Hybrid Gs-Ga Algorithm for Parallel Machine Scheduling Problem with Different Transport Vehicles

Abstract

This paper studies the scheduling problem of the same kind of parallel machines with different transportation vehicle capacity. The scheduling problem model is composed of a single warehouse and multiple manufacturers located in different geographical locations. Each manufacturer has the same batch processor, and several vehicles assemble the jobs in the warehouse in batches, transport them to different manufacturers and return them. The manufacturer of vehicle transportation depends on the destination of the jobs when the code is generated. At the manufacturer’s place, because the capacity of the vehicle may exceed the maximum capacity of the batch processor, the job will be re batch for processing after it arrives. Through modeling, a 2n dimensional code is designed, and the gravitational search algorithm is introduced. The mutation and crossover process of genetic algorithm are added. A hybrid genetic algorithm based on gravitational search is proposed. The algorithm is used to optimize the sum of manufacturing time span and transportation cost. The effectiveness of the algorithm is verified by different scale simulation experiments.

A Heuristic Integrated Scheduling Algorithm via Processing Characteristics of Various Machines

“…The makespan is 185 working hours. Refs [34][35][36][37][38][39][40] are all studies on the integrated scheduling problem with machine as the research object. As the latest algorithm, the algorithm (RSVHPSA) in Ref [37] is selected to be compared and analyzed with the proposed algorithm.…”

“…Refs. [34][35][36][37][38][39][40] are all studies on the integrated scheduling problem with machine as the research object. As the latest algorithm, the algorithm (RSVHPSA) in Ref.…”

“…Nowadays, integrated scheduling is widely used in many fields (see Refs. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]). For example, for the flexible integrated scheduling problem, Yang D. et al [19] proposed an improved artificial bee colony algorithm considering MSE with two new strategies.…”