• 대한산업공학회지
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• 제32권2호
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• pp.98-103
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• 2006

In this research, the single machine capacitated lot-sizing and scheduling problem with sequence- dependent setup costs and setup times (CLSPSD) is considered. This problem is the extension of capacitated lot-sizing and scheduling problem (CLSP) with an additional assumption on sequence-dependent setup costs and setup times. The objective of the problem is minimizing the sum of production costs, inventory holding costs and setup costs satisfying customers' demands. It is known that the CLSPSD is NP-hard. In this paper, the MIP formulation is presented. To handle the problem more efficiently, a conceptual model is suggested, and one of the well-known meta-heuristics, the simulated annealing approach is applied. To illustrate the performance of this approach, various instances are tested and the results of this algorithm are compared with those of the CLPEX. Computational results show that this approach generates optimal or nearly optimal solutions.

• 한국IT서비스학회:학술대회논문집
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• 한국IT서비스학회 2003년도 춘계학술대회
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• pp.215-221
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• 2003

In today's rapidly changing business environment, quality of responsiveness to customer requirements for short order cycles and on time delivery is becoming more important and considered as one of critical success factors in supply chain management. Yet despite its importance on reducing transportation cost and improving customer service, little attention has been given to the transportation planning system in Korea SI industry. In this paper, we present development of transportation planning system especially to deal with vehicle routing problem which has the goal to minimize the costs of daily transportation operation and to maximize customer delivery service. The system architecture with other enterprise application is presented and real-world constraints are well incorporated into the system by combining constraints programming and meta heuristics.

• Structural Engineering and Mechanics
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• 제54권3호
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• pp.453-474
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• 2015

A hybrid approach of Particle Swarm Optimization (PSO) and Swallow Swarm Optimization algorithm (SSO) namely Hybrid Particle Swallow Swarm Optimization algorithm (HPSSO), is presented as a new variant of PSO algorithm for the highly nonlinear dynamic truss shape and size optimization with multiple natural frequency constraints. Experimentally validation of HPSSO on four benchmark trusses results in high performance in comparison to PSO variants and to those of different optimization techniques. The simulation results clearly show a good balance between global and local exploration abilities and consequently results in good optimum solution.

• 산업경영시스템학회지
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• 제36권3호
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• pp.63-70
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• 2013

Many real world optimization problems are discrete and multi-valued. Meta heuristics including Genetic Algorithm and Particle Swarm Optimization have been effectively used to solve these multi-valued optimization problems. However, extensive comparative study on the performance of these algorithms is still required. In this study, performance of these algorithms is evaluated with multi-modal and multi-dimensional test functions. From the experimental results, it is shown that Discrete Particle Swarm Optimization (DPSO) provides better and more reliable solutions among the considered algorithms. Also, additional experiments shows that solution quality of DPSO is not lowered significantly when bit size representing a solution increases. It means that bit representation of multi-valued discrete numbers provides reliable solutions instead of becoming barrier to performance of DPSO.

• Steel and Composite Structures
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• 제14권5호
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• pp.431-451
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• 2013

The Big Bang-Big Crunch (BB-BC) optimization algorithm is developed for optimal design of non-linear steel frames with semi-rigid beam-to-column connections. The design algorithm obtains the minimum total cost which comprises total member plus connection costs by selecting suitable sections. Displacement and stress constraints together with the geometry constraints are imposed on the frame in the optimum design procedure. In addition, non-linear analyses considering the P-${\Delta}$ effects of beam-column members are performed during the optimization process. Three design examples with various types of connections are presented and the results show the efficiency of using semi-rigid connection models in comparing to rigid connections. The obtained optimum semi-rigid frames are more economical solutions and lead to more realistic predictions of response and strength of the structure.

• 한국컴퓨터정보학회논문지
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• 제24권1호
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• pp.167-178
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• 2019

In this paper, we study the multi-objectives vehicle and drone routing problem with time windows, MOVDRPTW for short, which is defined in an urban delivery network. We consider the dual modal delivery system consisting of drones and vehicles. Drones are used as a complement to the vehicle and operate in a point to point manner between the depot and the customer. Customers make various requests. They prefer to receive delivery services within the predetermined time range and some customers require fast delivery. The purpose of this paper is to investigate the effectiveness of the delivery strategy of using drones and vehicles together with a multi-objective measures. As experiment datasets, we use the instances generated based on actual courier delivery data. We propose a hybrid multi-objective evolutionary algorithm for solving MOVDRPTW. Our results confirm that the vehicle-drone mixed strategy has 30% cost advantage over vehicle only strategy.

• 전자공학회논문지CI
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• 제40권6호
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• pp.87-94
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• 2003

휴리스틱 알고리즘 연구에 있어서 중요한 분야 중 하나가 강화(Intensification)와 다양화(Diversification)의 조화를 맞추는 문제이다. 개미 집단 최적화(Ant Colony Optimization, ACO)는 최근에 제안된 조합 최적화 문제를 해결하기 위한 메타휴리스틱 탐색 방법으로, 그리디 탐색(greedy search)뿐만 아니라 긍정적 반응의 탐색을 사용한 모집단에 근거한 접근법으로 순회 판매원 문제(Traveling Salesman Problem, TSP)를 풀기 위해 처음으로 제안되었다. 본 논문에서는 ACO접근법의 하나인 개미 집단 시스템(Ant Colony System ACS)에서 강화와 다양화의 조화를 통한 성능향상기법에 대해 알아본다. 먼저 에이전트들의 방문 횟수 적용을 통한 상태전이는 탐색 영역을 넓힘으로써 에이전트들이 더욱 다양하게 탐색하게 한다. 그리고, 전역 갱신 규칙에서 전역 최적 경로만 갱신하는 전통적인 ACS알고리즘에서 대하여, 경로 사이클을 구성한 후 각 경로에 대해 긍정적 강화를 받는 엘리트 경로를 구분하는 기준을 정하고, 그 기준에 의해 추가 강화하는 방법을 제안한다. 그리고 여러 조건 하에서 TSP문제를 풀어보고 그 성능에 대해 기존의 ACS 방법과 제안된 방법을 비교 평가해, 해의 질과 문제를 해결하는 속도가 우수하다는 것을 증명한다.

• Industrial Engineering and Management Systems
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• 제7권2호
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• pp.171-181
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• 2008

Home care, known also as domiciliary care, is part of the community care service that is a responsibility of the local government authorities in the UK as well as many other countries around the world. The aim is to provide the care and support needed to assist people, particularly older people, people with physical or learning disabilities and people who need assistance due to illness to live as independently as possible in their own homes. It is performed primarily by care workers visiting clients' homes where they provide help with daily activities. This paper is concerned with the dispatching of care workers to clients in an efficient manner. The optimized routine for each care worker determines a schedule to achieve the minimum total cost (in terms of distance traveled) without violating the capacity and time window constraints. A collaborative population-based meta-heuristic called Particle Swarm Optimization (PSO) is applied to solve the problem. A particle is defined as a multi-dimensional point in space which represents the corresponding schedule for care workers and their clients. Each dimension of a particle represents a care activity and the corresponding, allocated care worker. The continuous position value of each dimension determines the care worker to be assigned and also the assignment priority. A heuristic assignment scheme is specially designed to transform the continuous position value to the discrete job schedule. This job schedule represents the potential feasible solution to the problem. The Earliest Start Time Priority with Minimum Distance Assignment (ESTPMDA) technique is developed for generating an initial solution which guides the search direction of the particle. Local improvement procedures (LIP), insertion and swap, are embedded in the PSO algorithm in order to further improve the quality of the solution. The proposed methodology is implemented, tested, and compared with existing solutions for some 'real' problem instances.

• 정보처리학회논문지B
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• 제16B권3호
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• pp.195-202
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• 2009

논문에서는 개미 군집 최적화 알고리즘을 이용하여 뇌 자기공명 영상의 백질 및 회백질 영역을 분할하는 방법을 제안한다. 확률적 조합 최적화에 적합한 알고리즘으로 알려진 개미 군집 최적화 알고리즘은 실제 개미들이 집에서 먹이를 찾아가는 동안의 방법을 기억하는 습성을 적용한 것이다. 논문에서 제안하는 방법은 개미가 먹이를 찾아가는 동안의 방법을 기억하는 습성처럼 영상에서 원하는 픽셀을 찾아갈 수 있다는 것이다. 원하는 픽셀을 찾은 개미들은 페로몬을 픽셀에 축적하게 되는데 이 페로몬은 이후에 지나가는 개미들이 다음 경로를 선택할 때 영향을 준다. 그리고 각각의 반복단계에서 상태전이 법칙에 따라 영상의 위치를 바꿔가면서 최종 목적지에 도달하게 되며, 마지막으로 페로몬 분포의 분석을 통해 영상에서 분할 된 결과를 얻는다. 제안한 알고리즘을 기존의 임계치 기반의 분할 알고리즘인 Otsu 방법, 메타휴리스틱 계열의 대표적인 방법인 유전자알고리즘, 퍼지방법, 원래의 개미 군집 최적화 알고리즘등과 비교하였다. 비교 실험을 통해 제안한 방법이 뇌의 특정 영역을 더 정확하게 분할함을 알 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제16권3호
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• pp.203-210
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• 2011

개미 집단 시스템은 조합 최적화 문제를 해결하기 위한 메타 휴리스틱 탐색 방법으로, 그리디 탐색뿐만 아니라 긍정적 피드백을 사용한 모집단에 근거한 접근법으로 순회 판매원 문제를 풀기 위해 처음으로 제안되었다. 본 논문에서는 이전 전역 최적 경로와 현재 전역 최적 경로의 중복 간선을 고려한 탐색 방법을 제안하였다. 이 방법은 이전전역 최적 경로와 현재 전역 최적 경로에서의 중복 간선은 최적 경로로 구성될 가능성이 높다고 판단하고, 해당 중복 간선에 대해 페로몬을 강화시켜 최적 경로를 구성할 확률을 높이게 하였다. 그리고, 실험을 통해 ACS-3-opt 알고리즘, ACS-Subpath 알고리즘, ACS-Iter 알고리즘에 비해 최적 경로 탐색 및 평균 최적 경로 탐색의 성능이 우수함을 보여 주었다.