Research To Solve Complex Scheduling And Vehicle Routing The Pipeline Issue Hybrid Intelligent Algorithm

Production scheduling are often of some complexities, such as large-scale, uncertainty and Multi-Objective. The study on the scheduling algorithms of production scheduling is always a hot topic in academic and engineering fields. Differential evolution (DE) is a novel evolutionary technique proposed for optimizing complex problems over a continuous domain. Population-based incremental learning (PBIL) algorithm is one of the estimation of distribution (EDA) algorithm, which could guide the population exploit solution space. In this paper, DE and PBIL are used to solve complex FSP and VRP respectively.(1) An efficient differential evolution approach, namely DE_NTJ, is presented to minimize the number of tardy jobs (NTJ) for the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs), which is a complex problem and can be abbreviated as NTJ-NFSSP with SDSTs and RDs.(2) An efficient PBIL is presented to minimize the distance of vehicles with capacity limit, encoding mechanism and probability model are designed as well as scheduling simulation software.(3) This part proposes a hybrid population-based incremental learning algorithm, namely HPBIL, to simultaneously minimize the number of vehicles and total travel distance for the vehicle routing problem with time windows (VRPTW).Simulation results demonstrate the effectiveness of the above proposed algorithm.