• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.3
• /
• pp.184-191
• /
• 2010

In the environment of 450mm wafers production known as the next-generation semiconductor production process, one of the most significant features is the full automation over the whole manufacturing processes involved. The full automation system for 450mm wafer production will minimize the human workers' involvement in the manufacturing process as much as possible. In addition, since the importance of an individual wafer processing increases noticeably, it is necessary to develop more robust scheduling systems in the whole manufacturing process than so ever. The scheduling systems for the next-generation semiconductor production processes also should be capable of monitoring individual wafers and collecting useful data on them in real time. Based on the information gathered from these processes, the system should finally have a real-time scheduling functions controlling whole the semiconductor manufacturing processes. In this study, preliminary investigations on the requirements and needed functions for constructing the real time scheduling system and transforming manufacturing environments for 300mm wafers to those of 400mm are conducted and through which the next generation semiconductor processes for efficient scheduling in a clustered production system architecture of the scheduler is proposed. Our scheduling architecture is composed of the modules for real-time scheduling, the clustered production type supporting, the optimal scheduling and so on. The specifications of modules to define the major required functions, capabilities, and the relationship between them are presented.

• The Journal of Society for e-Business Studies
• /
• v.17 no.1
• /
• pp.53-74
• /
• 2012

This study developed a method for selection of available human resources for incomingjob allocation that considers factors affecting resource performance in the business process management (BPM) environment. For many years, resource selection has been treated as a very important issue in scheduling due to its direct influence on the speed and quality of task accomplishment. Even though traditional resource selection can work well in many situations, it might not be the best choice when dealing with human resources. Humanresource performance is easily affected by several factors such as workload, queue, working hours, inter-arrival time, and others. The resource-selection rule developed in the present study considers factors that affect human resource performance. We used a Bayesian Network (BN) to incorporate those factors into a single model, which we have called the Bayesian Selection Rule (BSR). Our simulation results show that the BSR can reduce waiting time, completion time and cycle time.

• Management Science and Financial Engineering
• /
• v.11 no.2
• /
• pp.19-43
• /
• 2005

This paper considers a variation of the customer order scheduling problem, and the variation is the case where the machine-job assignment is fixed. We examine the parallel machine environment, and the objective is to minimize the sum of the completion times of the batches. While a machine can process only one job at a time, different machines can simultaneously process different jobs in a batch. The recognition version of this problem is known to be NP-complete in the strong sense even if there exist only two parallel machines. When there are an arbitrary number of parallel machines, we establish three lower bounds and develop a dynamic programming (DP) algorithm which runs in exponential time on the number of batches. We present two simple but intuitive heuristics, SB and GR, and find some special cases where SB and GR generate an optimal schedule. We also find worst case upper bounds on the relative error. For the case of the two parallel machines, we show that GR generates an optimal schedule when processing times of all batches are equal. Finally, the heuristics and the lower bounds are empirically evaluated.

• Management Science and Financial Engineering
• /
• v.15 no.2
• /
• pp.69-100
• /
• 2009

In this paper, we consider a relatively new two-machine flow shop scheduling problem where the unit time WIP cost increases as a job passes through various stages in the production process, and the objective is to minimize the total WIP (work-in-process) cost. Specifically, we study three special cases of the problem. First, we consider the problem where processing times on machine 1 are identical. Second, the problem with identical processing times on machine 2 is examined. The recognition version of the both problems is unary NP-complete (or NP-complete in strong sense). For each problem, we suggest two simple and intuitive heuristics and find the worst case bound on relative error. Third, we consider the problem where the processing time of a job on each machine is proportional to a base processing time. For this problem, we show that a known heuristic finds an optimal schedule.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.2
• /
• pp.9-17
• /
• 2019

In order to deal with high uncertainty and variability in emergency medical centers, many researchers have developed various models for their operational planning and scheduling. However, most of the models just provide static plans without any risk measures as their results, and thus the users often lose the opportunity to analyze how much risk the patients have, whether the plan is still implementable or how the plan should be changed when an unexpected event happens. In this study, we construct a simulation model combined with a risk-based planning and scheduling module designed by Simio LLC. In addition to static schedules, it provides possibility of treatment delay for each patient as a risk measure, and updates the schedule to avoid the risk when it is needed. By using the simulation model, the users can experiment various scenarios in operations quickly, and also can make a decision not based on their past experience or intuition but based on scientific estimation of risks even in urgent situations. An example of such an operational decision making process is demonstrated for a real mid-size emergency medical center located in Seoul, Republic of Korea. The model is designed for temporal short-term planning especially, but it can be expanded for long-term planning also with some appropriate adjustments.

• Journal of Intelligence and Information Systems
• /
• v.15 no.4
• /
• pp.213-224
• /
• 2009

This paper describes a real-time scheduling system re-construction project for automated manufacturing at a 300mm wafer fab of Korean semiconductor manufacturing company. During executing this project, for each main operation such as clean, diffusion, deposition, photolithography, and metallization, each adopted scheduling algorithm was developed, and then those were implemented in a real-time scheduling system. In this paper, we focus on the scheduling algorithms and real-time scheduling system for clean and diffusion operations, that is, a serial-process block with the constraint of limited queue time and batch processors. After this project was completed, the automated manufacturing utilizations of clean and diffusion operations became around 91% and 83% respectively, which were about 50% and 10% at the beginning of this project. The automated manufacturing system reduces direct operating costs, increased throughput on the equipments, and suggests continuous and uninterrupted processings.

• Industrial Engineering and Management Systems
• /
• v.7 no.3
• /
• pp.204-213
• /
• 2008

Companies that produce capital goods need to schedule the production of products that have complex product structures with components that require many operations on different machines. A feasible schedule must satisfy operation and assembly precedence constraints. It is also important to avoid deadlock situations. In this paper a Genetic Algorithm (GA) has been developed that includes a new repair process that rectifies infeasible schedules that are produced during the evolution process. The algorithm was designed to minimise the combination of earliness and tardiness penalties and took into account finite capacity constraints. Three different sized problems were obtained from a collaborating capital goods company. A design of experimental approach was used to systematically identify that the best genetic operators and GA parameters for each size of problem.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.4
• /
• pp.415-424
• /
• 2002

Semiconductor manufacturing has been emerged as a highly competitive but profitable business. Accordingly it becomes very important for semiconductor manufacturing companies to meet customer demands at the right time, in order to keep the leading edge in the world market. However, due-date oriented production is very difficult task because of the complex job flows with highly resource conflicts in fabrication shop called FAB. Due to its cyclic manufacturing feature of products, to be completed, a semiconductor product is processed repeatedly as many times as the number of the product manufacturing cycles in FAB, and FAB processes of individual manufacturing cycles are composed with similar but not identical unit processes. In this paper, we propose a production scheduling and control scheme that is designed specifically for semiconductor scheduling environment (FAB). The proposed scheme consists of three modules: simulation module, cycle due-date estimation module, and dispatching module. The fundamental idea of the scheduler is to introduce the due-date for each cycle of job, with which the complex job flows in FAB can be controlled through a simple scheduling rule such as the minimum slack rule, such that the customer due-dates are maximally satisfied. Through detailed simulation, the performance of a cycle due-date based scheduler has been verified.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.5
• /
• pp.1864-1873
• /
• 2018

Energy shifting is an innovative method used to obtain the highest profit from the operation of energy storage systems (ESS) by controlling the charge and discharge schedules according to the electricity prices in a given period. Therefore, in this study, we propose an optimal charge and discharge scheduling method that performs energy shift operations derived from an ESS efficiency model. The efficiency model reflects the construction of power conversion systems (PCSs) and lithium battery systems (LBSs) according to the rated discharge time of a MWh-scale ESS. The PCS model was based on measurement data from a real system, whereas for the LBS, we used a circuit model that is appropriate for the MWh scale. In addition, this paper presents the application of a genetic algorithm to obtain the optimal charge and discharge schedules. This development represents a novel evolutionary computation method and aims to find an optimal solution that does not modify the total energy volume for the scheduling process. This optimal charge and discharge scheduling method was verified by various case studies, while the model was used to realize a higher profit than that realized using other scheduling methods.

• Journal of Intelligence and Information Systems
• /
• v.13 no.3
• /
• pp.63-82
• /
• 2007

In this paper, we propose an adaptive hybrid genetic algorithm (ahGA) approach for effectively solving multistage-based scheduling problems in flexible manufacturing system (FMS) environment. The proposed ahGA uses a neighborhood search technique for local search and an adaptive scheme for regulation of GA parameters in order to improve the solution of FMS scheduling problem and to enhance the performance of genetic search process, respectively. In numerical experiment, we present two types of multistage-based scheduling problems to compare the performances of the proposed ahGA with conventional competing algorithms. Experimental results show that the proposed ahGA outperforms the conventional algorithms.