Research On NET Present Value Of Resource Constrained Project Scheduling Problem With Robustness Consideration

Traditionally,the vast majority research on the resource constrained project scheduling problem(RCPSP)in the literature has been developed with the objective of minimizing the project makespan or minimizing the project costs,unfortunately,the financial objective of maximizing the project net present value(Max-NPV)has been largely ignored.However,with higher interest rates and more expensive financing costs,it is significant to generate a reasonable baseline schedule in an effort to obtain the maximum NPV for the contractor or the client.But in today's rapidly changing marketplace,the traditional baseline schedule,assuming a static and deterministic environment,can no longer deal with the various uncertainties and risks.Robust project scheduling,emerging as an effective approach for solving project scheduling problem under uncertainty,has attracted an ever-growing attention.However robust project scheduling has not looked further into the important aspect:the project NPV.Therefore,based on the synthetical application of robust project scheduling,risk management,dynamic programming,robust resource allocation,time buffer management,Monte Carlo simulation,as well as heuristic algorithms,this paper performs a systematic study of the project NPV problem with robustness consideration in which the activity durations are uncertain.The main research contents are outlined as follows:To begin with,a deterministic Max-NPV model is constructed,which meets the procedure,resource,project deadline as well as the rate of return constraints.Then a simulated annealing algorithm is designed to solve the model proposed above to obtain an approximately optimal baseline schedule.Finally the baseline schedule is executed in a simulation experiment.The computational experiment results indicate that the duration variability has an adverse effect on the project NPV.In addition,it is obvious that the higher the activity duration variability,the more risks of the project NPV will be faced with,which provides effective datas and theory basis for further investigating the project NPV problem under uncertain.Secondly,in view of the problem that the propagations of activity delay through the project network as well as the resource flow network resulte in a kind of "snowball" effect.Firstly,a dynamic model of resource flow network optimization under the objective of minimizing the expected penalty cost(EPC)is developed.And then an algorithm is then proposed to solve the model,in which resources are transferred from one activity to another with a minimal EPC to improve the schedule robustness.An extensive computational experiment is carried out to validate the effectiveness and practicability of the proposed algorithm by comparison with another three algorithms.Thirdly,risk managemen is presented to reduce the loss risks of the project NPV where the activity durations are uncertain.Firstly of all,the risk probability of the activity delay is identified using the dynamic programming.Next,an index is proposed to calculate the risk loss of the project NPV quantitatively.And then a time buffer procedure is developed to allocate time buffers iteratively in the front of the activities with relative larger risk loss,which acts as time cushions to protect as much as possible the schedules against disruptions.Results of an extensive experiment present that the robust project schedules generated by our method can provide effective protection compared with the un-buffered ones,especially for higher level of the activity duration variability.Fourthly,two indexes are proposed to measure quality robustness as well as solution robustness of the schedules from the aspect of the project NPV.And then a project NPV optimization model with composite robustness consideration is projected to strike a balance between quality robustness and solution robustness.A two-stage algorithm with the integration of simulated annealing and tabu search is developed to obtain the optimal solution of the proposed model.Our simulation experiment indicates the superiority of the two-stage algorithm to the single stage algorithm.Furthermore,the composite robust project schedules not only can achieve a remarkable performance of the project NPV,but also can ensure the schedule stability compared with the correspondingly single robust project schedules.Finally,an extensive study of the integrate optimization of robust resource allocation and time buffer management is carried out.First,a three-stage integrate optimization procedure is proposed to generate more robust project schedules.Then an integrated optimization scheme that combines four-field classification:the initial schedule,resource allocation,time buffer insertion as well as the priority rule in reactive scheduling is introduced to describe 32 different integrate optimization procedures from the two aspects of robustness and un-robustness.The simulation experiment displays the affects of the four-field classification on the schedule robustness.Meanwhile the experiment presents the relative dominance of our integrate optimization procedure over the other procedures with the respect to the robustness performance.