Concrete Vehicle Scheduling Based on Immune Genetic Algorithm

Abstract

At present, the demand for ready-mixed concrete (RMC) in construction industry is increasing day by day, and the supply mode of multiple delivery depots corresponding to multiple construction sites has been widely used. In order to further improve the joint distribution efficiency between various delivery depots, this research establishes a multiobjective optimal distribution model with time window constraints and demand postponement attributes for the problem that the subbatching plants need to work together. The model divides the reasons for demand postponement into two types: the constraint for timely unloading of trucks cannot be met on time and the constraint for timely pouring at the construction site cannot be met on time. This work improved the coding method of genetic algorithm based on the characteristics of the distribution model. Using hierarchical real-coding form, the coding operator of each layer can be evolved separately, which ensures the globality of the search, and, at the same time, an improved immune operator is added to ensure the local search performance. By comparison, the results obtained by improved GA are 7.05% higher than those of the standard GA, and the early convergence speed of improved GA is obviously better than that of the standard GA. The simulation experiments show that the total trucks’ waiting time during the process of providing delivery services from 5 concrete plants to 8 construction sites is 769 minutes, and the total waiting time of 8 construction sites is 507 minutes. Through practical case analysis, this work can enable RMC production enterprises and construction sites to effectively reduce the waiting time of corresponding operations, and the obtained results are close to the simulation results. The proposed method indeed improves the efficiency of RMC distribution.

Two-Stage Optimization Model of Centralized Energy Storage Participating in Peak Shaving with Maximum Reserve Capacity and Minimum Carbon Emission of the System

“…Te obtained results in the second stage are returned to the model of the frst stage, and the two models iterate alternately to fnally obtain the result. Te immune genetic algorithm [27] is used to solve the frst stage problem, and the CPLEX solver in MATLAB is used to solve the second stage problem. In this paper, the reserve capacity of BESS is optimized with the objective of minimizing the risk cost of wind power and the operation cost of thermal power units after the thermal power output and BESS charging and discharging power are determined.…”

“…Te multiobjective algorithm for the two-stage model proposed in this paper is the immune genetic algorithm (IGA) [27]. In order to show the superiority of IGA in solving the model, the calculation results of IGA and of the traditional genetic algorithm (GA) were compared under the same setting of relevant parameters.…”

Half mirror algorithm: a metaheuristic that hybridizes swarm intelligence and evolution-based system

“…Ironically, the evolution-based metaheuristic was introduced earlier than the swarm intelligence. Moreover, the genetic algorithm, as the most popular evolution-based metaheuristic, is still utilized in many recent studies regarding optimization, such as for power system stabilizers [1], path planning [31], vehicular ad-hoc network [32], text encryption [33], vehicle scheduling [34], credit rating system [35], course timetabling [36], cloud system [37], and so on. This circumstance becomes the primary motivation of this work in developing a new evolution-based metaheuristic.…”