Uncertain Random Dependent-Chance Programming for Flow-Shop Scheduling Problem

In this paper, a type of uncertain random programming model based on the chance measure for the permutation flow shop (PFSP) scheduling problem is proposed with uncertain random job's processing times, i.e., dependent chance programming model (DCPM). The objective is to minimize the total wasted energy consumption induced by the machine idling. Moreover, to solve the proposed model, the uncertain random simulation and a two-stage eagle strategy (ES) are integrated to produce a hybrid intelligent algorithm. In the first stage of ES, the so-called Lévy Flights is employed as the global search algorithm. While in the second stage, the grey-wolf optimizer (GWO) is used as the local search algorithm. The generated hybridization ensures the proper balance between exploration and exploitation. Besides, the Variable Neighborhood Search (VNS) is adopted as local search methods to improve the performance of the highlighted algorithm. The numerical results are reported to demonstrate the applicability of the proposed model.