Gyu-Sung Cho

Integrated GA-VRP solver for multi-depot system

Abstract This research is concerned with development of an integrated VRP (vehicle routing problem) solver for multi-depot supply system based on an improved genetic algorithm and GUI-type programming. In this research, α we proposed a three-step approach to convert a multi-supply center problem into single supply center problems so as to make easy to solve. In this research we have developed an integrated VRP model using heuristic method and the improved genetic algorithm, GA, of which operators and initial population are improved. Also we developed a graphical user interface (GUI-type) program, Dongeui GA-VRP solver, based on three-step approaches such as: (1) clustering module to transfer the multi-supply center problem into single supply center problems which are easier to be solved, (2) improved vehicle routing, VRP, module, and (3) GA-TSP module. The objective of this problem is to minimize the logistic cost for a set of customers without being tardy or exceeding the travel time and the capacity of the vehicles. For the user’s conveniences, we have shown the sample results compared with those of existing methods. By the example problem, the proposed model is known to be potentially useful in solving the distribution routing problems.

A Simulation Study for the Operations Analysis of Dynamic Planning in Container Terminals Considering RTLS

The aim of this study is to propose using dynamic planning system (DPS) for yard tractors (YT) utilizing real time location systems technology (RTLS) and analyzing its performance. The system can provide real time job ordering in terms of load balancing using the information on location, in regards to the movement of YT. There are two required DPS operation rules regarding YT; the shortest operation time rule and the equal load distribution rule. We examined, by means of simulation, how productivity could be improved with the DPS in container terminals. For the examination, three operation methodologies were utilized: previous, pooling, and dual-cycling. According to the results, flow time of the pooling operation was shorter than previous operations, and dual-cycling was the shortest operation. The best performance was achieved with minimized cost when the shortest operation time rule applied to dual-cycling operation.

Supply Center Planning Model Using Fuzzy-AHP and VRP

This research is concerned with a new method of optimizing the design of the supply center for public service considering the locations and service level based on Fuzzy-AHP and service level of service facility using VRP (vehicle routing problem) is one of the evident areas of important points in the design of public service facility. This research is concerned with the development of decision method of location number of public service facilities with minimum travel distance (with optimal service level). We developed a 3-step facility planning based on VRP, fuzzy-AHP, and stochastic set-covering method in the view of: (1) optimal decision of service facility types using AHP (analytic hierarchy process) as a multi-attribute structured analysis method, (2) finding the optimal number of service facility and those locations with minimum travel distance using set-covering problem, and (3) these procedures are shown by visual outputs based on visual VRP. The computer program is developed and demonstrated the computational results. It is known that the proposed method is very effective on a set of test problems.

Function and service pattern analysis for facilitating the reconfiguration of collaboration systems

Recently, manufacturing companies have been attempting to increase competitiveness in their business collaboration with cooperative companies rather than within their own companies. In order to facilitate their collaboration, they are attempting to adopt or already using a collaboration system, which supports a number of functions and services. However, it is very difficult to apply existing systems into other organizations or industrial sections without customization or reconfiguration because functional or service requirements of users usually differ according to their domain knowledge. In order to re-apply and disseminate an existing system to other companies, therefore, the system must be reconfigured by modifying, upgrading, or newly developing some portions of the system. During the customization processes, functions or services of the system must be refined in order to satisfy user requirements. For facilitating the reconfiguration of collaboration systems, in this paper, we first define user patterns, and subsequently propose a method for investigating and analyzing patterns based on data mining approach. The proposed method validates normal versus abnormal patterns that show a drastic increase in the use of a specific function or service, and automatically makes the system recognize abnormal patterns as new normal patterns when abnormal patterns continue for a long time. We conduct experiments and comparison studies using an Apriori-like approach in order to establish the effectiveness of the proposed method. We also suggest a guideline for the reconfiguration of function modules or services with a specific collaboration system.

Container Terminal Demand Forecasting Framework Using Fuzzy-GMDH and Neural Network Method

In this paper, a fuzzy group method data handling- type (GMDH) neural networks and their application to the container terminal demand forecasting of port transportation system are described. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to neural networks, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of neural-fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the neural-fuzzy GMDH. The GMDH-type neural networks have several advantages compared with conventional multi-layered GMDH models. The related computer program is developed and successful applications are shown in the field of estimating problem of container terminal demand with the number of factors considered.