Abstract

The flexible job shop scheduling problem (FJSSP) and multi-row workshop layout problem (MRWLP) are two major focuses in sustainable manufacturing processes. There is a close interaction between them since the FJSSP provides the material handling information to guide the optimization of the MRWLP, and the layout scheme affects the effect of the scheduling scheme by the transportation time of jobs. However, in traditional methods, they are regarded as separate tasks performed sequentially, which ignores the interaction. Therefore, developing effective methods to deal with the multi-objective energy-aware integration of the FJSSP and MRWLP (MEIFM) problem in a sustainable manufacturing system is becoming more and more important. Based on the interaction between FJSSP and MRWLP, the MEIFM problem can be formulated as a multi-objective bi-level programming (MOBLP) model. The upper-level model for FJSSP is employed to minimize the makespan and total energy consumption, while the lower-level model for MRWLP is used to minimize the material handling quantity. Because the MEIFM problem is denoted as a mixed integer non-liner programming model, it is difficult to solve it using traditional methods. Thus, this paper proposes an improved multi-objective hierarchical genetic algorithm (IMHGA) to solve this model. Finally, the effectiveness of the method is verified through comparative experiments.

Highlights

  • With the diversification and personalization of market demand, multi-variety and small batch production has been adopted by more and more enterprises, aiming to respond to the changes of customers and provide the market with creative products quickly

  • In the proposed MEIFM problem, different from the previous literature [11,13,19], this paper considers flexible processing routes of jobs and unequal-area machines concurrently, which embodies a strong interaction between flexible job shop scheduling problem (FJSSP) and multi-row workshop layout problem (MRWLP)

  • FJSSP and MRWLP are traditionally addressed as two separate decisions, without considering the interaction between them

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Summary

Introduction

With the diversification and personalization of market demand, multi-variety and small batch production has been adopted by more and more enterprises, aiming to respond to the changes of customers and provide the market with creative products quickly. Suemitsu et al [19] proposed a multi-robots cellular manufacturing system layout problem that can determine the positions of manufacturing components and task scheduling simultaneously, and they applied MOGA to solve the problem for minimizing the operation time, layout area, and manipulability These studies validate that the ISLP can achieve better solutions than the independent optimization method, they still have several disadvantages, as follows:. As both scheduling and layout planning are NP-hard problems [20,21], the MEIFM problem considering the flexible processing route of jobs and unequal-area machines becomes a more complicated problem It is concerned with balancing the production efficiency and energy consumption of the overall manufacturing system, which makes the problem become more complex.

Literature Review on Solution Strategies
Problem Description
Model Formulation
Multi-Objective Bi-Level Programming Model
Algorithm Construction
Encoding and Initialization Population
Selection Operator
Multi-Parent Crossover Operator
Mutation Evaluation
Encoding and Decoding
Fitness Evaluation
Tournament Selection Operator with Parent-Offspring Competition Strategy
Computation Experiments
Description of Test Data and Parameter Setting
Experimental Analyses
Algorithm Comparison
Findings
Conclusions and Future Work
Full Text
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