• Journal of Korean Institute of Industrial Engineers
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• v.34 no.4
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• pp.481-488
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• 2008

This paper deals with a scheduling problem for two-machine flow shop, in which the preceding machine is a batch processing machine that can process a number of jobs simultaneously. To minimize makespan of the system, we present a mixed integer linear programming formulation for the problem, and using this formulation, it is shown that an optimal solution for small problem can be obtained by a commercial optimization software. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms including genetic algorithm to solve practical big-sized problems in a reasonable computational time. To verify performances of the algorithms, we compare them with lower bound for the problem. From the results of these computational experiments, some of the heuristic algorithms show very good performances for the problem.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.4
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• pp.257-265
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• 2009

This paper considers the hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. In hybrid flow shops, each job is processed through multiple production stages in series, each of which has multiple identical parallel machines. The problem is to determine the allocation of jobs to the parallel machines at each stage as well as the sequence of the jobs assigned to each machine. Due to the complexity of the problem, we suggest search heuristics, tabu search and simulated annealing algorithms with a new method to generate neighborhood solutions. In particular, to evaluate and select neighborhood solutions, three surrogate objectives are additionally suggested because not much difference in the number of tardy jobs can be found among the neighborhoods. To test the performances of the surrogate objective based search heuristics, computational experiments were performed on a number of test instances and the results show that the surrogate objective based search heuristics were better than the original ones. Also, they gave the optimal solutions for most small-size test instances.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.3
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• pp.173-181
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• 2016

This paper deals with the single-hoist and multiple-products scheduling problem. Although a mixed integer linear programming model for the problem was developed earlier, a branch-and-bound based heuristic algorithm is proposed in this paper to solve the big-size problems in real situation. The algorithm is capable of handling problems incorporating different product types, jobs in the process, and tank capacities. Using a small example problem the procedure of the heuristic algorithm is explained. To assess the performance of the heuristic we generate a bigger example problem and compare the results of the algorithm proposed in this paper with the optimal solutions derived from the mathematical model of earlier research. The comparison shows that the heuristic has very good performance and the computation time is sufficiently short to use the algorithm in real situation.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.3
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• pp.373-380
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• 2007

In this paper, we consider a new lot-sizing and scheduling problem (LSSP) that minimizes the sum of production cost, setup cost and inventory cost. Setup carry-over and overlapping as well as demand splitting are considered. Also, maximum number of setups for each time period is not limited. For this LSSP, we have formulated a mixed integer programming (MIP) model, of which the size does not increase even if we divide a time period into a number of micro time periods. Also, we have developed an efficient heuristic algorithm by combining decomposition scheme with local search procedure. Test results show that the developed heuristic algorithm finds good quality (in practice, even better) feasible solutions using far less computation time compared with the CPLEX, a competitive MIP solver.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• IE interfaces
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• v.9 no.2
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• pp.159-171
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• 1996

In recent, most'of automobile industries adopt the concept of JIT production. According to the concept, the paternal manufacturers require their suppliers to supply parts just in time, so it is very important for a subcontractor to produce the parts in the appointed time of delivery. This study develops a production information system for Woosin Industry, which is one of such subcontractors of Hyundai Motors Company(HMC). The system is developed for use in Client/Server environment. In this study, the following three modules are developed, i. e., the production scheduling module, the sales management module to deal with the information about orders of HMC, delivery of parts, and bill collecting, and the module for supporting material requirements planning and purchase of material.

• IE interfaces
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• v.7 no.2
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• pp.75-85
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• 1994

This paper presents a production scheduling model in a block assembly shop in shipbuilding industry. In a block assembly shop, the most important performance criterion is load leveling, which balances manpower and work area utilization through the planning horizon. The problem is formulated as a mixed-integer nonlinear programming(MINLP) problem of which objective function is to optimize load leveling. The developed MINLP problem can not be solvable due to computational complexity. The MINLP problem is decomposed into two stage mixed-integer linear programming (MILP) problems to obtain a good solution, but the decomposed MILP problems are still computationally intractable because of combinatorial complexity. Therfore, a heuristic method using linear programming is proposed to solve two stage MILP problems sequentially. The proposed heuristic generates a good production schedule within a reasonable computation time, and it is easily applicable for establishing the production schedule in a block assembly shop in shipbuilding industry.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.194-196
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• 1989

The production costing plays a key role in power system expansion and operations planning especially for the calculation of expected energy, loss of load probability and unserved energy. Therefore, it is crucial to develope a probabilistic production costing algorithm which gives sufficiently precise results within a reasonable computational time. In this respect, a number of methods of solving production simulation have been proposed. In previous paper we proposed the method used Fast Hartley Transform in convolution process with considering only the thermal units. In this paper, the method considering the scheduling of pumped-storage plants and hydro plants with energy constraints is proposed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.537-541
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• 2003

The synchronization of production-delivery artivities is one of crucial factors to get competitive collaboration benefits between the manufacturer and the retailor(s). There were several researches to study on He optimal delivery policy to minimize the total cost of integrated system of both manufacturer and retailor(s). In this research, we investigate the joint optimal shipment policy in case that a manufacturer produces multiple products sharing a single production facility in the manufacturer side and retailor(s) deploys JIT delivery pattern with equal-size shipment policy. We formulate this problem as a form of 'Common Cycle Approach' in classical ELSP (Economic Lot Scheduling Problem) and provide simple optimal solution procedure.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.61-61
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• 2000

Since FMS(Flexible Manufacturing System) such as semiconductor production systems have the characteristic that each device has to be commonly used in several stages, it is difficult to find an optimal solution. In this paper, we proposed the new algorithm which can get the optimal ratio of sharing resources. We will implement the proposed algorithm to semiconductor production systems. We introduce the optimal algorithm, which is modeled and analyzed by ExSpect, a petri net based simulation tool. When there exist conflicts of sharing resources, the scheduling method is adopted, which gives a priority to the most preceded process. The suggested algorithm can be used not only in semiconductor production systems but also in various FMS.