• 대한기계학회논문집A
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• 제20권1호
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• pp.14-23
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• 1996

Scheduling jobs effectively under consideration of actual loads on machines is one of the most complicated tasks in production control. The complexity of the finite capacity scheduling often makes the conventional methods of industrial engineering fail. As an alternative, Knowledge-based approaches to job-shop scheduling have been evolved recently. This paper presents a blackboard- based scheduling expert system which combines knowledge-based scheduling with interactive scheduling. It is shown to be possible to generate the feasible schedule within a reasonable time. Flexible reaction management is also possible while keeping the changes in the generated schedule to the minimal and adjusting the schedule to tardy operations or working environmental changes. The system is equipped with a rule base with heuristics for handling conflicted event. A case study applying the implemented system is described.

• 산업경영시스템학회지
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• 제20권44호
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• pp.185-195
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• 1997

In this paper, research and applications of a knowledge-base system in scheduling are review. The method is based on the problem-solving techniques developed in artificial intelligent. The object of this paper is to enable the re-time rescheduling under dynamic environments. Developed to KBS(Knowledge-Based Scheduling) system in this paper, will based on expert system, and applicate to requirement of users effectively.

• 대한산업공학회지
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• 제23권1호
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• pp.197-213
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• 1997

It is not easy to find a good production schedule which can be used in practice. Therefore, production scheduling simulation with a simple dispatching rule or a set of dispatching rules is used. However, a simple dispatching rule may not create a robust schedule, for the same rule is blindly applied to all internal production processes. The presumption is that there might be a specific combination of appropriate rules that can improve the efficiency of a total production system for a certain type of orders. In order to acquire a better set of dispatching rules, simulation is used to examine the performance of various combinations of dispatching rule sets. There are innumerable combination of rule sets. Hence it takes too much computer simulation time to find a robust set of dispatching rule for a specific production system. Therefore, we propose a concept of the knowledge based simulation to circumvent the problem. The knowledge based simulation consists of knowledge bases, an inference engine and a simulator. The knowledge base is made of rule sets that is extracted from both simulation and human intuition obtained by the simulation studies. For a certain type of orders, the proposed system provides several sets of dispatching rules that are expected to generate better results. Then the scheduler tries to find the best by simulating all proposed set of rules with the simulator. The knowledge-based simulator armed with the acquired knowledge has produced improved solutions in terms of time and scheduling performance.

• 산업경영시스템학회지
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• 제19권38호
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• pp.139-146
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• 1996

In this thesis, we suggest the application of dock scheduling which is based on zero-one integer programming. A knowledge based system for dock scheduling was developed to implement this mathematical method.

• 산업경영시스템학회지
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• 제21권48호
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• pp.213-222
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• 1998

The objective of this study is to develop a knowledge-based scheduler applying simulation and knowledge base. This study utilizes a machine induction to build knowledge base which enables knowledge acquisition without domain expert. In this study, the best job dispatching rule for each order is selected according to the specifications of the order information. And these results are built to the fact base and knowledge base using the attribute-oriented induction method and simulation. When a new order enters in the developed system, the scheduler retrieves the knowledge base in order to find a matching record. If there is a matching record, the scheduling will be carried out by using the job dispatching rule saved in the knowledge base. Otherwise the best rule will be added to the knowledge base as a new record after scheduling to all the rules. When all these above steps finished the system will furnish a learning function.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제4B권1호
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• pp.29-35
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• 2004

Locomotive scheduling in railway systems experiences many difficulties because of the complex interrelations among resources, knowledge and various constraints. Artificial intelligence technology has been applied to solve these scheduling problems. These technologies have proved to be efficient in representing knowledge and rules for complex scheduling problems. In this paper, we have applied the CSP (Constraints Satisfaction Problems) programming technique, one of the AI techniques, to solve the problems associated with locomotive scheduling. This method is more effective at solving complex scheduling problems than available mathematical programming techniques. The advanced locomotive scheduling system using the CSP programming technique is realized based on the actual timetable of the Saemaul type train on the Kyong-bu line. In this paper, an overview of the CSP programming technique is described, the modeling of domain and constraints is represented and the experimental results are compared with the real-world existing schedule. It is verified that the scheduling results by CSP programming are superior to existing scheduling performed by human experts. The executing time for locomotive scheduling is remarkably reduced to within several decade seconds, something requiring several days in the case of locomotive scheduling by human experts.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1992년도 춘계공동학술대회 발표논문 및 초록집; 울산대학교, 울산; 01월 02일 May 1992
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• pp.249-257
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• 1992

An automated machining system involves concurrent use of manufacturing resources, alternative process plans, and flexible routings. High investment in the installation of automated facilities requires an efficient scheduling system that is able to allocate the resources specified for operations over a scheduling horizon. The primary emphasis of this paper is to generate schedules that accurately reflect details of the automated environment and the objectives stated for the system. In this paper, a scheduling algorithm for automated machining is presented. Using the previous simulation research for this topic, a rule-based scheduling system is constructed. An architecture for an intelligent scheduling system is proposed, and the system has a high potential to provide efficient schedules based on the task-specific knowledge for the dynamic scheduling environment

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2490-2495
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• 2005

The scheduling of plant should be determined based on the product demands correctly forecasted by reasonable methods. However, because most existing forecasting packages need user's knowledge about forecasting, it has been hard for plant engineers without forecasting knowledge to apply forecasted demands to scheduling. Therefore, a forecasting module has been developed for plant engineers without forecasting knowledge. In this study, for the development of the forecasting module, an automatic method using the ARIMA model that is framed from the modified Box-Jenkins process is proposed. And a new method for safety inventory determination is proposed to reduce the penalty cost by forecasting errors. Finally, using the two proposed methods, the web-based automatic module has been developed.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.249-252
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• 2004

The automatic determination of vehicle operation status as well as continuous tracking of vehicle location with intelligent management is one of major elements to achieve the goals. Especially, vehicle operation status can only be analyzed in terms of expert experiences with real-time location data with scheduling information. However the scheduling information of individual vehicle is very difficult to be interpreted immediately because there are hundreds of thousand vehicles are run at the same time in the national wide range workplace. In this paper, we propose the location-based knowledge management system(LKMs) using the active trajectory analysis method with routing and scheduling information to cope with the problems. This system uses an inference technology with dispatching and geographic information to generate the logistics knowledge that can be furnished to the manager in the central vehicle monitoring and controlling center.

• 한국항행학회논문지
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• 제2권1호
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• pp.61-67
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• 1998

이 연구에서는 항공기의 주기 문제를 해결하여 주는 스케줄링 시스템과 그 조정을 위한 전문가 시스템(RACES : Ramp Activity Co-ordination Expert System)을 설계 및 개발한 내용을 기술하고 있다. RACES는 공항에서 매일 발생하는 출발편 및 도착편 항공기를 브릿지(bridge)와 스팟(spot)에 배정하기 위해 인간 전문가(human expert)로부터 습득한 해당 분야의 지식(도메인 지식) 및 휴리스틱(heuristic)을 지식 베이스로 갖고 있다. 이 RACES는 브릿지/스팟과 항공기 간에 내적 관계, 예를 들어 승객 및 공항의 그라운드 핸들링(ground handling) 등과 같은 복잡하며 동적인 제약조건 들로부터 발생하는 복잡한 스케줄링 문제를 수반한다. 매일 발생하는 600편 정도의 항공기에 대한 주기장 관리 스케줄링이 인간 전문가에 의해 수행되어졌을 경우에는 약 4~5시간이 소요되는 반면 RACES에 의해 수행되어졌을 경우에는 약 20초 정도의 시간이 소요되었고 RACES로부터 얻어진 스케줄링 결과는 해당 분야의 전문가들로부터 인정되었다. RACES는 또한 예외적인 상황이 발생했을 경우에 스케줄의 부분적인 조정을 처리하도록 설계되었다. 하루의 스케줄링이 완료된 후 항공기의 변경 및 지연 메시지는 도메인 전문가의 지식을 바탕으로 스케줄링에 반영되어 스케줄이 조정되어야 한다. 동적 재스케줄링(reactive scheduling) 단계는 도메인 전문가의 지식 모델 분석을 통해 사용자 그래픽 인터페이스의 규칙과 시나리오로써 효과적으로 나타내어진다. 항공편의 변경 및 취소로 인해 발생되는 항공기 배치의 조정은 현재 스케줄에 반영되어져야 하기 때문에 이러한 항공기 배치의 조정은 동적 재스케줄링을 위해 메인 프레임으로부터 RACES에게 통보되어져야 하며 부분적인 재스케줄링을 처리하는 것에는 불규칙적인 요소들이 많기 때문에 RACES에 의해 스케줄의 조정이 반 자동적으로 수행된다.