Research On The Multiple Objective Optimization Of Project Scheduling Under Uncertain Resource Conditions

The classical Resource-constrained Project Scheduling Problem (RCPSP) aimsto achieve the objective of minimizing the project makespan by determining the realstart time of each activity and allocate resources to each activity, under the constraintsof certain activity duration and resource demand as well as the temporal constraintsbetween some activities. However, the assumption of the classical RCPSP model hasmany restrictions in practical applications. Especially, when the duration and theresource demand to accomplish an activity used to be fuzzy, there is a lot more thanthe minimization of project makespan to take into consideration, such as the trade-offbetween the makespan minimization and the efficiency of resource utilizationmaximization. Hence, there is not only theoretical significance, but also crucialpractical application value in researches on the project scheduling multi-objectiveoptimization under uncertain resource constraints.This paper expands the assumption of the duration and resources of the classicalRCPSP from the certain to the fuzzy uncertain, and extends the goal from themakespan minimization into the time-resource trade-off. It studies the modeling andsolving methods of a series of problems in project scheduling multi-objectiveoptimization under uncertain resource constraints. The main contents and innovativework include:Firstly, an improved fuzzy maximum operator and the fuzzy subtraction operatorare proposed for determining the fuzzy time parameters in fuzzy network under thecondition of fuzzy activity time. The improved methods thereby on the one hand,overcome the problem in the existing work which did not consider the fact that thecritical path may change in case of fuzzy activity time; on the other hand, theysuccessfully avoid generating negative and infeasible solution in the traditionalbackward recursive calculation.Secondly, it is advanced to use an improved fuzzy numbers ranking method todeal with the fuzzy uncertain condition of both the activity time and resource demand.This improved method ranks the fuzzy numbers based on the left and right dominanceand the decision maker’s optimistic attitude. In order to solve this problem, a model ofthis problem is built, and a fuzzy parallel scheduling-based genetic algorithm isproposed. Besides, an example is put forward to demonstrate the rationality of themodel and the effectiveness of the proposed fuzzy parallel genetic algorithm.Thirdly, an innovative resource leveling metric of resource fluctuation cost is presented on ways to improve the efficiency of the resource utilization and to measurethe resource fluctuation. The new metric can directly measure and minimize thenegative effect of resource fluctuation on the project construction productivity andcost by either allowing resource idle or adopting the fire-and-rehire resourceutilization strategy under the circumstance of unfavorable resource fluctuation. Afterconsidering the actual characteristics of the resource leveling problem, an improvedgenetic algorithm is presented to solve this model, and the encoding code of thisalgorithm is improved, meanwhile an actual project example is illustrated todemonstrate the rationality of the model and the effectiveness of the proposedalgorithm.Finally, the resource constrained time-resource fluctuation cost multipleoptimization tradeoff project scheduling problem with fuzzy time and fuzzy resourceis studied. In this way, the model of fuzzy resource constrained project schedulingmulti-objective optimization problem is built, an improved non-dominated sortinggenetic algorithm is designed to address this problem, and later an actual example isillustrated to demonstrate the rationality of the model and the effectiveness of theproposed algorithm.