• IE interfaces
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• v.16 no.4
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• pp.450-462
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• 2003

In this paper, we deal with a process and layout design for producing a planned amount of Sikhye in a given limited time period under a reasonable production schedule. We represent a Sikhye production line as a vector N, the element of which denotes the number of tanks required in each process and our objective is to find an appropriate vector which minimizes the total investment cost. We suggest a systematic method for finding an appropriate N and an appropriate layout to N. In detail, first, we decide the required sequence of processes and the required operations for each process and we estimate standard operating times. Second, constructing a precedence diagram, we find a critical path in order to reduce the total production lead time for a batch of Sikhye. Third, given a limited N space, we manage to construct manually each production schedule using both the processing times of the critical operations and transfer times. Finally, we find an optimal vector N which gives a minimum investment cost and meets both the time constraint and quantity constraint. In addition, with the estimated relative size of each tank, we suggest an appropriate conceptual layout design including an expansion area for future demands, based on the span technique used in the field of architectural design.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.213-216
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• 1996

R'||'&'||'D project management is a process of decisions concerned with the achievement of goals of objectives. Especially, defense R'||'&'||'D project planning is the key in the successfull management of defense development. The defense project managers are constantly having to perform "what if\ulcorner" exercise, such as what if the project is extended out for an additional cost\ulcorner In this reserch, we developed a schedule-cost analysis model based upon Critical Path Method(CPM) and Venture Evaluation and Review Technique(VERT) for schedule-cost trade off analysis defense R'||'&'||'D projects. In the first step, a deterministic model is developed as a heuristic which deterministic model is developed as a heuristic which determines the schedule extension and reduction cost as a function desired schedule. In the second step, a stochastic network simulation model is developed to analyse the project risk (sucess and failure). The expected time and cost can be determined for desired schedule under the assumptions of stochastic arc data (time and cost) with a various precedence relationships. This model provides the defense R'||'&'||'D managers with an estimated and expected cost for curtailing or extending a project a given amount of time. The effectiveness and efficiency of the proposed methods, a heuristic and stochastic networks simulations, have been demonstrated through examples.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.315-327
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• 2001

This paper considers the problem of operation sequencing in nonlinear process planning, which is the problem of selecting and sequencing operations required to produce a part with the objective of minimizing the sum of operation processing costs and machine, setup and tool change costs. Main constraints are the precedence relations among operations. The problem can be decomposed into two subproblems: operation selection and operation sequencing. We suggest four simulated annealing algorithms, which solve the two subproblems iteratively until a good solution is obtained. Here, the operation selection problem can be solved using a shortest path algorithm. Application of the algorithms is illustrated using an example. Also, to show the performances of the suggested algorithms, computational experiments were done on randomly generated test problems and the results are reported. In particular, one of the suggested algorithms outperforms an existing simulated annealing algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.51-69
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• 2014

Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.678-685
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• 2002

e-business curriculum needs to be developed reflecting requirements of the stakeholder which involve a close relation with a industrial and educational job performed in the field. In this paper, we present a new methodology for developing a market-oriented and stakeholder-led e-business curriculum using quality function deployment(QFD) and conjoint analysis. For this purpose, we analyze the data resulting from the surveyed opinions of respondents working for e-business companies, the interviewed results with professors and students and evaluate the importance of each course being involved in the curriculum with respect to the job classification (e.g.. Web planner. Web master. Web programmer. Web marketer. Web designer), and then. complete a curriculm flow diagram(CFD) considering precedence and relative difficulty among the selected courses. The e-business curriculum developed by the proposed method is useful to provide guidelines for determining courses required toward a desired job and for making a partial amendment of the curriculum.

• Industrial Engineering and Management Systems
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• v.7 no.3
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• pp.204-213
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• 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 Industrial Technology
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• v.27 no.A
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• pp.69-76
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• 2007

The body of an excavator, one sort of the construction equipment, consists of mainframe part, track frame part, boom part and arm part. The all parts are manufactured in the body welding operation. The scheduling in the body welding operation of a construction equipment manufacturing is to take all the various constraints into consideration. The offset time, due date, daily capacity of operations, daily jig's capacity, precedence relation, outsourcing, alternative resource and all of the shop floor environment should be considered. An APS(Advanced Planning & Scheduling) system is a proper and efficient system in such circumstance. In this paper, we present an APS system, the optimal scheduling system for the construction equipment manufacturing specifically for the body welding operation, using ILOG Solver/Scheduler. ILOG Solver/Scheduler is a general purposed commercial software which supports to find a feasible or optimal solution using object oriented technique and constraints satisfaction programming, given constraints and objectives.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.23-35
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• 2005

This paper aims to develop a practical finite capacity MRP (FCMRP) system based on the needs of an automotive parts manufacturing company in Thailand. The approach includes a linear goal programming model to determine the optimal start time of each operation to minimize the sum of penalty points incurred by exceeding the goals of total earliness, total tardiness, and average flow-time considering the finite capacity of all work centers and precedence of operations. Important factors of the proposed FCMRP system are penalty weights and dispatching rules. Effects of these factors on the performance measures are statistically analyzed based on a real situation of an auto-part factory. Statistical results show that the dispatching rules and penalty weights have significant effects on the performance measures. The proposed FCMRP system offers a good tradeoff between conflicting performance measures and results in the best weighted average performance measures when compared to conventional forward and forward-backward finite capacity scheduling systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.95-101
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• 2022

The two-sided assembly line balancing problem is a kind of NP-hard problem. This problem primarily can be solved metaheuristic method. This paper suggests parallel clustering algorithm that each left and right-sided workstation assigned by operations with T_i = c^* ± α < c, c^* = ${\lceil}$W/m^*${\rceil}$ such that M^* = ${\lceil}$W/c${\rceil}$ for precedence diagram of two-sided assembly line with total complete time W and cycle time c. This clustering performs forward direction from left to right or reverse direction from right to left. For the 4 experimental data with 17 cycle times, the proposed algorithm can be obtain the minimum number of workstations m^* and can be reduce the cycle time to T_max < c then metaheuristic methods. Also, proposed clustering algorithm maximizes the line efficiency and minimizes the variance between workers operation times.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.117-129
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• 2008

The cost-based scheduling work has been done in both the Operations Research (OR) and Artificial Intelligence (AI) literature. To deal with more realistic problems, AI-based heuristic scheduling approach with non-regular performance measures has been studied. However, there has been little research effort to develop a full inter-job cost propagation algorithm (CPA) for different jobs having multiple downstream and upstream activities. Without such a CPA, decision-making in scheduling heuristics relies upon local, incomplete cost information, resulting in poor schedule performance from the overall cost minimizing objective. For such a purpose, we need two types of CPAs : intra-job CPA and inter-job CPA. Whenever there is a change in cost information of an activity in a job in the process of scheduling, the intra-job CPA updates cost curves of other activities connected through temporal constraints within the same job. The inter-job CPA extends cost propagation into other jobs connected through precedence relationships. By utilizing the cost information provided by CPAs, we propose cost-based scheduling heuristics that attempt to minimize the total schedule cost. This paper develops inter-job CPAs that create and update cost curves of each activity in each search state, and propagate cost information throughout a whole network of temporal constraints. Also we propose various cost-based scheduling heuristics that attempt to minimize the total schedule cost by utilizing the cost propagation algorithm.