Multiobjective Project Scheduling with Multiple Compression Capabilities of the Multistate Activities with Project Reliability Approach using the Metaheuristic Algorithm

Abstract

We examine the time-cost-reliability-project balance of the project by considering the limitations of renewable and nonrenewable resources and performing multistate group activities in the same conditions and with the possibility of multiple compression activities. This study aimed to select the best mode for conducting actions in each subset and find the best execution method by determining the number of compression time units of actions to maximize project reliability and minimize project completion risk, time, and cost. Drilling projects will also be implemented for evaluation in the Azar oil field. The exact Epsilon Limitation method was used to solve this problem in low dimensions. The combined genetic metaheuristic algorithm and particle swarming were used to solve high dimensions problems. The low-dimensional Epsilon solution method could clearly balance the objective functions. Also, the Taguchi experimental design method was used to adjust the parameters of the problem algorithm. The correct value of the algorithm parameters is also specified. Based on the results, the hybrid metaheuristic algorithm has less solution time than the exact method. In high dimensions, as it was observed, GAMS software could not solve the problem. Still, the hybrid metaheuristic algorithm solved the problem well in high dimensions.