• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.137-141
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• 1998

본 연구는 TPN unfolding을 이용하여 WIP의 FMS(Flexible Manufacturing System)를 분석하는 방법을 제시한다. PN의 unfolding은 상태폭발이 발생하지 않는 concurrent system의 검증에 사용되는 순서기반방법이다. 본 연구는 일반적으로 발생하는 순환상태스케쥴문제에서 가장 그 작업과정 시간을 최적화함을 위하여 원래의 net을 동일한 비순환 net으로 바꾸어 줄 수 있는 unfolding 개념을 기반으로 한 것이다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.310-313
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• 1996

Mathematical programming method for finding optimal solution of job shop scheduling is inadequate to real situation because fo too much computation time. In contrast, dispatching rule is helpful for reducing compuation time but is not guaranted to find optimal solution. The purpose of this paper is to develop a new dispatching rule and procedure to minimize mean flow time whose result is near the optimal solution for job shop scheduling. First step is to select machine which have shortest finishing operation time among the schedulable operations. Second step is to select operation with regard to estimated remaining operation time. The suggested rule is compared with nondelay and MWKR rule for three examples, and is confirmed to be most effective to minimize mean flow time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1071-1076
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• 2003

Evolutionary algorithm is recognized as a promising approach to solving multi-objective combinatorial optimization problems. When no preference information of decision makers is given, multi-objective optimization problems have been commonly used to search for diverse and good Pareto optimal solution. In this paper we propose a new multi-objective evolutionary algorithm based on competitive coevolutionary algorithm, and demonstrate the applicability of the algorithm. The proposed algorithm is designed to promote both population diversity and rapidity of convergence. To achieve this, the strategies of fitness evaluation and the operation of the Pareto set are developed. The algorithm is applied to job shop scheduling problems (JSPs). The JSPs have two objectives: minimizing makespan and minimizing earliness or tardiness. The proposed algorithm is compared with existing evolutionary algorithms in terms of solution quality and diversity. The experimental results reveal the effectiveness of our approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.125-138
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• 1998

In this paper, we investigate the effect of re-entrance to the same work station in a job shop with multiple identical machines. System A is defined as the system in which re-entrance is not permitted, and system B is defined as the system in which re-entrance is permitted. By the analytical result of the queueing network, we find that the two systems have the same queue length distributions and utilizations under FIFO dispatching rule when all parameters are same. Simulation models are developed for various comparisons between the two systems, and simulation experiments are conducted for the combinations of five dispatching rules, two average workloads and two due date allowances. Five performance measures are selected for the comparison. The simulation results show that permitting re-entrance affects for some combinations of system environments.

• Korean Management Science Review
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• v.10 no.1
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• pp.171-192
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• 1993

In this paper, a rule for dispatching operations, named the Most Dissimilar Resources (MDR) dispatching rule is presented. The MDR dispatching rule has been designed to maximize utilization of resources in a resource-constrained job shop with general precedence constraints. In shown that solving the above scheduling problem with the MDR dispatching rule is equivalent to multiple solving of the maximum clique problem. A graph theoretic approach is used to model the latter problem. The pairwise counting heuristic of computational time complexity O(n$^{2}$) is developed to solve the maximum clique problem. An attempt is made to combine the MDR dispatching rule with the existing look-ahead dispatching rules. Computational experience indicates that the combined MDR dispatching rules provide solutions of better quality and consistency than the dispatching rules tested in a resource-constrained job shop.

• IE interfaces
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• v.17 no.2
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• pp.242-248
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• 2004

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots to allow the overlapping of operations between successive machines in a multi-stage production system. A new genetic algorithm (NGA) is proposed for minimizing the mean weighted absolute deviation of job completion times from due dates when jobs are scheduled in a no-wait lot-streaming flow shop. In a no-wait flow shop, each sublot must be processed continuously from its start in the first machine to its completion in the last machine without any interruption on machines and without any waiting in between the machines. NGA replaces selection and mating operators of genetic algorithms (GAs), which often lead to premature convergence, by new operators (marriage and pregnancy operators) and adopts the idea of inter-chromosomal dominance. The performance of NGA is compared with that of GA and the results of computational experiments show that NGA works well for this type of problem.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.551-558
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• 1998

In this paper, a job shop scheduling system is developed which can cope with the changes of shop floor status with flexibility. This system suggests near optimal sequence of operations by using Genetic Algorithm which considers alternative process plans. The Genetic Algorithm proposed in this paper has some characteristics. The mutation rate is differentiated in order to enhance the chance to escape a local optimum and to assure the global optimum. And it employs the double gene structure to easily make the modeling of the shop floor. Finally, the quality of its solution and the computational time are examined in comparison with the method of a Simulated Annealing.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.563-570
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• 2004

Genetic algorithms have been successfully applied to various optimization problems belonging to NP-hard problems. The sequential ordering problems(SOP) and the job shop scheduling problems(JSP) are well-known NP-hard problems with strong influence on industrial applications. Both problems share some common properties in that they have some imposed precedence constraints. When genetic algorithms are applied to this kind of problems, it is desirable for genetic operators to be designed to produce chromosomes satisfying the imposed precedence constraints. Several genetic operators applicable to such problems have been proposed. We call such genetic operators precedence-preserving genetic operators. This paper presents three existing precedence-preserving genetic operators: Precedence -Preserving Crossover(PPX), Precedence-preserving Order-based Crossover (POX), and Maximum Partial Order! Arbitrary Insertion (MPO/AI). In addition, it proposes two new operators named Precedence-Preserving Edge Recombination (PPER) and Multiple Selection Precedence-preserving Order-based Crossover (MSPOX) applicable to such problems. It compares the performance of these genetic operators for SOP and JSP in the perspective of their solution quality and execution time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.46-49
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• 1996

This paper presents a multi-pass heuristic for job shop scheduling with due dates. The proposed heuristic iteratively improves solutions based on operation due dates. To find a good solution efficiently, a method for searching the neighborhood of current schedule is developed. The heuristic is compared with two existing heuristics as well as several dispatching rules in terms of solution quality and computation time. The experimental results show that the proposed approach is promising.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.9-18
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• 2000

This paper considers the problem of two-machine, M-parallel flow shop scheduling to minimize makespan, and proposes a series of heuristic algorithms and a branch and bound algorithm. Two processing times of each job at two machines on each line are identical on any line. Since each flow-shop line consists of two machines, Johnson's sequence is optimal for each flow-shop line. Heuristic algorithms are developed in this paper by combining a "list scheduling" method and a "local search with global evaluation" method. Numerical experiments show that the proposed heuristics can efficiently give optimal or near-optimal schedules with high accuracy. with high accuracy.