어떤 해역의 해상교통혼잡도를 평가하는 데 있어서 단위 시간당 항행 척수인 교통량을 분석하는 것보다 어떤 시간 단면에 존재하는 단위 면적당의 밀집도 분석을 활용하는 것이 합리적일 수 있다. 본 연구에서는 해상교통안전진단 대행기관의 해상교통혼잡도 평가기법을 표준화하고, 선박톤급별 환산교통량 사용으로 인한 평가오차를 최소화하기 위하여 새로운 방안을 찾고자 한다. 이를 해결하기 위해 선박자동식별장치(Automatic Identification System, AIS)의 통항선박 데이터를 활용하여 항로구간면적 대비 식별된 개개의 통항선박이 갖고 있는 점용영역의 면적을 합산한 값과의 백분율을 해상교통혼잡도로 평가하는 방안을 제시하였다. 새로운 모형에서는 정보통신기술의 획기적인 발달로 인해 실제 데이터 사용이 가능하여 환산 데이터에 의한 오차발생을 줄일 수 있고, 항로구간별 해상교통혼잡도 평가도 가능하게 되었다.

Different from general operating policies to be applied for controllable queueing models, two of three well-known simple N, T and D operating policies are applied alternatingly to the single server controllable queueing models, so called alternating (NT), (ND) and (TD) policies. For example, the alternating (ND) operating policy is defined as the busy period is initiated by the simple N operating policy first, then the next busy period is initiated by the simple D operating policy and repeats the same sequence after that continuously. Because of newly designed operating policies, important system characteristic such as the expected busy and idle periods, the expected busy cycle, the expected number of customers in the system and so on should be redefined. That is, the expected busy and idle periods are redefined as the sum of the corresponding expected busy periods and idle periods initiated by both one of the two simple operating policies and the remaining simple operating policy, respectively. The expected number of customers in the system is represented by the weighted or pooled average of both expected number of customers in the system when the predetermined two simple operating policies are applied in sequence repeatedly. In particular, the expected number of customers in the system could be used to derive the expected waiting time in the queue or system by applying the famous Little’s formulas. Most of such system characteristics derived would play important roles to construct the total cost functions per unit time for determination of the optimal operating policies by defining appropriate cost elements to operate the desired queueing systems.

A steady-state controllable M/G/1 queueing model operating under the {T:Min(T,N)} policy is considered where the {T:Min(T,N)} policy is defined as the next busy period will be initiated either after T time units elapsed from the end of the previous busy period if at least one customer arrives at the system during that time period, or after T time units elapsed without a customer’ arrival, the time instant when Nth customer arrives at the system or T time units elapsed with at least one customer arrives at the system whichever comes first. After deriving the necessary system characteristics including the expected number of customers in the system, the expected length of busy period and so on, the total expected cost function per unit time for the system operation is constructed to determine the optimal operating policy. To do so, the cost elements associated with such system characteristics including the customers’ waiting cost in the system and the server’s removal and activating cost are defined. Then, procedures to determine the optimal values of the decision variables included in the operating policy are provided based on minimizing the total expected cost function per unit time to operate the queueing system under considerations.

A steady-state controllable M/G/1 queueing model operating under the (TN) policy is considered where the (TN) policy is defined as the next busy period will be initiated either after T time units elapsed from the end of the previous busy period if at least one customer arrives at the system during that time period, or the time instant when Nth customer arrives at the system after T time units elapsed without customers’ arrivals during that time period. After deriving the necessary system characteristics such as the expected number of customers in the system, the expected length of busy period and so on, the total expected cost function per unit time in the system operation is constructed to determine the optimal operating policy. To do so, the cost elements associated with such system characteristics including the customers’ waiting cost in the system and the server’s removal and activating cost are defined. Then, the optimal values of the decision variables included in the operating policies are determined by minimizing the total expected cost function per unit time to operate the system under consideration.

Using the known result of the expected busy period for the triadic Med (N, T, D) operating policies applied to a controllable M/G/1 queueing model, its upper and lower bounds are derived to approximate its corresponding actual values. Both bounds are represented in terms of the expected busy periods for the dyadic Min (N, T), Min (N, D) and Min (T, D) or Max (N, T), Max (N, D) and Max (T, D) with the simple N, T and D operating policies without using any other types of triadic operating policies such as Min (N, T, D) and Max (N, T, D) policies. All three input variables N, T and D are equally contributed to construct such bounds for estimation of the expected busy period.

The most generalized form of the triadic operating policy for a controllable M/G/1 queueing model is analyzed to obtain fundamental relations among the other forms of operating policies based on its corresponding expected busy period. Since it consists of

Using the known result of the expected busy period for a controllable M/G/1 queueing model operating under the triadic Max (N, T, D) policy, its upper and lower bounds are derived to approximate its corresponding actual value. Both bounds are represented

Based on the known results of the expected busy periods for the triadic Min (N, T, D) and Max (N, T, D) operating policies applied to a controllable M/G/1 queueing model, a relation between them is constructed. Such relation is represented in terms of the

Us ing the known result of the expected bllsy period for the triadic Min (N, T, 0) operating po licy applied to a controllable M/GI1 queueing model, its upper and lower bounds are derived to approximate its corresponding ac tual value. 80th bounds are rep

In a technological driven environment, a depreciation estimate which is based on traditional like analysis results in a decelerated rate of capital recovery This time pattern of technological growths models needs to be incorporated into life analysis fram

The most generalized form of the triadic operation policy for an M/G/1 queueing model is developed It consists of three simple N, T and D operating policies and has a peculiar structure possessing concepts of dyadic policies Using the concept of the pseud

Using the results of the expected busy periods for the dyadic Min(N, D) and Max(N, D) operating policies in a controllable M/G/1 queueing model, an important relation between them is derived. The derived relation represents the complementary property betw

Estimation of mortality behavior of a industrial property are useful for calculating depreciation and making management decisions relating to property. The common methods of computing depreciation require an estimation of service life, and some methods ma

  The expected busy period for the controllable M/G/1 queueing model operating under the triadic policy is derived by using the pseudo probability density function which is totally different from the actual probability density function. In order to justif

  일반 투자안의 의사결정에서와 마찬가지로 산업설비의 경제성 분석에서도 가장 중요한 결정 요소 중의 하나가 설비의 생존곡선 추정이다. 설비의 자산 가치가 감소하는 원인은 여러 가지가 있으나, 여러 원인 중 물리적 훼손이 과거의 산업설비에서는 가장 중요한 원인이었으므로 기존의 생존모형 분석에서는 Iowa 생존곡선을 이용하여 설비의 생존곡선을 추정하였다. 그러나 새로운 기술상의 변화로 인한 첨단 생산시스템의 설비교체 분석시에는 적합지 않다. 따라서, 본 연