A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

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PeerJ Computer Science

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Introduction

State of the Art of FJSP

Problem Formulation

  1. An operation cannot be interrupted while a machine is processing it.

  2. One machine can process one operation at most.

  3. Once the order of operations has been determined, it cannot be modified.

  4. Breakdowns in machines are not considered.

  5. The works are independent of one another.

  6. The machines are independent of one another.

  7. The time used for the machines’ preparation and the transfer of operations between them is negligible.

Global-local Neighborhood Search Algorithm for the FJSP

General description of the GLNSA

Encoding and decoding of solutions

Selection method

Neighborhood structure

Exploration neighborhood

Insertion

Swapping

Path relinking

Mutation of the string of machines

Local search neighborhood

Critical path

Simplified Nopt1 neighborhood

Parameters of the GLNSA

  • Number of iterations for the whole optimization process: Gn.

  • Number of smart-cells: Sn.

  • Global neighborhood size: l.

  • Probabilities αI, αS, and αP for insertion, swapping, and PR, respectively, and probability αM for mutation.

  • Maximum number of stagnation iterations: Sb.

  • Proportion of elitist solutions: Ep.

  • Number of tabu iterations for every optimization iteration: Tn.

  • Tabu threshold: Tu.

Parameter tuning

Experimental Results

Comparative analysis of computational complexity between algorithms

First experiment, Kacem instances

Second experiment, Brandimarte instances

Third experiment, Hurink instances with low flexibility

Fourth experiment, Hurink instances with greater flexibility

Conclusions and Further Work

Supplemental Information

GLNSA Matlab code.

Global-Local Neighborhood Search Algorithm (GLNSA) to solve the Flexible Job Shop Scheduling Problem. Developed in MATLAB R2015a(7.08)

DOI: 10.7717/peerj-cs.574/supp-1

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Nayeli Jazmin Escamilla Serna performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, and approved the final draft.

Juan Carlos Seck-Tuoh-Mora conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

Joselito Medina-Marin analyzed the data, performed the computation work, authored or reviewed drafts of the paper, and approved the final draft.

Norberto Hernandez-Romero conceived and designed the experiments, performed the experiments, prepared figures and/or tables, and approved the final draft.

Irving Barragan-Vite conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

Jose Ramon Corona Armenta analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The code is available in the Supplemental File, and the code and test problem are also available at GitHub: https://github.com/juanseck/GLNSA-FJSP-2020.

Funding

This study was supported by the National Council for Science and Technology (CONACYT) with project number CB- 2017-2018-A1-S-43008, and Nayeli J. Escamilla Serna was supported by CONACYT with grant number 1013175. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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