• Journal of the Korea Society for Simulation
• /
• v.9 no.3
• /
• pp.91-102
• /
• 2000

The distribution routing problem is one of the important problems in distribution and supply center management. This research is concerned with an integrated distribution routing problem for multi-supply centers based on improved genetic algorithm and GUI-type programming. In this research, we used a three-step approach; in step 1 a sector clustering model is developed to transfer the multi-supply center problem to single supply center problems which are more easy to be solved, in step 2 we developed a vehicle routing model with time and vehicle capacity constraints and in step 3, we developed a GA-TSP model which can improve the vehicle routing schedules by simulation. For the computational purpose, we developed a GUI-type computer program according to the proposed methods and the sample outputs show that the proposed method is very effective on a set of standard test problems, and it could be potentially useful in solving the distribution routing problems in multi-supply center problem.

• Journal of the Korea Safety Management & Science
• /
• v.10 no.1
• /
• pp.155-166
• /
• 2008

During last two decades the transportation system has developed into very intelligent system with GIS, GPS and ITS. The practical transportation management system provides real time response module to manage the customer's order. We have surveyed research papers on the real time vehicle routing problem in last two decades to figure out the dynamic vehicle routing problem. The papers are classified by basic routing algorithms and by managing the dynamic events which are the order management, the routing re-optimization, the routing post-optimization and the waiting strategy.

• Journal of the military operations research society of Korea
• /
• v.12 no.2
• /
• pp.37-55
• /
• 1986

Vehicle fleet planning problem is generic name given to a whole class of practical decision making problems which find the vehicle routes and schedules to accomplish the reqired service to customers using vehicles. In this paper the various problems are classified into the three groups according to their characteristics: (1) vehicle routing problems, (2) vehicle scheduling problems, and (3) vehicle routing and scheduling problems. The State of the art of each group is described and the future research directions are presented.

• Journal of the Korea Safety Management & Science
• /
• v.15 no.2
• /
• pp.193-204
• /
• 2013

The growing logistics strategy of a company is to optimize their vehicle route scheduling in their supply chain system. It is very important to analyze for continuous pickups and delivery vehicle scheduling. This paper is a computational study to investigate the effectiveness of continuous pickups and delivery vehicle routing problems. These scheduling problems have 3 subproblems; Inbound Vehicle Routing Problem with Makespan and Pickup, Line-haul Network Problem, and Outbound Vehicle Routing Problem with Delivery. In this paper, we propose 5 heuristic Algorithms; Selecting Routing Node, Routing Scheduling, Determining Vehicle Type with Number and Quantity, and Modification Selecting Routing Node. We apply these Algorithms to S vehicle company. The results of computational experiments demonstrate that proposed methods perform well and have better solutions than other methods considering the basic time and due-date.

• Journal of Korean Institute of Industrial Engineers
• /
• v.35 no.1
• /
• pp.1-14
• /
• 2009

The efficiency of transportation requests fulfillment can be increased through extending the problem of vehicle routing and scheduling by the possibility of subcontracting a part of the requests to external carriers. This problem extension transforms the usual vehicle routing and scheduling problems to the more general integrated operational transportation problems. In this contribution, we analyze the motivation, the chances, the realization, and the challenges of the integrated operational planning and report on experiments for extending the plain Vehicle Routing Problem to a corresponding problem combining vehicle routing and request forwarding by means of different sub-contraction types. The extended problem is formalized as a mixed integer linear programming model and solved by a commercial mathematical programming solver. The computational results show tremendous costs savings even for small problem instances by allowing subcontracting. Additionally, the performed experiments for the operational transportation planning are used for an analysis of the decision on the optimal fleet size for own vehicles and regularly hired vehicles.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1997.10a
• /
• pp.201-204
• /
• 1997

This study is focussed on optimization problems which require allocating the restricted inventory to demand points and assignment of vehicles to routes in order to deliver goods for demand sites with optimal decision. This study investigated an integrated model using three step-by-step approach based on relationship that exists between the inventory allocation and vehicle routing with restricted amount of inventory and transportations. we developed several sub-models such as; first, an inventory-allocation model, second a vehicle-routing model based on clustering and a heuristic algorithms, and last a vehicle routing scheduling model, a TSP-solver, based on genetic algorithm. Also, for each sub-models we have developed computer programs and by a sample run it was known that the proposed model to be a very acceptable model for the inventory-allocation and vehicle routing problems.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.25 no.4
• /
• pp.97-109
• /
• 2000

This work proposes a neural network approach to solve vehicle routing problems which have diverse application areas such as vehicle routing and robot programming. In solving these problems, classical mathematical approaches have many difficulties. In particular, it is almost impossible to implement a real-time vehicle routing with multiple vehicles. Recently, many researchers proposed methods to overcome the limitation by adopting heuristic algorithms, genetic algorithms, neural network techniques and others. The most basic model for path planning is the Travelling Salesman Problem(TSP) for a minimum distance path. We extend this for a problem with dynamic upcoming of new positions with multiple vehicles. In this paper, we propose an algorithm based on SOM(Self-Organization Map) to obtain a sub-optimal solution for a real-time vehicle routing problem. We develope a model of a generalized multiple TSP and suggest and efficient solving procedure.

• Proceedings of the IEEK Conference
• /
• 2002.07b
• /
• pp.1180-1183
• /
• 2002

Types of problems and solutions to vehicle routing problems are discussed. A case study on a public transport is conducted to discover the characteristics of problems in route selection. The characteristics of problems solved by certain technique or algorithm are also studied to investigate the capability of current techniques in solving public bus routing.

• Journal of Korean Institute of Industrial Engineers
• /
• v.30 no.4
• /
• pp.346-354
• /
• 2004

Most industrial logistic systems have focused on carrying products from manufacturers or distribution centers to customers. In recent years, they are faced with the problem of integrating reverse flows into their transportation systems. In this paper, we address the vehicle routing problems with mixed delivery and pick-up(VRPMDP). Mixed operation of delivery and pick-up during a vehicle tour requires rearrangement of the goods on board. The VRPMDP considers the reshuffling time of goods at customers, hard time windows, and split operation of delivery and pick-up. We construct a mixed integer mathematical model and propose a new genetic algorithm named GAMP for VRPMDP. Computational experiments on various types of test problems are performed to evaluate GAMP against the modified Dethloff's algorithm. The results show that GAMP reduces the total vehicle operation time by 5.9% on average, but takes about six times longer computation time.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.4
• /
• pp.171-178
• /
• 2012

A possible heuristic to solve metropolitan area vehicle routing problems with variable vehicle speeds is suggested in this research. Delivery hours are classified into 4 different time zones to make variable vehicle speeds no change within the same time zone to make TDVRP simple to solve. The suggested heuristic consists of 2 stages such as initial solution development step and initial solution improvement step. A computer program using C++ is constructed to evaluate the suggested heuristic. Randomly generated vehicle routing problems are used for the experiments. This heuristic could be helpful to logistics companies by increasing delivery efficiencies since the 4 zone classification is taken from the observed traffic information offered by a local government.