• Proceedings of the Korean Society for Quality Management Conference
• /
• 2009.10a
• /
• pp.131-135
• /
• 2009

This paper considers the sequencing of products in car assembly lines. The sequence which minimizes overall utility work in car assembly lines reduce the cycle time and the risk of conveyor stopping. The sequencing problem is solved using Tabu Search. Tabu Search is a heuristic method which can provide a near optimal solution in real time. The performance of proposed technique is compared with existing heuristic methods in terms of solution quality and computation time. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.5 no.2
• /
• pp.87-97
• /
• 1999

This study is concerned with developing a hybrid heuristic algorithm for solving the redundancy optimization problem which is very important in system safety, This study develops a HH(Hybrid Heuristic) method combined with two strategies to alleviate the risks of being trapped at a local optimum. One of them is to construct the populations of the initial solutions randomly. The other is the additional search with SA(Simulated Annealing) method in final step. Computational results indicate that HH performs consistently better than the KY method proposed in Kim[8]. Therefore, the proposed HH is believed to an attractive to other heuristic methods.

• Journal of Korea Game Society
• /
• v.15 no.4
• /
• pp.179-188
• /
• 2015

The A* algorithm is a well-known pathfinding algorithm. However, if the information about obstacles is not exploited, the algorithm may collide with obstacles or lead into swamp areas unnecessarily. In this paper, we propose new heuristic functions using the information of obstacles to avoid them or swamp areas. It takes time to process the information of obstacles before starting pathfinding, but it may not cause any problems most of cases because it is not processed in real time. We showed that the proposed methods could reduce the search space effectively through experiments. Furthermore, we showed that heuristic functions using obstacle information could reduce the search space effectively without processing obstacle information at all.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.1
• /
• pp.206-209
• /
• 2005

Optimal path search systems to take continuously changed traffic flows into consideration is necessary in order to reduce the cost to get destination. However, to search optimal path in client terminals with low computing power yields high computational cost. Thus, a method with low cost and near optimal path as well is required. In this paper, we propose a path search method using variable heuristic for the sake of reducing operation time. The heuristic is determined by the change of the average speeds of cars located in grid which means a rectangle region.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.3
• /
• pp.401-406
• /
• 2011

In order to solve a complex optimization problem more efficiently than traditional approaches, various meta-heuristic algorithms such as genetic algorithm, ant-colony algorithm, and harmony search algorithm have been extensively researched. Compared with other meta-heuristic algorithm, harmony search algorithm shows a better result to resolve the complex optimization issues. Harmony search algorithm is inspired by the improvision process of musician for most suitable harmony. In general, the performance of harmony search algorithm is determined by the value of harmony memory considering rate, and pitch adjust rate. In this paper, modified harmony search algorithm is proposed in order to derive best harmony. If the optimal solution of a specific problem can not be found for a certain period of time, a part of original harmony memory is updated as the selected suitable harmonies. Experimental results using test function demonstrate that the updated harmony memory can induce the approximation of reliable optimal solution in the short iteration, because of a few change of fitness.

• Management Science and Financial Engineering
• /
• v.3 no.1
• /
• pp.75-88
• /
• 1997

Tabu search (TS) has been successfully applied for solving many complex combinatorial optimization problems in the areas of operations research and production control. However, TS is for single-objective problems in its present form. In this article, a TS-based heuristic is developed to determine Pareto-efficient solutions to a multi-objective combinatorial optimization problem. The developed algorithm is then applied to the discrete optimal design problem in statistics to demonstrate its usefulness.

• Industrial Engineering and Management Systems
• /
• v.11 no.2
• /
• pp.170-175
• /
• 2012

In this paper, a procedure using a genetic algorithm (GA) and a heuristic local search (HLS) is proposed for solving facility rearrangement problem (FRP). FRP is a decision problem for stopping/running of facilities and integration of stopped facilities to running facilities to maximize the production capacity of running facilities under the cost constraint. FRP is formulated as an integer programming model for maximizing the total production capacity under the constraint of the total facility operating cost. In the cases of 90 percent of cost constraint and more than 20 facilities, the previous solving method was not effective. To find effective alternatives, this solving procedure using a GA and a HLS is developed. Stopping/running of facilities are searched by GA. The shifting the production operation of stopped facilities into running facilities is searched by HLS, and this local search is executed for one individual in this GA procedure. The effectiveness of the proposed procedure using a GA and HLS is demonstrated by numerical experiment.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.10
• /
• pp.2606-2615
• /
• 1994

Sorting time is one of the most important issues for auto IC test handling systems. In actual system, because of too much path, reducing the computing time for finding a sorting path is the key way to enhancing the system performance. The exhaustive path search technique can not be used for real systems. This paper proposes heuristic sorting algorithm to find the minimal sorting time. The suggested algorithm is basically based on the best-first search technique and multi-level search technique. The results are close to the optimal solutions and computing time is greately reduced also. Therefore the proposed algorthm can be effectively used for real-time sorting process in auto IC test handling systems.

• The KIPS Transactions:PartB
• /
• v.16B no.5
• /
• pp.403-410
• /
• 2009

The two-dimensional bin packing problem(2D-BPP) has been known to be NP-hard, and it is difficult to solve the problem exactly. Many approximation methods, such as genetic algorithm, simulated annealing and tabu search etc, have been also proposed to gain better solutions. However, the existing approximation algorithms, such as branch-and-bound and tabu search, have shown the low efficiency and the long execution time due to a large of iterations. To solve these problems, we first define the fitness function to simplify and increase the utility of algorithm. The function decides whether an item is packed into a given area, and as an important information for a packing strategy, the number of subarea that can accommodate a given item is obtained from the variant of the fitness function. Then we present a heuristic algorithm BF for 2D bin packing, constructed by the fitness function and subarea. Finally, the effectiveness of the proposed algorithm will be expressed by the comparison experiments with the heuristic and the metaheuristic of the literatures. As comparing with existing heuristic algorithms and metaheuristic algorithms, it has been found that the packing rate of algorithm BP is the same as 97% as existing heuristic algorithms, FFF and FBS, or better than them. Also, it has been shown the same as 86% as tabu search algorithm or better.

• Journal of KIISE:Software and Applications
• /
• v.30 no.9
• /
• pp.862-866
• /
• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.