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a b s t r a c tIn project investment decisions, it is often assumed that estimated values of project parameters are certain and they would not deviate by the time. However, project parameters normally change during a life cycle of the project. Therefore, an existence of a deviation or gap between forecasted values and actual values is inevitable. Because of the uncertainty of the future, forecasting the true and exact values of project parameters is almost impossible. In this study, an integrated decision support approach based on simulation and fuzzy set theory is proposed for project investors in risky and uncertain environments. This approach determines the risk levels of the projects and helps investors to make investment decisions. In the scope of the study, a flowchart is presented to guide to decision maker in different situations of information uncertainty that belongs to project parameter values. Via this flowchart, the values of project parameters can be chosen depending on how they are determined (deterministic, stochastic or fuzzy) by project analyst. Besides, calculating and analyzing the project risk in all possible situations would be easier. Illustrative examples are given to demonstrate the application of this approach.

This paper deals with the problem of scheduling additional train unit (TU) services in a double parallel rail transit line, and a mixed integer programming (MIP) model is formulated for integration strategies of new trains connected by TUs with the objective of obtaining higher frequencies in some special sections and special time periods due to mass passenger volumes. We took timetable scheduling and TUs scheduling as an integrated optimization model with two objectives: minimizing travel times of additional trains and minimizing shifts of initial trains. We illustrated our model using computational experiments drawn from the real rail transit line 16 in Shanghai and reached results which show that rail transit agencies can obtain a reasonable new timetable for different managerial goals in a matter of seconds, so the model is well suited to be used in daily operations.

Enterprises are confronted with several project alternatives that they assume to gain revenue in the future, but their own economical resources are limited to carry out all alternatives. Therefore, a decision process arises to prioritize and select among alternatives according to the predetermined goals and criteria to reach the maximum utilization. On the other hand, in project evaluation, the values of project parameters are often assumed to be known with complete certainty. However, project parameters normally change during a life cycle of the project, and it is necessary to consider uncertainty and risk phenomena while evaluating projects. Simulation-based project evaluation approaches enable to make more reliable investment decision since they permit to include future uncertainty and risk in analysis process. In this article, a novel simulation-based optimal decision approach is proposed for evaluating and comparing investment projects under uncertain and/or risky environments. The phases of the proposed approach are; (a) developing the effectiveness measure formulation of a project, (b) identifying and checking all controllable project parameters that affect the measure, (c) developing simulation model for the measure, and (d) performing the project ranking and selection procedures in order to rank and select the projects. Three ranking and selection procedures, previously used for comparing performances of the different production/service systems, are embedded in the proposed approach.