Integrating Chaotic Initialized Opposition Multiple-Objective Differential Evolution and Stochastic Simulation to Optimize Ready-Mixed Concrete Truck Dispatch Schedule

Abstract

Delivering ready-mixed concrete (RMC) efficiently to construction sites is a practical concern and one of the most challenging tasks for RMC batch managers. Batch plant managers must consider both time and order factors in order to set an RMC truck dispatch schedule that successfully balances batch plant (supplier) and construction site (customer) priorities. This paper develops an optimization framework that integrates discrete event simulation (DES) and multiobjective differential evolution (MODE) to determine the solutions for RMC truck dispatch scheduling. The model takes into consideration uncertainties as well as unexpected situations such as truck breakdowns during delivery. In addition, the chaotic initialized opposition multiobjective differential evolution (COMODE) algorithm is used to optimize the dispatching schedule, which minimizes the total waiting duration both of RMC trucks at construction sites and of construction sites for trucks. A batch plant case study is used to illustrate the capability of the new DES-COMODE algorithm, with results showing that DES-COMODE-generated nondominated solutions can assist batch plant managers to set efficient truck dispatch schedules in a timely manner, a task both difficult and time-consuming using current methods. Results demonstrate that DES-COMODE is superior to four currently used algorithms, including the nondominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC) in terms of efficiency and effectiveness.