• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.1
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.38
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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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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• v.4B no.1
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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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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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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• 2005.06a
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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.

• Proceedings of the KSRS Conference
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• 2004.10a
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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.

• Journal of Advanced Navigation Technology
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• v.2 no.1
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• pp.61-67
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• 1998

In this paper, we have described the Ramp Activity Coordination Expert System (RACES) which can solve aircraft parking problems. RACES includes a knowledge-based scheduling problem which assigns every daily arriving and departing flight to the gates and remote spots with the domain specific knowledge and heuristics acquired from human experts. RACES processes complex scheduling problem such as dynamic inter-relations among the characteristics of remote spots/gates and aircraft with various other constraints, for example, custome and ground handling factors at an airport. By user-driven modeling for end users and knowledge-driven near optimal scheduling acquired from human experts, RACES can produce parking schedules of aircraft in about 20 seconds for about 400 daily flights, whereas it normally takes about 4 to 5 hours by human experts. Scheduling results in the form of Gantt charts produced by the RACES are also accepted by the domain experts. RACES is also designed to deal with the partial adjustment of the schedule when unexpected events occur. After daily scheduling is completed, the messages for aircraft changes and delay messages are reflected and updated into the schedule according to the knowledge of the domain experts. By analyzing the knowledge model of the domain expert, the reactive scheduling steps are effectively represented as rules and the scenarios of the Graphic User Interfaces (GUI) are designed. Since the modification of the aircraft dispositions such as aircraft changes and cancellations of flights are reflected to the current schedule, the modification should be notified to RACES from the mainframe for the reactive scheduling. The adjustments of the schedule are made semi-automatically by RACES since there are many irregularities in dealing with the partial rescheduling.