After Dantzig and Rasmer introduced Vehicle Routing Problem in 1959, this field has been studied with numerous approaches so far. Classical Vehicle Routing Problem can be described as a problem of multiple number of homogeneous vehicles sharing a same starting node and having their own routes to meet the needs of demand nodes. After satisfying all the needs, they go back to the starting node. In order to apply the real world problem, this problem had been developed with additional constraints and pick up & delivery model is one of them. To enhance the effectiveness of pick up & delivery, hub became a popular concept, which often helps reducing the overall cost and improving the quality of service. Lots of studies have suggested heuristic methods to realize this problem because it often becomes a NP-hard problem. However, because of this characteristic, there are not many studies solving this problem optimally. If the problem can be solved in polynomial time, optimal solution is the best option. Therefore, this study proposes a new mathematical model to solve this problem optimally, verified by a real world problem. The main improvements of this study compared to real world case are firstly, make drivers visit every nodes once except hub, secondly, make drivers visit every nodes at the right time, and thirdly, make drivers start and end their journey at their own homes.

This paper considers a topological optimization of a network design with mean packet delay and node connectivity constraints. The objective is to find the topological layout of links, at minimal cost. This Problem is known to be NP-hard. To efficiently so

  본 논문은 시간 제약을 갖는 차량 라우팅 문제를 해결하기 위해 유전자 알고리듬과 부분 최적화 알고리듬을 적용한 방법을 소개한다. 유전자 알고리듬에서의 염색체는 노드를 나타내는 정수의 순열로 표현되어 직접적인 해를 나타내지 않지만, 경험적 방법에 의한 해석을 통해 유효한 해로 변형되도록 하였다. 유전자 알고리듬에 의해 생성된 주어진 수의 우수한 해들에는 세 부분 최적화 방법이 순차적으로 적용되어 보다 좋은 해를 생성하도록 하였다. 부분 최적화 방법들에

  The main objective of this study is to find out the shortest path of the vehicle routing problem with time window constraints by using both genetic algorithm and heuristic. Hard time constraints were considered to the vehicle routing problem in this sug

  Vehicle routing problem with time windows is determined each vehicle route in order to minimize the transportation costs. All delivery points in geography have various time restriction in camparision with the basic vehicle routing problem. Vechicle rout

Vehicle routing problem with Time Windows is determined each vehicle route in order to minimize the transportation costs. All delivery points in geography have various time restriction in camparision with the basic Vehicle routing problem. Vechicle routing problem with Time Windows is known to be NP-Hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study aims to develop a heuristic method which combines guided local search with a Tabu Search in order to minimize the transportation costs for the vehicle routing assignment and uses ILOG programming library to solve. The computational tests were performed using the benchmark problems.

Databases are central to business information systems and RDBMS is most widely used for the database system. Normalization was designed to control various anomalies (insert, update, delete anomalies). However, normalized database design does not account f

Databases are central to business information systems and RDBMS is most widely used for the database system. Normalization was designed to control various anomalies (insert, update, delete anomalies). However, normalized database design does not account for the tradeoffs necessary for performance. In this research, we model a database design method considering denormalization of duplicating attributes in order to reduce frequent join processes. This model considers response time for processing each select, insert, update, delete transaction, and for treating anomalies. A branch and bound method is proposed for this model.