• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.21-31
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• 1992

Today, manpower is used as a major factor in the production of service industry that is on the increasing trend. So the research on manpower structure have been regarded as a important field. The research on personnel scheduling, however, have got de-emphasizeed as compared wi th the field of manufacturing scheduling. As the increasing trend of service industry, an appropriate personnel scheduling for each type of service industry is needed urgently. This paper deals with nurse scheduling problem in the field of personnel scheduling. We aim at developing prototype of the expert nurse scheduling program which is easy to use when scheduling without help of expert, and satisfies the various request of hospital scheduling policy and nurse scheduling.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.506-512
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• 2006

In this paper, we propose a lot-sizing and scheduling problem that seeks to minimize the sum of production cost and inventory cost over a given planning horizon while considering idle state of a machine in a batch production system. For this problem, we develop an integer programming model. And, we develop an efficient 2-phase heuristic algorithm to find a high quality feasible solution within reasonable time bounds. In the first phase, we seek to minimize the production cost by assigning batches to machines. Then, in the second phase, we find a production sequence of batches that reduces the inventory cost, while considering adding or deleting idle states between batches. Computational results show that the developed heuristic algorithm finds excellent feasible solutions within reasonable time bounds. Also, we could significantly reduce the total cost consisting of production cost and inventory cost by using the developed heuristic algorithm at a real manufacturing system that produces zinc alloys.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.3
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• pp.202-211
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• 2023

The profile shops in shipyards produce section steels required for block production of ships. Due to the limitations of shipyard's production capacity, a considerable amount of work is already outsourced. In addition, the need to improve the productivity of the profile shops is growing because the production volume is expected to increase due to the recent boom in the shipbuilding industry. In this study, a scheduling optimization was conducted for a parallel welding line of the profile process, with the aim of minimizing tardiness and the number of set-up changes as objective functions to achieve productivity improvements. In particular, this study applied a dynamic scheduling method to determine the job sequence considering variability of processing time. A Markov decision process model was proposed for the job sequence problem, considering the trade-off relationship between two objective functions. Deep reinforcement learning was also used to learn the optimal scheduling policy. The developed algorithm was evaluated by comparing its performance with priority rules (SSPT, ATCS, MDD, COVERT rule) in test scenarios constructed by the sampling data. As a result, the proposed scheduling algorithms outperformed than the priority rules in terms of set-up ratio, tardiness, and makespan.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.36-45
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• 1993

This paper presents a model and a heuristic procedure to design production planning and transportation scheduling systems of critical items, components and products on the basis of material requirement planning concept and transportation planning model. These systems are stemmed from a multi-site multi-product production company in a international economic zone. An example is provided to validate the heuristic procedure developed.

• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.1-12
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• 1996

Utilizing the simulation approaches, the dynamic production scheduling system FOLS(Flexible flowshop On-Line Simulation) is developed under the flexible flowshop environment. When an interruption such as machine failure/recovery is occurred at the shop floor, the FOLS system performs evaluations for job selection rule oriented alternatives, and generates a dynamic production schedule based on the collected current shop floor data. For the case study, the FOLS system is applied to the printed circuit card assembly(PCCA) line and simulation results are reported.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.106-110
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• 1998

Some successful scheduling systems are based on simulation technique. However, they are relatively expensive. A cost-effective solution is to apply general-purpose simulation software to the scheduling system. This paper is concerned with developing a simulation model for MPS(Master Production Schedule) evaluation system. The target system of the model is PC production system. AIM (Analyzer for Improving Manufacturing system), a well-known manufacturing oriented simulation software, is used for modeling tool. Eventually, the model can be included into an MPS evaluation system as simulation module and a scheduler can use the system to verify the MPS in advance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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• pp.165-171
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• 1993

The algorithm of Giffler and Thompson is modified for solving job shop scheduling problems related to the overlapping production The start-lag and stop-lag are also applied New priority rules for the overlapping production are proposed and the efficiency of them is compared with that of the classical priority rules.

• The Journal of Engineering Research
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• v.4 no.1
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• pp.35-45
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• 2002

This paper deals with the scheduling problems which are the most important subtask in High-Level Synthesis. Especially, we have concentrated our attentions on the data-path scheduling which can get the structural informations from the behavioral algorithm as a first step in synthesis procedure. Suggest Forward scheduling methode is executed the ASAP and ALAP scheduling to use the fifth – order elliptic wave filter of a standard benchmark model, and then it is drawing up T.N matrix table by the number of resource and control-step, using the table extract of the simple than down-limit value of the control-step for the number of given resource to use this table. All of existing list scheduling techniques determine the priority functions first, and then do the operation scheduling, But, the suggested forward scheduling technique does the schedule first, and determines the priority functions if needed in scheduling process.

• 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.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.1-9
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• 2014

In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.