• Korean Management Science Review
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• v.17 no.1
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• pp.41-54
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• 2000

in the real world situations that some jobs need be processed only on certain limited machines frequently occur due to the capacity restrictions of machines such as tools fixtures or material handling equipment. In this paper we consider n-job non-preemptive and m parallel machines scheduling problem having two machines group. The objective function is to minimize the sum of earliness and tardiness with different release times and due dates. The problem is formulated as a mixed integer programming problem. The problem is proved to be Np-complete. Thus a heuristic is developed to solve this problem. To illustrate its suitability and efficiency a proposed heuristic is compared with a genetic algorithm and tabu search for a large number of randomly generated test problems in ship engine assembly shop. Through the experimental results it is showed that the proposed algorithm yields good solutions efficiently.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.3
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• pp.81-89
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• 2000

The real-time multicast problem is to construct a multicast tree starting from a source node and including multiple destination nodes and that has minimum network cost with delay constraints. It is known that to find a tree of the minimum network cost is the Steiner Tree problem which is NP-complete. In this paper, we propose a genetic algorithm to solve the multicast tree with minimum network cost and the delay constraints. The computational results obtained by comparing an existing algorithm. Kompella algorithm, and the proposed algorithm show that our algorithm tends to find lower network cost on the average than Kompella algorithm does.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.328-338
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• 1994

TSP(Traveling Salesman Problem) is a famous problem in Operations Research fields due to its applicability to various problems. It is also well-known that the problem is hard to solve in reasonable time, since it is in the NP-Complete class. Hence it is desired to develop heuristics which have polynominal complexity and also solve the problem to near-optimality. This paper presents a heuristic algorithm for TSP using the concept of dynamic programming. The proposed method has the complexity of O(N$\^$3/), and gives improved solutions than other well-known algorithms in our extensive computational experiments.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.307-309
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• 1996

This paper analyses a deterministic scheduling problem concerned with manufacturing two types of components at a pre-assembly stage which consists of two independent feeding machines each producing its own type of component. Each type represents a unique component which may have variations in its size or quality. Therefore, the completion time of each component depends on both its type and quality (size) variations. Such manufactured components are subsequently assembled into various component dependent products. The problem has the objective measure of minimizing the total weighted completion time of a finite number of jobs(products) where the completion time of each job is measured by the latest completion time of its two components at the pre-assembly stage. The problem is shown to be NP-complete in the strong sense. A WSPT rule coupled with a machine-aggregation idea is developed for good heuristics which show the error bound of 2.

• IE interfaces
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• v.11 no.3
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• pp.1-13
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• 1998

This paper addresses a scheduling scheme for Flexible Flow Shop(FFS) in the case that a factory is a sub-plant of an electronic device manufacturing plant. Under this environment, job orders for the sub-plants in the production route are generated together with job processing time bucket when the customer places orders for final product. The processing time bucket for each job is a duration from possible release date to permissible due date. A sub-plant modeled FFS should schedule these jobs orders within time bucket. Viewing a Printed Circuit Board(PCB) assembly line as a FFS, the developed scheme schedules an incoming order along with the orders already placed on the scheduled. The scheme consists of the four steps, 1)assigning operation release date and due date to each work cells in the FFS, 2)job grouping, 3)dispatching and 4)machine allocation. Since the FFS scheduling problem is NP-complete, the logics used are heuristic. Using a real case, we tested the scheme and compared it with the John's algorithm and other dispatching rules.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.4
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• pp.285-293
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• 2004

In this study, a machine grouping problem for the formation of manufacturing cells is considered. We constructed the problem as minimizing manufacturing leadtime consisting of parts' processing, moving, and waiting time. Specifically, the main objective of the defined problem is established as minimizing inter-cell traffic in order to minimize the part's moving time. In addition, to reduce the waiting time of parts, the load balance among cells is implicitly included as constraints. Since this problem is well known as NP-complete and cannot be solved in polynomial time, a genetic algorithm is implemented to obtain solutions. Also, a local optimization algorithm is applied in order to improve the solution by the genetic algorithm. Several experiments show that the suggested algorithms guarantee near optimal solutions in a few seconds.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.29-41
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• 1998

This paper addresses the problem of scheduling a set of jobs with a common due data on a single machine. The objective is to minimize the sum of the earliness and tardiness of jobs subject to $T_{max}{\le}{\Delta}\;for{\Delta}{\ge}0$. Properties for the $MAD/T_{max}$ problem are found and the problem is shown to be NP-complete in the ordinary sense. According to the range of Δ, the problem can be solved in polynomial time. Also, some special cases where an optimal schedule is found in polynomial time are discussed.

• Korean Management Science Review
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• v.14 no.2
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• pp.47-61
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• 1997

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• Korean Management Science Review
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• v.12 no.2
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• pp.77-87
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• 1995

In this paper we develop an efficient heuristic algorithm for the problem of scheduling n sequence-dependent jobs on a basic processor to minimize makespan. Efficient solution methods are already known for the sequence- independent case. But for the sequence-dependent case, this problem belongs to a set of strong NP-complete problems. We present a heuristic which is similar to shortest setup time heuristic but opportunity cost of setup time rather than shortest setup time is used for choosing next job. This heuristic algorithm has same computational complexity and worst case ratio as the shortest setup time heuristic. We used Wilcoxon signed rank test to show that our heuristic is superior to nearest setup time heuristic in term of average behavior.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.27-37
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• 2005

The rectilinear Steiner tree problem (RSTP) is to find a minimum-length rectilinear interconnection of a set of terminals in the plane. It is well known that the solution to this problem will be the minimal spanning tree (MST) on some set Steiner points. The RSTP is known to be NP-complete. The RSTP has received a lot of attention in the literature and heuristic and optimal algorithms have been proposed, A key performance measure of the algorithm for the RSTP is the reduction rate that is achieved by the difference between the objective value of the RSTP and that of the MST without Steiner points. A hybrid evolution strategy on RSTP based upon the Prim algorithm was presented. The computational results show that the evolution strategy is better than the previously proposed other heuristic. The average reduction rate of solutions from the evolution strategy is about 11%, which is almost similar to that of optimal solutions.