Dealing with Uncertainty in the MRCPSP/Max Using Discrete Differential Evolution and Entropy

In this paper, we investigate the characterization of MRCPSP/max under uncertainty conditions and emphasize managerial ability to recognize and handle positively disruptive events. This proposition is then demonstrated using the entropy approach to find disruptive events and response time intervals. The problem is solved using a resilient characteristic of the three-stage procedure gauged by schedule robustness and adaptivity; the resulting schedule absorbs the impact of an unexpected event without rescheduling during execution. The use of the differential evolution algorithm, known as DDE, in a discrete manner is proposed and evaluated against the best known optima (BKO). Our findings indicate the DDE is effective overall; moreover, compared against the BKO for every stage, the most significant difference is that the stability of the solutions provided by DDE under the three-stage framework proves to be sufficiently robust when practitioners add response times at certain range levels, in this case from 8% to 15%.

Automated summary

This paper investigates the characteristics of MRCPSP/max under uncertainty and emphasizes the importance of managerial ability to recognize and handle disruptions effectively. Using the entropy approach, disruptive events and response time intervals are identified. The problem is solved using a resilient three-stage procedure that measures schedule robustness and adaptivity. The differential evolution algorithm (DDE) is proposed and evaluated against the best known optima, showing its effectiveness in managing disruptions. The stability of solutions provided by DDE is particularly robust when response times are added within a certain range.