A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Wenxiang Xu,Shunsheng Guo

doi:10.3390/su11051329

Abstract

Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved.

Highlights

With the development of the manufacturing industry and the changes of the competitive market, meeting customer’s diverse demands and improving service quality has become the main direction of shifting their strategy for manufacturing enterprises, and flexible manufacturing system (FMS) become an effective way to meet these needs
An FMS consists of material handling devices (automated guided vehicles (AGVs) and robots), workstations, automated storage systems, and so on
Equation (1) defines the calculation method of MS, which can be represented by the maximum finish time of the operations assigned to each machine, the maximum finish time of traveling tasks assigned to each AGV (i.e., max{AFTl+T(k,Nk)}), the maximum finish time of operations of each job (i.e., max{ot(i)}) or the maximum finish time of traveling for each job (i.e., max{tt(i)+ot(i,mi)})

Summary

Introduction

With the development of the manufacturing industry and the changes of the competitive market, meeting customer’s diverse demands and improving service quality has become the main direction of shifting their strategy for manufacturing enterprises, and flexible manufacturing system (FMS) become an effective way to meet these needs. The multi-objective is to minimize the makespan and energy consumption of the machines and the number of AGVs; the multi-dimensional objective is to simultaneously optimize the sequence of operation of related jobs, the matching relation between transfer tasks and AGVs (AGV-task) and the matching relation between operations and machines (operation-machine) for the multi-objective. To solve this kind of problem, evolutionary algorithms (EAs) are the common methods [9,37,38,39].

Problem Description

Algorithm Design

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Initial Data

Experiment Results and Discussion

Conclusions and Future Work