• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.81-81
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• 1996

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.2
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• pp.209-229
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• 1996

Scheduling problems which determine the sequence of jobs are one of the Important issues to many industries. This paper deals with a single-machine job sequencing problem which has complex constraints and an objective function. To solve the problem, an expressive constraint description language and an automatic code generator are developed for our scheduling system. The user just needs to describe the scheduling problem using the constraint description language that allows to express both quantitative and qualitative constraints as well as an objective function in real world semantics. Then, a complete scheduling program based on constraint satisfaction technique is automatically generated through the code generator. Advantage of this approach is that models of the scheduling problems are easily developed and maintained because models ore formulated by using the language which reflects real world semantics.

• The Transactions of the Korea Information Processing Society
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• v.7 no.10
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• pp.3113-3118
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• 2000

A framework proposed in this paper includes representations of multi-objectives rule-basd constraint satisfaction problem(RCSP) and especially, flexible structure of RCSP which provides adequate solution sithin an expectation time and adaptively considers some change of constratraints on the RCSP. In this study, the power system implemented by above framework is entirely programmed by using a modeling system for the quantitative model and a rule-based constraint programming tool for the qualitative model. The results of an exemplary cse show how the structure that involves the problem representation gives the scheduler an effective decision and what are the advantages of the easy and diverse representation for considering the techmical characteristics of the units on the RCSP over the traditional problem.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.43-61
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• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.

• Journal of KSNVE
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• v.7 no.5
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• pp.773-780
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• 1997

Using Generalized Inverse Method presented by Udwadia and Kalaba in 1992, we can obtain equations to exactly describe the motion of constrained systems. When the differential equations are numerically integrated by any numerical integration scheme, the numerical results are generally found to veer away from satisfying constraint equations. Thus, this paper deals with the numerical integration of the differential equations describing constrained systems. Based on Baumgarte method, we propose numerical methods for reducing the errors in the satisfaction of the constraints.

• Journal of IKEEE
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• v.13 no.1
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• pp.71-76
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• 2009

As one of the most important requirements for wireless sensor networks, prolonging network lifetime can be realized by minimizing energy consumption in cluster heads as well as sensor nodes. While most of the previous researches have focused on the energy of sensor nodes, we devote our attention to cluster heads because they are most dominant source of power consumption in the cluster-based sensor networks. Therefore, we seek to minimize energy consumption by minimizing the maximum(MINMAX) energy dissipation at each cluster heads. This work requires energy-efficient clustering of the sensor nodes while satisfying given energy constraints. In this paper, we present a constraint satisfaction modeling of cluster-based routing in a heterogeneous sensor networks because mixed integer programming cannot provide solutions to this MINMAX problem. Computational experiments show that substantial energy savings can be obtained with the MINMAX algorithm in comparison with a minimum total energy(MTE) strategy.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1638-1641
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• 2002

A constraint satisfaction problem (CSP) is a general framework that can formalize various application problems in artificial intelligence. In this paper, we will focus on an important subclass of distributed partial CSP called the distributed maximal CSP that can be applied to more practical kinds of problems. Specifically, we propose a method of solving distributed mammal CSPs using a combination of approximate and exact algorithms that yields faster optimal solutions than otherwise possible using conventional methods. Experimental results are presented that demonstrate the effectiveness of the proposed new approach.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.539-550
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• 1998

A computerized layout system based on a constraint satisfaction problem (CSP) technique was developed to treat both facility layout and instrument panel layout problems. This layout system attempts to allocate various facilities under multiple layout criteria including qualitative design objectives. Since most of the design objectives can be understood as constraints which the layout solution must satisfy to improve the goodness of the solution, a CSP technique was employed to solve the multi-constraints layout problem. The effectiveness of the system was evaluated by the comparison with the well-known facility layout program, CORELAP. Furthermore, the proposed system can also be applied to the instrument panel layout problem successfully. Several user-centered guidelines were well reflected on the solution obtained. CONSLAY, the prototype layout system being developed in the research, greatly enhanced the interactions with the designer so as to deal with the problem-dependent nature of the layout problem and to properly reflect the domain-specific knowledge of the designer.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.163-170
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• 2004

In order to automatically determine the sequences of block assembly operations in shipbuilding, a process planning system using case-based reasoning (CBR) is developed. A block-assembly planning problem is modeled as a constraint satisfaction problem where the precedence relations between operations are considered constraints. The process planning system generates an assembly sequence by adapting information such as solutions and constraints collected from similar cases retrieved from the case base. In order to find similar cases, the process planning system first matches the parts of the problem and the parts of each case based on their roles in the assembly, and then it matches the relations related to the parts-pairs. The part involved in more operations are considered more important. The process planning system is applied to simple examples fur verification and comparison.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.99-110
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• 2000

오늘날의 기업은 상품을 판매하는 것 뿐만 아니라 기업의 신용과 이미지를 위해 그 상품에 대한 사후처리(After Service) 업무에 많은 투자를 하고 있다. 이러한 양질의 사후서비스를 고객에게 공급하기 위해서는 많은 인력을 합리적으로 관리해야 하며 요청되는 고장수리 서비스 업무의 신속한 해결을 위해 업무를 인력에게 합리적으로 배정을 해야 한다. 그러므로, 회사의 비용을 최소화하면서 정해진 시간에 요청된 작업을 처리하기 위해서는 인력들에게 작업을 배정하고 스케줄링하는 문제가 발생된다. 본 논문에서는 이러한 문제를 해결하기 위해서 화학계기의 A/S작업을 인력에게 합리적으로 배정 하는 스케줄링 시스템에 관한 연구이다. 먼저 스케줄링 모델을 HP사의 화학분석 및 시스템을 판매, 유지보수 해주는 Y사의 작업 스케줄을 분석하여 필요한 도메인과 고객서비스전략과 인력관리전략에서 제약조건을 추출하였고 여기에 스케줄링 문제를 해결하기 위한 방법으로 제약만족문제(CSP) 해결기법인 도메인 여과기법을 적용하였다. 도메인 여과기법은 제약조건에 의해 변수가 갖는 도메인의 불필요한 부분을 여과하는 것으로 제약조건과 관련되어 있는 변수의 도매인 크기가 축소되는 것이다. 또한, 스케줄링을 하는데에 있어서 비용적인 측면에서의 스케줄링방법과 고객만족도에서의 스케줄링 방법을 비교하여 가장 이상적인 해를 찾는데 트래이드오프(Trade-off)를 이용하여 최적의 해를 구했으며 실험을 통해 인력에게 더욱 효율적으로 작업들을 배정 할 수 있었고 또한, 정해진 시간에 많은 작업을 처리 할 수 있었으며 작업을 처리하는데 있어 소요되는 비용을 감소하는 결과를 얻을 수 있었다.