• Korean Management Science Review
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• v.18 no.1
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• pp.147-153
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• 2001

In this paper, we Propose the Job-Pair Comparison (JPC) rule to minimize total tardiness in a single machine. For this purpose we derive conditional expression to choose the desirable job sequence. We also prove the transitivity of JPC rule, to prevent cycle and to decrease the complexity of comparison.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.269-272
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• 2001

In this paper, we presents a systematic methodology for minimizing tardiness and maximizing resource utilization in a multi-plant supply chain. A methodology is represented to a multi-objective mathematical program model. The model offers flexible and efficient multi-plant planning and scheduling. Also, We develope a realistic and flexible planning model using the genetic algorithm to solve the model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.59
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• pp.37-42
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• 2000

The parallel machines scheduling problems is one of the combinatorial optimization problems that often occurs in the real world. This problem is classified into two cases, one of which is the case which processing time are identical and the other, nonidentical. Not so much researches have been made on the case that nonidentical parallel machines scheduling problem. This study proposes Tabu Search methods for solving parallel machines scheduling problems related to due dates: minimizing mean tardiness, minimizing the number of tardy jobs, minimizing the maximum tardiness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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• pp.67-72
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• 2000

In this paper we consider to parallel machines scheduling problems for minimizing mean tardiness that is known NP-complete. This problems is classified into two cases, one of which is the case which processing time are identical and the other, nonidentical. A Tabu Search method is applied to the problems considered in this paper to get an improved solution. To this end, we design move attribute, Tabu attribute and Tabu tenure, and thereafter perform the experiments to the problems.

• 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.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.51-62
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• 2013

This paper considers a single machine rescheduling problem whose original (efficiency related) objective is minimizing makespan. We assume that disruptions such as order cancelations and newly arrived orders occur after the initial scheduling, and we reschedule this disrupted schedule with the objective of minimizing a disruption related objective while preserving the original objective. The disruption related objective measures the impact of the disruptions as difference of completion times in the remaining (uncanceled) jobs before and after the disruptions. The artificial due dates for the remaining jobs are set to completion times in the original schedule while newly arrived jobs do not have due dates. Then, the objective of the rescheduling is minimizing the maximum earliness without tardiness. In order to preserve the optimality of the original objective, we assume that no-idle time and no tardiness are allowed while rescheduling. We first define this new problem and prove that the general version of the problem is unary NP-complete. Then, we develop three simple but intuitive heuristics. For each of the three heuristics, we find a tight bound on the measure called modified z-approximation ratio. The best theoretical bound is found to be 0.5 - ${\varepsilon}$ for some ${\varepsilon}$ > 0, and it implies that the solution value of the best heuristic is at most around a half of the worst possible solution value. Finally, we empirically evaluate the heuristics and demonstrate that the two best heuristics perform much better than the other one.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.823-835
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• 2019

Purpose: This study deals with a production planning and scheduling problem to minimize the total weighted tardiness on hybrid flow shop with sets of non-identical parallel machines on stages, where parallel machines in the set are dedicated to perform specific subsets of jobs and sequence-dependent setup times are also considered. Methods: A two-stage approach, that applies MILP model in the 1st stage and dispatching rules in the 2nd stage, is proposed in this paper. The MILP model is used to assign jobs to a specific machine in order to equalize the workload of the machines at each stage, while new dispatching rules are proposed and applied to sequence jobs in the queue at each stage. Results: The proposed two-stage approach was implemented by using a commercial MILP solver and a commercial simulation software and a case study was developed based on the spark plug manufacturing process, which is an automotive component, and verified using the company's actual production history. The computational experiment shows that it can reduce the tardiness when used in conjunction with the dispatching rule. Conclusion: This proposed two-stage approach can be used for HFS systems with dedicated machines, which can be evaluated in terms of tardiness and makespan. The method is expected to be used for the aggregated production planning or shop floor-level production scheduling.

• Journal of the Korea Safety Management & Science
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• v.25 no.4
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• pp.131-140
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• 2023

This paper proposes an algorithm for the Unrelated Parallel Machine Scheduling Problem(UPMSP) without setup times, aiming to minimize total tardiness. As an NP-hard problem, the UPMSP is hard to get an optimal solution. Consequently, practical scenarios are solved by relying on operator's experiences or simple heuristic approaches. The proposed algorithm has adapted two methods: a policy network method, based on Transformer to compute the correlation between individual jobs and machines, and another method to train the network with a reinforcement learning algorithm based on the REINFORCE with Baseline algorithm. The proposed algorithm was evaluated on randomly generated problems and the results were compared with those obtained using CPLEX, as well as three scheduling algorithms. This paper confirms that the proposed algorithm outperforms the comparison algorithms, as evidenced by the test results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.335-335
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• 1999

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.699-708
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• 1997

Production control is usually composed of due-dote assignment, production input control, and priority dispatching rule. A production input control(PIC) is mainly to control the WIP level on the shop floor. On the other hand, a priority dispatching rule(PDR) is mainly to control the tardiness/earliness of on order and number of tardy jobs. Therefore, if we select a particular PIC which can control only a particular performance measure(i.e., tardiness), it may cause worsening other performance measure(i.e., WIP level, shopfloor time, etc.) This newly developed production input control, DRD(Dual Release-Dates), is mainly designed to control the WIP level on the shop floor by employing two different release-dates of an order(earliest release. date and latest release-date and the release condition (relationship between the current WIP level and the pre-defined maximum WIP level) while trying to meet the due-date of the order.