This paper presents some simulation‐oriented techniques, particularly the resource allocation point (RAP) heuristic rule, for an activity‐based construction (ABC) simulation that requires only one kind of element to model construction operations. RAP heuristic rule provides the simulation with the decision‐making ability for allocating limited resources during simulation. Predefined entity management strategies control the movements of simulation entities so as to model some complex features of construction operations. An activity object‐oriented (AOO) simulation strategy based on object‐oriented approach for the implementation of the ABC simulation by regarding activities as objects controls the mechanism of the ABC simulation by checking only relevant activities at certain time, other than checking all activities for each simulation time unit. An easy‐to‐use animation aims at enhancing understanding of simulation and assisting modellers in verifying and validating model.
The purpose of this paper is to propose a variant of superiority and inferiority ranking (SIR) method called SIR‐Grey for determining the location of large‐scale…
The purpose of this paper is to propose a variant of superiority and inferiority ranking (SIR) method called SIR‐Grey for determining the location of large‐scale harbour‐front project development.
The study is illustrated with an application example obtained from the Environmental Protection Department of the Hong Kong Special Administrative Region to demonstrate the concept and application procedure of SIR‐Grey. The strengths and weaknesses of SIR‐Grey are highlighted when compared with the traditional weighted average approach.
The strengths and weaknesses of SIR‐Grey are highlighted when compared with the traditional weighted average approach. Among the strengths, the global comparison scores of SIR‐Grey can give a clearer and easier comprehensible algorithm. Further, the global comparison generated from superiority flows (S‐flows) [S‐flow: A is preferred to A′(AP>A′) or A is indifferent to A′(AI>A′)] and inferiority flows (I‐flows) [I‐flow: A is preferred to A′(AP<A′) or A is indifferent to A′(AI<A′)] can be used to select a solution matching the nature of the problem; e.g. a conservative approach can adopt the ranking from I‐flow because the selected option will have the criteria farthest from the virtual worst site while the ranking from S‐flow can be adopted for an aggressive approach because the final decision will have the criteria closest to the virtual perfect site. Regarding the weaknesses, the major one is the requirement of a full appreciation of the nature of criteria in setting the thresholds and preference structure, which may complicate the application of the model.
This study proposes a variant of SIR method called SIR‐Grey for determining the location of large‐scale harbour‐front project development. This approach can overcome the problem encountered in using other methods which could lead to variation in the final ranking and hence an inconsistent result.
The purpose of this paper is to provide a method that can better evaluate the credit risk (CR) under PPP project finance.
The principle to evaluate the CR of PPP projects is to calculate three critical indicators: the default probability (DP), the recovery rate (RR) and the exposure at default (EAD). The RR is determined by qualitative analysis according to Standard & Poor’s Recovery Scale, and the EAD is estimated by NPV analysis. The estimation of the DP is the focus of CR assessment because the future cash flow is not certain, and there are no trading records and market data that can be used to evaluate the credit condition of PPP projects before financial close. The modified CreditMetrics model and Monte Carlo simulation are applied to evaluate the DP, and the application is illustrated by a PPP project finance case.
First, the proposed method can evaluate the influence of the project’s cash flow uncertainty on the potential loss of the bank. Second, instead of outputting a certain default loss value, the method can derive an interval of the potential loss for the bank. Third, the method can effectively analyze how different repayment schedules and risk preference of banks influence the evaluating result.
The proposed method offers an approach for the bank to value the CR under PPP project finance. The method took into consideration of the uncertainty and other characteristics of PPP project finance, adopted and improved the CreditMetrics model, and provided a possible loss range under different project cash flow volatilities through interval estimation under certain confident level. In addition, the bank’s risk preference is considered in the CR evaluating method proposed in this study where the bank’s risk preference is first investigated in the CR evaluating process of PPP project finance.