing method was also proposed to handle the infeasible solutions that violate various constraints. The effectiveness of proposed method in this paper was examined through the case studies. Further studies for the validation of the hydro scheduling scheme obtained by genetic algorithm will be very appreciated.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.73-82
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• 2010

Productivity improvement is considered as one of hot potato topics in international shipyards by the increasing amount of orders. In order to improve productivity of lines, shipbuilders have been researching and developing new work method, process automation, advanced planning and scheduling and so on. An optimization approach was accomplished on short-term scheduling of subassembly lines in this research. The problem of subassembly line scheduling turned out to be a non-deterministic polynomial time problem with regard to SKID pattern’s sequence and worker assignment to each station. The problem was applied by simulated annealing algorithm, one of meta-heuristic methods. The algorithm was aimed to avoid local minimum value by changing results with probability function. The optimization result was compared with discrete-event simulation's to propose what pros and cons were. This paper will help planners work on scheduling and decision-making to complete their task by evaluation.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.1940-1949
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• 2015

This paper presents an Enhanced Particle Swarm Optimization (EPSO) to solve short-term hydrothermal scheduling (STHS) problem with non-convex fuel cost function and a variety of operational constraints related to hydro and thermal units. The operators of the conventional PSO are dynamically controlled using exponential functions for better exploration and exploitation of the search space. The overall methodology efficiently regulates the velocity of particles during their flight and results in substantial improvement in the conventional PSO. The effectiveness of the proposed method has been tested for STHS of two standard test generating systems while considering several operational constraints like system power balance constraints, power generation limit constraints, reservoir storage volume limit constraints, water discharge rate limit constraints, water dynamic balance constraints, initial and end reservoir storage volume limit constraints, valve-point loading effect, etc. The application results show that the proposed EPSO method is capable to solve the hard combinatorial constraint optimization problems very efficiently.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.370-375
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• 1992

This paper deals with the scheduling and simulation for FMC(Flexible Manufacturing Cells). In order to achieve CIM, there is a critical need to link factory level and machine level. The primary functions performed by this link for all jobs issued to the shop floor and cell include short-term scheduling and dynamic operational scheduling. Here, hierarchical control structure is introduced to define these functions. And Intelligent scheduling through expert module is adopted for efficiency of FMC operation. Computer simulation reveals that expert scheduling method is better than heuristics in various performance index.

• Proceedings of the KSR Conference
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• 2003.05a
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• pp.280-285
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• 2003

The process of railway traffic planning is composed of several steps such as long-term, mid-term. short-term, and real-time plan. The planning of vehicle and manpower resources is a main research topic in tactical short-term planning step. Railway vehicle is usually consisted of a power car, passenger/freight cars and human resource is composed of engine driver, cabin crew, ground personnel. So far, power car was main research target in railway vehicle scheduling problem. Recently according as the light electric railway or high-speed railway is introduced, the operational planning of trainset vehicles become important, In this paper, we introduce the conceptional model for trainset restoring problem and developed heuristic algorithm.

• Proceedings of the Safety Management and Science Conference
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• 2003.05a
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• pp.53-61
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• 2003

The process of railway traffic planning is composed of several steps such as long - term, mid - term, short - term, and real - time plan. The planning of vehicle and manpower resources is a main research topic in tactical short - term planning step Railway vehicle is usually consisted of a power car, passenger/freight cars and human resource is composed of engine driver, cabin crew, ground personnel. So far , power car was main research target in railway vehicle scheduling problem. Recently according as the light electric railway or high - speed railway is introduced, the operational planning of train set vehicles become important . In this paper , we introduce the conceptional model for trainset restoring problem and developed heuristic algorithm.