• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.19-34
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• 2000

Impact of business process re-engineering requires the fundamental rethinking of how information systems are analyzed and designed. It is no longer sufficient to establish a monolithic system for fixed business environments. Information systems must be adaptive in nature. This demand is also applied in production domain. Enabling concept for the adaptive information system is reusability. This paper presents a new object-oriented analysis process for creating such reusable software components in production domain, especially for production planning and scheduling. Our process called MeCOMA is based on three meta-models: physical object meta-model, data object meta-model, and activity object meta-model. After the three meta-models are extended independently for a given production system, they are collaboratively integrated on the basis of integration pattern. The main advantages of MeCOMA are (1) to reduce software development time and (2) to consistently build reusable production software components.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.55-61
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• 1986

Mathematical forecasting models and a practical computer based forecasting system are developed for planning production in a manufacturing and distribution network. The forecasting system works at the highest level of a hierarchical computer-based decision support system consisting of the forecasting system, an aggregate planning system and a shop floor scheduling system. The dynamics of business operations for an actual company have been considered to make this study a unique comprehensive analysis of a real world forecasting problem.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.441-442
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• 2010

• Korean Management Science Review
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• v.13 no.1
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• pp.1-12
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• 1996

This paper deals with production scheduling of block assembly shop in shipbuilding company. The Objectives of the schedule are leveling of daily total workload and fair assignment of workload to each department of the shop. Optimizing the objectives, we consider some restrictions which consisit of block due dates, workability of each department, and space restriction. We formulate the problem in mathematical form. But the model is too complex to find the optimal solution (the problem is NP-complete) and we use the genetic algorithm to find a good solution.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.727-728
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• 2011

• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.105-112
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• 2015

Product Lifecycle Management (PLM) ranges from design concepts of products to disposal. In this paper, we focus on the production planning phase in PLM, which is related to process planning and production scheduling and so on. In this study, key decisions for the creation of production plans are defined as production-planning attributes. Production-planning attributes correlate complexly in production-planning problems. Traditionally, the production-planning problem splits sub-problems based on experiences, because of the complexity. In addition, the orders in which to solve each sub-problem are determined by priorities between sub-problems. However, such approaches make solution space over-restricted and make it difficult to find a better solution. We have proposed a representation of combinations of alternatives in production-planning attributes by using Zero-Suppressed Binary Decision Diagrams. The ZDD represents only feasible combinations of alternatives that satisfy constraints in the production planning. Moreover, we have developed a solution search method that solves production-planning problems with ZDDs. In this paper, we propose an approach for managing solution candidates by ZDDs' network for addressing larger production-planning problems. The network can be created by linkages of ZDDs that express constraints in individual sub-problems and between sub-problems. The benefit of this approach is that it represents solution space, satisfying whole constraints in the production planning. This case study shows that the validity of the proposed approach.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.1-22
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• 2005

This paper presents new scheduling heuristics, namely Mean Progressive Weighted Tardiness Estimator (MPWT) Heuristic Method and modified priority rules with sequence-dependent setup times consideration. These are designed to solve job shop scheduling problems with new performance measures - progressive weighted tardiness penalties. More realistic constraints, which are inter-machine overlapping sequence-dependent setup times, are considered. In real production environments, inter-machine overlapping sequence-dependent setups are significant. Therefore, modified scheduling generation algorithms of active and nondelay schedules for job shop problems with inter-machine overlapping sequence-dependent setup times are proposed in this paper. In addition, new customer-based measures of performance, which are total earliness and progressive weighted tardiness, and total progressive weighted tardiness, are proposed. The objective of the first experiment is to compare the proposed priority rules with the consideration of sequence-dependent setup times and the standard priority rules without setup times consideration. The results indicate that the proposed priority rules with setup times consideration are superior to the standard priority rules without the consideration of setup times. From the second experiment and the third experiment to compare the proposed MPWT heuristic approach with the efficient priority rules with setup times consideration, the MPWT heuristic method is significantly superior to the Batched Apparent Tardiness Cost with Sequence-dependent Setups (BATCS) rule, and other priority rules based on total earliness and progressive weighted tardiness, and total earliness and tardiness.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.209-223
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• 2020

Executions of SCM in a chemical company of which divisions produce petrochemicals, compounds, batteries, IT material and medicine directly affect their own profit. Execution level of SCM or optimization is very important. This work presents activities of SCM and optimization of inefficient issues in several industrial divisions using mathematical optimization method. The meaning is not only academic research but also making a useful tool which active partner deals with in his work. It is explained how to do beforehand and afterward optimization problem. The benefits are mentioned in the sections. The first of examples would be cover supply plan optimization, optimal profit business plan, and scheduling of a stretching process of polarizer based on minimizing raw material loss in polarizer production. The second example would be cover the optimization of production/packaging plans to maximize productivity of Poly Olefin processes, and the third example is minimization of transition loss in the production of battery electrodes. The fourth example would be cover scheduling of vessel approaching to berth. Because transportation of large portion of raw material and products of petrochemical industry is dealt with vessel, scheduling of vessel approaching to berth is important at the shore of large difference of tide. The final example would be scheduling problem to minimization of change over time of ABS semi products.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.19-33
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• 2019

Recently, manufacturers have been struggling to efficiently use production equipment as their production methods become more sophisticated and complex. Typical factors hindering the efficiency of the manufacturing process include setup cost due to job change. Especially, in the process of using expensive production equipment such as semiconductor / LCD process, efficient use of equipment is very important. Balancing the tradeoff between meeting the deadline and minimizing setup cost incurred by changes of work type is crucial planning task. In this study, we developed a scheduling model to achieve the goal of minimizing the duedate and setup costs by using reinforcement learning in parallel machines with duedate and work preparation costs. The proposed model is a Deep Q-Network (DQN) scheduling model and is a reinforcement learning-based model. To validate the effectiveness of our proposed model, we compared it against the heuristic model and DNN(deep neural network) based model. It was confirmed that our proposed DQN method causes less due date violation and setup costs than the benchmark methods.

• IE interfaces
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• v.15 no.1
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• pp.26-39
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• 2002

Two key data areas of the integrated production database in computer-integrated manufacturing (CIM) systems are the product structure in the forms of bills of material(BOM) and the process structure in the forms of routings. The great majority of existing information systems regard the BOM and routing as two separate data entities, possibly with some degree of cross-referencing. This paper proposes new information structure called the bills of material and routings(BMR) that logically integrates the BOM and routings for the CIM systems in ship production. The characteristics of ship production are described as: 1) make-to-order production type, 2) combined manufacturing principles (workshop production and construction site production), 3) significant overlapping of design, planning and manufacturing, 4) very long order throughput time, 5) complex product structure and production process. The proposed BMR systematically manages ail parts and operations data needed ship production considering characteristics of ship production. Also, the BMR situated on the integrated production database more efficiently supports interface between engineering and production functions, and integrates a wide variety of functions within production such as production planning, process planning, operation scheduling, material planning, costing etc., and simplifies information flow between sub-systems in CIM systems.