Heung-Suk Hwang

An improved model for vehicle routing problem with time constraint based on genetic algorithm

A vehicle routing problem (VRP) with time constraint is one of the important problems in distribution and transportation. Thus the generic VRP and its practical extensions are discussed in great detail in the literatures. In the VRP, the service of a customer must start and finish within a given time interval. The objective of this problem is to minimize the cost of servicing the set of customers without being tardy or exceeding the capacity or travel time of the vehicles. In this research we concentrated on developing a GA-TSP model by improving the genetic algorithm (GA) operators and the initial population. For the computational purpose, we developed a GUI (graphic user interface)-type computer program according to the proposed method. The computational results show that the proposed method is very effective on a set of standard test problems and it can be potentially useful in solving the VRPs.

A food distribution model for famine relief

Many of the problems encountered by the urgency of need to deliver food to famine relief area arose further complicated distribution problems because there were neither inventory systems in place nor plans for transporting goods to these regions. This study develops an effective distribution model for determining optimal patterns of food supply and inventory allocation for famine relief area. This research addresses the formulation of those complex vehicle routing problems incorporating inventory allocation and optimal distribution based on minimizing the amount of pains and starving instead of travel distance or time. Also, we have developed computer programs for each step of approaches and have shown the results of sample run for North Korea famine relief problem. It was known that the proposed model was a very acceptable for the inventory allocation and vehicle routing problems.

A study on an inventory model for items with weibull ameliorating

Abstract This paper is concerned with the development of ameliorating inventory models. The ameliorating inventory is the inventory of goods whose utility increases over the time by ameliorating activation. The term ameliorating inventory is used in this paper at least, since the terminology is not standard well known. This study is performed according to areas; one is an economic order quantity(EOQ) model for the items whose utility is ameliorating in accordance with Weibull distribution, and the other is a partial selling quantity(PSQ) model developed for selling the surplus inventory accumulated by ameliorating activation. The proposed models cannot be solved directly in a closed form, thus we used a computer program and a graphical solution method to obtain the optimal ordering and selling quantity in this paper. Numerical examples to illustrate the effect of ameliorating rate on inventory polices are shown at the end of this paper.

Inventory models for both deteriorating and ameliorating items

Abstract Most of the previous works on classical inventory models are based on the assumptions that the value (or utility) of inventory remains constant over time. In this study, a special case of inventory model is studied for both ameliorating when the items stay at breeding yard (fish culture facility or farming yard) and deteriorating when the items stay in the distribution systems. The term ameliorating inventory is used in this paper at least, since the terminology is not standard well known. First, We have developed economic order quantity(EOQ) models for both deteriorating and ameliorating, second we have developed partial selling model(PSQ) and economic production model(EPQ) considering issuing policies, FIFO and LIFO. Also we have developed computer programs and numerical examples to illustrate the model are examined also.

R&D project evaluation model based on fuzzy set priority

Abstract Most of the conventional concepts used by the R&D project evaluation models do not seem to be appropriate for modeling the imprecision R&D project evaluation. This paper is concerned with the project evaluation by aggregating the multiple rank-ordered sets based on fuzzy set priority. First the rank-ordered priority lists of the R&D projects were determined based on the majority-rule methods, then the aggregate fuzzy set rank order was computed and compared with the others. Finally, we have developed a systemic and practical program suitable for a simple and easy calculation of all the algorithms. It was found that our model was validated by the comparative computations in various R&D project examples.

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.

A performance evaluation model for FMS based on RAM and LCC using FACTOR/AIM

Abstract The objective of this study is to develop a general design and performance evaluation model for the system designers in the initial design phase of the integrated manufacturing system based on the RAM(Reliability, Availability and Maintainability) and life cycle cost(LCC). The methodology proposed in this research includes the following three-step generative approach. First, a deterministic approach to find the optimal system configuration for the initial system configuration is considered under the assumption that the system availability is one(no failure and maintenance), and in second step a stochastic simulation model based on RAM and LCC is developed. Using the results of this two-stage simulation, a system performance index(SPI) was developed for the performance evaluation of the system. In the last step we developed a simulation model using FACTOR/AIM to consider a variety of performance factors and compared the results through a sample example.

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.

A reliability prediction model for missile systems based on truncated Weibull distribution

A reliability prediction model was developed for missile systems based on a truncated Weibull distribution both time and failure truncated cases. In practice, there are usually limited test data available in the development and in most cases test phases of missile systems, and the obtained data are failure and time truncated. Thus in this paper we developed a time and failure truncated Weibull model for reliability analysis of missile systems under development. The central purposes of this model are focussed on the analysis on the present system reliability and the prediction of the system reliability at some future time based on obtained test data. For the purpose of computing, a computer program was developed and have shown the prediction results through a sample problem. This model can be applied to a wide variety of systems not only to missile systems.

A study on a probabilistic two-echelon location system for deteriorating items

Abstract A special case of the probabilistic multi-locationproblem is studied for deteriorating items. The objective of this study is to determine the location of the minimum number of supply centres among a discrete set of location sites of supply centres under the condition that the probability of each retailer being covered by one of the supply centres is not less than a specified value. A logistic cost function including the deterioration rate is developped for a performance measure of the system. We have formulated a probabilistic set-covering programming which can be solved by a 0–1 programming. The result of a set of numerical examples well shows the impact of deteriorating rate on the location problems. And also this study will provide a method of a useful guidelines for the location decisions.