• Journal of Korea Port Economic Association
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• v.25 no.4
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• pp.183-202
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• 2009

This study estimates the port waiting cost of international trade ports in Korea by an opportunity cost approach. In the next step, we present a method to assess the levels of port services by the average waiting cost of ships derived from the results of the first step. Because the port waiting cost reflects the social cost, it is difficult to use as a service indicator even though it is the decision support information for a particular port facility expansion. The percentages of waiting ships and time also are insufficient indicators to reflect only the quantitative aspects by the time. However, the average waiting cost of ships in this study can be utilized as a service indicator to reflect waiting time and the loss of economic value simultaneously. It is also very useful information for a shipper and a carrier to select a port. Based on the average waiting cost of ships in 2007, it is analyzed in order of lowest service ports sequentially such as Pyeongtaek-Dangjin, Pohang, Donghae, and Samcheonpo. It is different from the sequential order of ports by the port waiting cost such as Pohang, Incheon, Gwangyang, Pyeongtak-Dangjin, and Ulsan. The port waiting cost is to a port authority as a key indicator what the average waiting cost of ships is to a port user as a useful indicator to evaluate the levels of port services.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.1
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• pp.27-33
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• 2019

Several studies have been completed on the topic of container terminals in Northern Vietnam. Few of them, however, deal with competition in terms of costs related to vessel waiting time or cargo handling. This paper estimates the average waiting cost per TEU for all the container terminals in Northern Vietnam. After average waiting time was first estimated by applying queuing theory, uncertainty theory was applied to estimated vessel daily cost. A simulation was performed to create a series of data representing waiting cost per TEU in relation to the rate of volume handled/capacity of each terminal. Non-linear regression based on this series was used to present a function for the relationship between the average waiting cost of each terminal and the rate of volume handled /capacity.

• International Journal of Reliability and Applications
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• v.3 no.4
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• pp.165-171
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• 2002

An optimization of the M/M/1 queue with impatient customers is studied. The impatient customer does not enter the system if his or her virtual waiting time exceeds the threshold K ＞ 0. After assigning three costs to the system, a cost proportional to the virtual waiting time, a penalty to each impatient customer, and also a penalty to each unit of the idle period of the server, we show that there exists a threshold K which minimizes the long-run average cost per unit time.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.133-142
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• 2018

Subway metro scheduling is one of the most important problems impacting passenger convenience today. To operate efficiently, the Seoul metro uses regular, periodic schedules for all lanes, both north and southbound. However, many past studies suggest that non-periodic scheduling would better optimize costs. Since the Seoul metro is continuously facing a deficit, adopting a non-periodic schedule may be necessary. Two objectives are presented; the first, to minimize the average passengers' waiting time, and the second, to minimize total costs, the sum of the passenger waiting time, and the operational costs. In this paper, we use passenger smart card data and a precise estimation of transfer times. To find the optimal time-table, a genetic algorithm is used to find the best solution for both objectives. Using Python 3.5 for the analysis, for the first objective, we are able to reduce the average waiting time, even when there are fewer trains. For the second objective, we are able to save about 4.5 thousand USD with six fewer trains.

• Environmental and Resource Economics Review
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• v.11 no.4
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• pp.659-687
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• 2002

This study used a discrete choice model to investigate an association between air pollution and hospital visits for acute respiratory symptoms with the national health interview survey conducted in 1998 in South Korea. The results showed that $NO_2$ and TSP were significantly related to hospital visits in a single-pollutant model, but when they were simultaneously considered, only $NO_2$ remained significant. It was estimated as $NO_2$ level increased by 10%(0.0027ppm) from 0.027ppm (the mean $NO_2$ level), hospital visits increase by 0.176%. This study also measured respondent's out-of-pocket expense and the time cost for commuting and waiting for the visit. We found that on the average, out-of-pocket expense is 5,600 won per hospital visit, but the total cost per hospital visit is measured at 33,440 won with time cost of commuting and waiting at 27,840 won. Time cost was over 63.6~83.3% of the total cost per hospital visit.

• IE interfaces
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• v.13 no.3
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• pp.424-430
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• 2000

Supply chain management (SCM) is an area that has recently received a great deal of attention in the business community. While SCM is relatively new, the idea of coordinated planning is not. During the last decades, many researchers have investigated multi-stage inventory problems. However, only a few papers address the problem of cost-optimal coordination of multi-stage inventory control with respect to service measures. Even published approaches have a shortcoming in dealing with a delivery lead time consisted of a shipping time and a waiting time. Assumed that there is no waiting time, or that the delivery lead time is implicitly compounded of a shipping time and a waiting time, the problem is often simplified into a multi-stage buffer allocation and a single-stage stochastic buffer sizing problem at all installations. This paper presents a simulation-based heuristic algorithm and a comparison with others for the problem that cannot be decomposed into a multi-stage buffer allocation and a single-stage stochastic buffer sizing problem because the waiting time ties together all stages. The comparison shows that the simulation-based heuristic algorithm performs better than other approaches in saving average inventory cost for both Poisson and Normal demands.

• Journal of Korean Port Research
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• v.5 no.2
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• pp.19-37
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• 1991

This work aims to : establish a model of the container physical distribution system of Pusan port comprising 4 sub-systems of a navigational system, on-dock cargo handling/transfer/storage system, off-dock CY system and an in-land transport system : examine the system regarding the cargo handling capability of the port and analyse the cost of the physical distribution system. The overall findings are as follows : Firstly in the navigational system, average tonnage of the ships visiting the Busan container terminal was 33,055 GRT in 1990. The distribution of the arrival intervals of the ships' arriving at BCTOC was exponential distribution of $Y=e^{-x/5.52}$ with 95% confidence, whereas that of the ships service time was Erlangian distribution(K=4) with 95% confidence, Ships' arrival and service pattern at the terminal, therefore, was Poisson Input Erlangian Service, and ships' average waiting times was 28.55 hours In this case 8berths were required for the arriving ships to wait less than one hour. Secondly an annual container through put that can be handled by the 9cranes at the terminal was found to be 683,000 TEU in case ships waiting time is one hour and 806,000 TEU in case ships waiting is 2 hours in-port transfer capability was 913,000 TEU when berth occupancy rate(9) was 0.5. This means that there was heavy congestion in the port when considering the fact that a total amount of 1,300,000 TEU was handled in the terminal in 1990. Thirdly when the cost of port congestion was not considered optimum cargo volume to be handled by a ship at a time was 235.7 VAN. When the ships' waiting time was set at 1 hour, optimum annual cargo handling capacity at the terminal was calculated to be 386,070 VAN(609,990 TEU), whereas when the ships' waiting time was set at 2 hours, it was calculated to be 467,738 VAN(739,027 TEU). Fourthly, when the cost of port congestion was considered optimum cargo volume to be handled by a ship at a time was 314.5 VAN. When the ships' waiting time was set at I hour optimum annual cargo handling capacity at the terminal was calculated to be 388.416(613.697 TEU), whereas when the ships' waiting time was set 2 hours, it was calculated to be 462,381 VAN(730,562 TEU).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.153-162
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• 1997

M/G/1 queueing system is one of the most widely used one to model the real system. When operating a real systems, since it often takes cost, some control policies that change the operation scheme are adopted. In particular, the N-policy is the most popular among many control policies. Almost all researches on queueing system are based on the assumption that the arrival rates of customers into the queueing system is constant, In this paper, we consider the M/G/1 queueing system whose arrival rate varies according to the servers status : idle, set-up and busy states. For this study, we construct the steady state equations of queue lengths by means of the supplementary variable method, and derive the PGF(probability generating function) of them. The L-S-T(Laplace Stieltjes transform) of waiting time and average waiting time are also presented. We also develop an algorithm to find the optimal N-value from which the server starts his set-up. An analysis on the performance measures to minimize total operation cost of queueing system is included. We finally investigate the behavior of system operation cost as the optimal N and arrival rate change by a numerical study.

• Water for future
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• v.12 no.1
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• pp.60-71
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• 1979

In general, the lock structure is built at place of having great deal range for dealing with effective ships operation such as in the west coast of Korea. In Inchon harbour, the two locks of 10 KT and 50 KT tonnage class in total has been constructed for several years age, however, it has been recorded many waiting vessels at outer harbour due to the increment of oceangoing vessels & shortage of berthing facilities in accordance with beyond expectation of cargo amount increment. This paper attempts to solves the waiting vessels problem at outer harbour by simulation in applying queing theory. It is found that the simulation results such as average queue time, service time, and queue length during lock operation can be applied to find the minimum of the cost function for determination of optimum Lock Size

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.147-155
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• 2016

Industry 4.0's goal is the 'Smart Factory' that integrates and controls production process, procurement, distribution and service based on the fundamental technology such as internet of the things, cyber physical system, sensor, etc. Basic requirement for successful promotion of this Industry 4.0 is the large supply of semiconductor. However, company I who produces dicing blades has difficulty to meet the increasing demand and has hard time to increase revenue because its raw material includes high price diamond, and requires very complex and sensitive process for production. Therefore, this study is focused on understanding the problems and presenting optimal plan to increase productivity of dicing blade manufacturing processes. We carried out a study as follows to accomplish the above purposes. First, previous researches were investigated. Second, the bottlenecks in manufacturing processes were identified using simulation tool (Arena 14.3). Third, we calculate investment amount according to added equipments purchase and perform economic analysis according to cost and sales increase. Finally, we derive optimum plan for productivity improvement and analyze its expected effect. To summarize these results as follows : First, daily average blade production volume can be increased two times from 60 ea. to 120 ea. by performing mixing job in the day before. Second, work flow can be smoother due to reduced waiting time if more machines are added to improve setting process. It was found that average waiting time of 23 minutes can be reduced to around 9 minutes from current process. Third, it was found through simulation that the whole processing line can compose smoother production line by performing mixing process in advance, and add setting and sintering machines. In the course of this study, it was found that adding more machines to reduce waiting time is not the best alternative.