Earticle

글로벌 경제 침체 속에서 기업은 날로 높아져 가는 소비자들의 수요를 만족하기 위하여 납기 대응 그리고 LB(Line Balance, 라인편성효율) 향상과 제조원가의 절감을 위한 생산성 향상은 중요한 개선 항목이다. 따라서 본 연구에서는 자동차 물류 중 조달물류를 대상으로 하여 불출자의 로드밸런스율을 증대할 수 있는 휴리스틱 알고리즘 개발에 대하여 연구를 진행함으로써 1차 목표 값을 적용하 였을 load balancing율은 45.6%에서 91.7%로 개선 된 것을 확인할 수 있었다.

본 연구에서는 LCD나 반도체 제조장비 등 일정한 크기의 작업공간에서 기계장비를 생산하는 주문형 조립장비의 작업 공간 할당과 일정계획을 다룬다. 각각의 주문은 일정계획과 그에 따른 조립 장비의 작업공간이 요구된다. 작업공간은 세가지 크기(대중소)의 작업 환경으로 나뉘어져 있다고 가정 한다. 조립장비의 작업공간은 일정계획과 장비크기에 따라 결정되는데, 현장의 경험과 노하우에 의존 하여 수작업으로 일정계획이 이루어지고 있는 것이 현실이다. 그러나 수 작업 일정계획은 많은 시간 이 소요되며, 주문을 만족시키는 일관성 있는 작업 일정계획을 제시하지도 못한다. 본 연구에서는 작 업공간 할당과 일정 계획문제를 휴리스틱 알고리즘을 이용하여 동시에 적용하는 연구를 수행하였다. 주문에 따른 작업일정과 작업공간 계획을 위한 휴리스틱 알고리즘을 제시하고 기존의 알고리즘과 비 교하여 일정계획에 따른 납기 지연 작업수, 납기 지연일 최대값, 평균 납기 지연일, 총 납기지연일 합 등의 지표를 비교분석 하였다.

We study a problem for scheduling and work space allocation of assemble-to-order equipments used in LCD or semiconductor production line. Each order has a distinct due date and requires work space for assemble. The allocation space is assumed to be three kinds of size, i.e. large, medium and small size. In this study, we propose a heuristic algorithm for assembly scheduling and work space allocations concurrently. We perform computational experiments to test performances of the proposed algorithm using randomly generated test data. Test results show that the proposed heuristic algorithm performs better than previous algorithms.

This paper investigates the optimal nesting system for a board. A hybrid method is used to search the optimal solution for rectangular nesting problem. This method is composed of heuristic approach algorithm. An engineer's experience of board nesting in which a loss occurred to sheet because of various individual error and diffidence. So, item layout at resource sheet were evaluated in engineering algorithm logic in which specially designed was installed. The nesting system consists of Lisp and Visual Basic. The system was controlled by AutoCAD so as to best item batch path test.

This paper investigates the optimal nesting system for a board. A hybrid method is used to search the optimal solution for rectangular nesting problem. This method is composed of heuristic approach algorithm. An engineer's experience of board nesting in which a loss occurred to sheet because of various individual error and diffidence. So, item layout at resource sheet were evaluated in engineering algorithm logic in which specially designed was installed. The nesting system consists of Lisp and Visual Basic. The system was controlled by AutoCAD so as to best item batch path test.

Students in cyber universities can take every course because there are no physical constraints such as class rooms. On the other hand, cyber universities should take a heavy burden to schedule real-time exams in a designated time period. Any two courses a student takes must not be simultaneously allocated to a time slot in such a schedule. It is well known that the complexity of this kind of scheduling problem increases exponentially as the number of courses does. This research provides a heuristic method to make on-line real-time exam timetables with minimal exam time span. It considers the constraint of not allocating courses a student takes to simultaneous exam time and prioritizes courses by the number of students.

In this study, we developed a heuristic algorithm to get better efficiency of clustering than conventional algorithms. Conventional clustering algorithm had lower efficiency of clustering as there were no solid method for selecting initial center of cluster and as they had difficulty in search solution for clustering. EMC(Expanded Moving Center) heuristic algorithm was suggested to clear the problem of low efficiency in clustering. We developed algorithm to select initial center of cluster and search solution systematically in clustering. Experiments of clustering are performed to evaluate performance of EMC heuristic algorithm. Squared-error of EMC heuristic algorithm showed better performance for real case study and improved greatly with increase of cluster number than the other ones.

In this paper, we developed a heuristic algorithm to assign job to workers in parallel line inspection process without sequence. Objective of assigning job in inspection process is only to assign job to workers evenly. But this objective needs much time and effort since there are many cases in assigning job and cases increase geometrically if the number of job and worker increases. In order to solve this problem, we proposed heuristic algorithm to assign job to workers evenly. Experiments of assigning job are performed to evaluate performance of this heuristic algorithm. The result shows that heuristic algorithm can find the optimal solution to assign job to workers evenly in many type of cases. Especially, in case there are more than two optimal solutions, this heuristic algorithm can find the optimal solution with 98% accuracy.

공급자와 소비자간의 정보공유는 기능 중심에서 프로세스 중심이 되면서, 유연성과 고객 서비스를 극대화하기 위한 새로운 물류 개념을 요구하게 되었다. 다시 말하면 원부자재의 조달에서부터 생산을 거쳐 고객에게 판매되기까지의 전 과정에 걸친 개체간의 수요와 공급의 사슬관계를 의미하는 공급망 내에서 정보, 자금 그리고 물의 흐름을 관리 통제하는 공급사슬경영(SCM)이 사업의 핵심역량으로 인식되고 있다. 또한 국내 기업들의 수․배송 업무의 합리화는 국내 기업 물류관리의 가장 중요한 과제중의 하나로 부각되고 있다. 물류센터로부터 각 거래처로 물품을 배달하는 배송업무의 경우에는 기업의 물류정보시스템이 상류 중심의 데이터 체계로 구축되어왔기 때문에 물류관리 업무의 합리화에 활용할 수 있는 기초 데이터 부재와 현실적인 제약조건들을 고려한 효율적인 자동배차 알고리듬을 적용하지 못했기 때문에 배차담당자들의 수작업 조정시간이 장시간 소요되었다. 따라서 본 논문에서는 현실적인 다양한 제약조건을 만족하고, 수작업 조정시간이 대폭 줄이면서, 우편중심 좌표를 이용한 차량별 근거리 그룹핑으로 자동배차 지원시스템의 휴리스틱 알고리듬을 설계하도록 한다.

Moreover a new logistics concept is needed through the sharing information between suppliers and consumers, which maximizes the level of customers service and its flexibility by changing functional-oriented to process-oriented. In other words, Supply Chain Management(SCM) is now considered as a key aspect of business, which controls the flows of information, funds, and goods in the supply chain. Rationalization of transport-delivery system will be one of the most important issues on logistics management to the domestic companies. The companies need the effective delivery system. Especially in the case of delivery system from distribution centers to customers or vendors, it might take a long time to control the delivery system manually because it would be hard to apply the automatic vehicle routing algorithm effectively considering all the practical constraints. Thus this study develops a heuristic algorithm of automatic vehicle delivery support system in terms of grouping by short ranges of vehicle movement utilizing postal coordinates, which satisfies a variety of realistic constraints and reduces controlling time of manual operations.

In this paper, we raised the performance of heuristic algorithm to assign job to workers in parallel line inspection process without sequence. In previous research, we developed the heuristic algorithm. But the heuristic algorithm can't find optimal solution perfectly. In order to solve this problem, we proposed new method to make initial solution called FN(First Next) method and combined the new FN method and old FE method using previous heuristic algorithm. Experiments of assigning job are performed to evaluate performance of this FE+FN heuristic algorithm. The result shows that the FE+FN heuristic algorithm can find the optimal solution to assign job to workers evenly in many type of cases. Especially, in case there are optimal solutions, this heuristic algorithm can find the optimal solution perfectly.

Companies are pursuing the management of small quantity batch production or JIT(Just-in-time) system for improving the delivery response and LOB(Line Balancing) in order to satisfy consumers’ increasing demands in the current global economic recession. And in order to improve the growth of production for reducing manufacturing cost, improvements of the Load Balancing have become an important reformation factor. Thus this paper is aimed at warehouse which procures materials on the assembly line in procurement logistics of automotive logistics and proceed with research on heuristic algorithm development which can increase the Load Balancing of workers. As a result of this study, when applied the primary target value, it was verified that the whole workers decreased from 28 to 24. Furthermore, when specified the secondary target value and applied algorithm once more, it was verified that the Load Balance Ratio was improved from 44.96% to 91.7%.

k -club은 소셜 네트워크 분석에서 다양한 형태의 소셜 그룹을 설명하기 위해 제안된 그래프 모델 중 하나로, 단순 그래프에서 부분 정점 집합 S 에 의한 유도 부분그래프(Induced subgraph)의 지름이 k보다 작거나 같은 경우 S 를 k -club이라 한다. 본 논문에서는 유전알고리즘을 이용하여 그래프에서 크기가 최대인 k -club을 찾는 문제인 Mk CP(Maximum k -Club Problem)을 계산하는 HGA+DROP 알고리즘을 제안한다. 본 알고리즘은 k -club을 위한 휴리스틱 알고리즘 k -CLIQUE & DROP을 변형하고 휴리스틱 유전 알고리즘(HGA)을 사용해 한 번의 수행으로 복수 개의 k -club을 구하였다. 기존 알고리즘의 결과와 비교하기 위해 DIMACS 그래프들에 대하여 k 가 2, 3, 4 그리고 5일 때 MkCP를 계산하였다.

Given an undirected simple graph, k -club is one of the proposed structures to model social groups that exist in various types in Social Network Analysis (SNA). Maximum k -Club Problem (Mk CP) is to find a k -club of maximum cardinality in a graph. This paper introduces a Genetic Algorithm called HGA+DROP which can be used to approximate maximum k -club in graphs. Our algorithm modifies the existing k -CLIQUE & DROP algorithm and utilizes Heuristic Genetic Algorithms (HGA) to obtain multiple k-clubs. We experiment on DIMACS graphs for k = 2, 3, 4 and 5 to compare the performance of the proposed algorithm with existing algorithms.

교통 네트워크에서 하나의 노드로부터 다른 노드로 가는 최단 경로 탐색은 탐색속도와 함께 정확성도 매우 중요시 되고 있다. 기존 A* 알고리즘은 빠른 탐색속도가 큰 장점이기는 하지만, 분석네트워크가 다소 복잡하고, 링크수가 많은 대규모 네트워크에서는 최단 통행경로를 가까운 노드의 순서대로 단계적으로 찾아내는 데 정확도가 다소 낮은 약점을 갖고 있다. 따라서 본 연구에서는 A* 알고리즘의 평가함수와 알고리즘을 수정하여 정확성을 높일 수 있도록 하였다. 구 체적으로는 평가함수를 선적인 개념에서 면적인 개념으로 전환하였고, 계산단계의 진행과정에서 실제 부하량이 적을수 록 무조건 좋은 것이 아니라, 부하량이 커도 목표노드에 가까운 것이라면 더욱 최단경로에 유리하다는 개념을 도입한 것이다. 마지막으로 평가함수 값은 반복계산을 수행할수록 적어야 하는데, 이렇지 못할 경우, 피드백 기능을 부가하여 탐색 정확도를 높이도록 알고리즘을 수정하였다. 이렇게 개선된 알고리즘을 실제 네트워크상에서 적용해 본 결과, 유용 성이 있는 것으로 밝혀졌다.

In transportation networks, searching speed and result accuracy are becoming more critical on searching minimum path algorithm. Current A* algorithm has a big advantage of high searching speed. However, it has disadvantage of complicated searching network and low accuracy rate of finding the minimum path algorithm. Therefore, this study developed A* algorithm’s heuristic function and focused on improving it’s disadvantages. Newly developed function in this study contains the area concept, not the line concept. During the progress, this study adopts the idea of a heavier node that remains lighter to the target node is better that the lighter node that becomes heavier when it is connected to the other. Lastly, newly developed algorithm has the feedback function, which allows the larger accuracy value of heuristic than before. This developed algorithm tested on real network, and proved that developed algorithm is useful.

This paper aims to investigate data features for neighbor path selection (NPS) in computer network with regional failures. It is necessary to find an available alternate communication path in advance when regional failures due to earthquakes or forest fires occur simultaneously. We describe previous general heuristics and simulation heuristic to solve the NPS problem in the regional fault network. The data features of general heuristics using proximity and sharing factor and the data features of simulation heuristic using machine learning are explained through examples. Simulation heuristic may be better than general heuristics in terms of communication success. However, additional data features are necessary in order to apply the simulation heuristic to the real environment. We propose novel data features for NPS in computer network with regional failures and Keras modeling for computing the communication success probability of candidate neighbor path.

In this paper we study the production-scheduling problem for a two-stage flow shop with outsourcing options. The maintenance of in-house machine and job-related discount for outsourcing cost are considered in model, in which total cost and makespan are regarded as bi-objective for the scheduling problem. Based on the analysis of model, we introduce a concept-packaging to reduce the complexity of problem and then develop heuristic algorithm to solve the problem. Computational experiments using different groups of data are conducted to test the algorithm.

The School Bus Routing Problem (SBRP) covers the issue of establishing plans to efficiently transport students distributed across a designated area to the relevant schools using defined resources. As with the similar Vehicle Routing Problem (VRP), the SBRP may have diverse constraints such as heterogeneous vehicles, the allotted time window and multiple depots. Many solutions for effectively solving the problem are currently being studied. By their nature, these routing problems are NP-Hard (non-deterministic polynomial-time hard) problems in which the search domains increase exponentially as they become larger, thus making it difficult to obtain solutions using an exact approach except for relatively simple and localized problems. Therefore the heuristic approach is being studied in many regions. In this study, an algorithm was developed using genetic algorithms, which stem from meta-heuristic algorithms, and the algorithm was tested against diverse problems to identify its performance and practicality.

In order to solve issues of grid network as a broadband wireless access network, such as high data transmission rate and long-distance signal coverage and so on, this paper proposes two heuristic algorithms with O (m3) time complexity, based on the user neighbors’ information and location information. The algorithm will meet transferring the minimum relay deployment problem required by users into the minimum clique partition problem of adjacency graph and gives specific algorithm steps. Experimental results show that: the proposed algorithm ensures network performance and reduces network cost.