• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.222-224
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• 2006

This paper addresses a particle swarm optimization-based approach for solving a generating unit maintenance scheduling problem(GMS) with some constraints. We focus on the power system reliability such as reserve ratio better than cost function as the objective function of GMS problem. It is shown that particle swarm optimization-based method is effective in obtaining feasible schedules such as GMS problem related to power system planning and operation. In this paper, we find the optimal solution of the GMS problem within a specific time horizon using particle swarm optimization algorithm. Simple case study with 16-generators system is applicable to the GMS problem. From the result, we can conclude that PSO is enough to look for the optimal solution properly in the generating unit maintenance scheduling problem.

• Journal of Sensor Science and Technology
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• v.25 no.2
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• pp.91-102
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• 2016

The objective of this paper is to propose a heuristic algorithm to optimize the railway track maintenance scheduling, a NP-hard problem, by reflecting conditions of the actual field more quickly and easily. We develop the mechanism based on Binary Artificial Bee Colony (BABC) and Binary Harmony Search (BHS), and verify their performance through simulation experiments. Our proposed BABC and BHS mechanisms were applied to problems composed of 30, 60, 100, and 200 operations for railway track maintenance scheduling to carry out experiments and analysis. On comparing it with the results solved by CPLEX, it is found that the mechanism could present an optimal solution within limited time by user.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.8-15
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• 2010

A new technique using a search method which is based on fuzzy multi-criteria function is proposed for GMS(generator maintenance scheduling) in order to consider multi-objective function. Not only minimization of probabilistic production cost but also maximization of system reliability level are considered for fuzzy multi-criteria function. To obtain an optimal solution for generator maintenance scheduling under fuzzy environment, fuzzy multi-criteria relaxation method(fuzzy search method) is used. The practicality and effectiveness of the proposed approach are demonstrated by simulation studies for a real size power system model in Korea in 2010.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.7
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• pp.352-357
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• 2005

In competitive electricity markets, the System Operator (SO) coordinates the overall maintenance schedules when the collective maintenance schedule reported to 50 by Gencos not satisfy the specified operating criteria, such as system reliability or supply adequacy. This paper presented a method that divides generator maintenance scheduling of the 50 into a master-problem and a sub-problem. Master-problem is schedule coordination and sub-problem is DC-optimal power flow. If sub-problem is infeasible, we use the algorithm of modifying operating criteria of master-problem. And, the 50 should use the open information only, because the information such as cost function of a generator and bidding Price is highly crucial for the strategies of profit maximization.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.216-217
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• 2006

This paper presents development of program for generator maintenance scheduling. The maintenance scheduling of generating units is a dynamic discrete combinatorial optimization problem with constraints to determine the optimal maintenance periods of each generating units for a given planning periods. The developed program is designed so as to provide the maintenance schedule satisfying the operating reserve margin levelization and the procurement of proper reliability. In order to verify the effectiveness of the developed program, the numerical study has been performed with the practical data in 2005.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.33-49
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• 2002

CBM (Condition-Based Maintenance) has increasingly drawn attention in industry because of its many benefits. CBM Problem Is characterized as a state-dependent scheduling model that demands simultaneous maintenance actions, each for an attribute that influences on machine condition. This problem is very hard to solve within conventional Markov decision process framework. In this paper, we present an intelligent machine maintenance scheduler, for which a new incremental decision tree learning method as evolutionary system identification model and shortest path problem as schedule generation model are developed. Although our approach does not guarantee an optimal scheduling policy in mathematical viewpoint, we verified through simulation based experiment that the intelligent scheduler is capable of providing good scheduling policy that can be used in practice.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.9
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• pp.542-549
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• 2003

In this paper, a novel approach to generator maintenance scheduling strategy in competitive electricity markets based on non-cooperative dynamic game theory is presented. The main contribution of this study can be considered to develop a game-theoretic framework for analyzing strategic behaviors of generating companies (Gencos) from the standpoints of the generator maintenance-scheduling problem (GMP) game. To obtain the equilibrium solution for the GMP game, the GMP problem is formulated as a dynamic non-cooperative game with complete information. In the proposed game, the players correspond to the profit-maximizing individual Gencos, and the payoff of each player is defined as the profits from the energy market. The optimal maintenance schedule is defined by subgame perfect equilibrium of the game. Numerical results for two-Genco system by both proposed method and conventional one are used to demonstrate that 1) the proposed framework can be successfully applied in analyzing the strategic behaviors of each Genco in changed markets and 2) both methods show considerably different results in terms of market stability or system reliability. The result indicates that generator maintenance scheduling strategy is one of the crucial strategic decision-makings whereby Gencos can maximize their profits in a competitive market environment.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.798-800
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• 2005

In competitive electricity markets, the System Operator (SO) coordinates the overall maintenance schedules whenever the collective maintenance schedule reported to SO by Gencos & Transco in the pool does not satisfy the specified operating criteria, such as system reliability or supply adequacy. We propose a two-layer mathematical algorithm amenable to maintenance scheduling problem into master problem and sub-problem. The master problem coordinates the scheduling, and sub-problem, DC optimal power flew, checks the adequacy. The proposed algorithm was demonstrated with a case study.

• Journal of the Korean Society for Railway
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• v.13 no.6
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• pp.601-607
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• 2010

The objective of this paper is to propose a fast and easy Binary Artificial Bee Colony (BABC) heuristic algorithm to optimize NP-hard scheduling problem of railway track maintenance considering real conditions. The optimal or best solutions can be found using proposed BABC within very short or user specified computation time. We can greatly maximize the objective value using this proposed method in 30, 60, 100 and 200 work size railway track maintenance scheduling problems for experiment and analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.303-312
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• 2018

The university facility is made up of multiple buildings and has many maintenance items. In addition, administrative constraints need to be handled within a limited period. Most maintenance work is small scale and multi-work construction, such as waterproofing, needs to be organized. The facility manager makes annual unit price contract with a maintenance company and carries out the maintenance work. On the other hand, delay and rework is occurring because existing maintenance work performed without scheduling based on the manpower input. This study proposed a scheduling model that can support the facility manager to manage maintenance works of multiple university facilities at the program level. The model consists of three stages in order. In object analysis, details of the maintenance items were analyzed and the quantity is calculated based on the quantity takeoff sheet. In resource analysis, the craftsmen and construction period of detailed works are derived for the effective input of craftsmen. In scheduling, the priority of each work and the optimal manpower input are derived. The optimal schedule is selected according to the goodness of fit. The applicability and effectiveness of the prototype was evaluated through a case study and interviews with case participants. The model was found to be an effective tool to support the scheduling of maintenance works for the facility manager.