Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

Abstract

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS

“…GA has the capability of simultaneous evaluation of many points in the search area, which increases the probability of finding the global solution of the problem [27][28][29][30]. GA and PSO methods for the model have been explained in [17]. By hybridizing GA and PSO, the natural capabilities (balancing between exploration and exploitation and combinatorial problem solving) of these search methods can promise better performance in the problem involved [17].…”

“…GA and PSO methods for the model have been explained in [17]. By hybridizing GA and PSO, the natural capabilities (balancing between exploration and exploitation and combinatorial problem solving) of these search methods can promise better performance in the problem involved [17]. Fuzzy hybrid GA-PSO is a method which got some of the PSO parameters and some of the GA operators in fuzzy mode to improve the quality of results.…”

“…A particle is every potential sequence of operations, and each operation is represented by a particle dimension. Three sub-steps for encoding a particle are as follows (for more details refer to [17]): 1) Applying smallest position value (SPV) rule. SPV is a rule that facilitates transformation of the continuous PSO algorithm to discrete cases applicable to all types of the scheduling problem [31].…”

“…The GA mutation operator initiates extra variability to maintain the diversity within a population. Mutation operator is not applied on immune chromosomes (more details in [17]). …”

“…GA and PSO are two well-known metaheuristic methods in optimization and both have remarkable capabilities such as 'balancing between exploration and exploitation' and 'combinatorial problem solving'. Hence, by integrating the advantages of the compensatory properties of PSO and GA their hybrid can yield better results [15][16][17].…”