An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab’s AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS’s complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab’s production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab’s AMHS.

The Dynamic Vehicle Routing Problem (DVRP) involves a combinatorial optimization problem where new customer demands become known over time, and old routes must be reconfigured to generate new routes while executing the current solution. We consider the high level of dynamism problem. An application of highly dynamic DVRP is the ambulance service where a patient contacts the service center, followed by an evaluation of case severity, and a visit by a practitioner/ ambulance is scheduled accordingly. This paper considers a variant of the DVRP and proposes a decentralized algorithm in which collaborators (Depot and Vehicle), both have only partial information about the entire system. The DVRP is modeled as a periodic re optimization of VRP using the proposed decentralized algorithm where collaborators exchange local information to achieve the best global objective for the current state of the system. We assume the existence of a dispatcher e.g., headquarter of the company who can communicate to vehicles in order to gather information and assigns the new visits to them. The effectiveness of the proposed decentralized coordination algorithm is further evaluated using benchmark data given in literature. The results show that the proposed method performed better than the compared algorithms which utilize the centralized coordination in 12 out of 21 benchmark problems.

현재 부산항신항과 배후단지 물류업체를 연계하는 셔틀운송에는 배후단지 물류업체가 독자적으로 운송차량을 보유하고 운용함으로써 자원소모의 중복성이 있으며 불규칙한 운송화물의 발생으로 화물운송에 매번 다른 차량이 투입되어 업체들이 특정 지역에 밀집되어 있는 단지의 지리적 효과를 충분히 활용하지 못하고 있다. 따라서 본 연구는 이러한 문제들을 해결하기 위해 동적계획을 구성하고 휴리스틱 방법을 통해 작업 스케줄링과 실시간 작업규칙을 구성하며 물류기업의 수 배송 계획에 풀링과 듀얼사이클링을 적용하여 셔틀운송에 소요된 자원의 감축효과와 효율성을 시뮬레이션을 통해 검증한다.

Significant increase of container flows in the marine terminals requires more efficient port equipments such as logistic and transfer systems. This paper presents collision avoidance and routing approach based on dynamic programming (DP) algorithm for a linear motor based shuttle car which is considered as a new transfer system in the port terminals. Most of routing problems are focused on automatic guided vehicle (AGV) systems, but its solutions are hardly utilized for LM based shuttle cars since both are mechanically different. Our proposed DP is implemented for real-time searching of an optimal path for each shuttle car in the Agile port terminal located at California in USA.

본 연구의 목적은 추계동역학적 상태·공간형태의 하도 저류함수모형을 개발하고 실시간 홍수예보를 위한 모형의 적용성을 검토하는데 있다. 팔당댐에서 인도교 지점에 이르는 하도구간을 개발된 모형의 적용 대상구간으로 설정하였으며, 1987∼1998년에 발생한 13개의 홍수사상을 선택하여 모형 매개변수 산정 및 적용성을 검토하였다. 그 결과 최적 매개변수는 각 홍수사상마다 다르지만, 현재 실무에서 사용하고 있는 매개변수를 사용한 유량예측은 비교적 합리적인 결과를