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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization.

Song Huang1, Na Tian1, Yan Wang1

  • 1School of Internet of Things Engineering, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu Province 214122 China.

Springerplus
|September 22, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-objective particle swarm optimization with variable neighborhood search to solve the flexible job shop problem (FJSP). The method efficiently optimizes resource allocation and machine utilization in manufacturing systems.

Keywords:
Flexible job shop schedulingNon-dominated archive update strategyParticle swarm optimizationVariable neighborhood search

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Area of Science:

  • Operations Research
  • Manufacturing Systems Engineering
  • Computational Intelligence

Background:

  • The flexible job shop problem (FJSP) is a complex scheduling challenge in manufacturing.
  • Efficient resource allocation and machine utilization are critical for optimizing production.

Purpose of the Study:

  • To develop an efficient algorithm for solving the flexible job shop problem (FJSP).
  • To improve machine resource utilization and rational allocation within manufacturing systems.

Main Methods:

  • A multi-objective particle swarm optimization (MOPSO) integrated with variable neighborhood search (VNS) was developed.
  • Population initialization used assignment rules (AL) and dispatching rules (DR).
  • Special discrete operators, earliest completion machine (ECM), and archive update strategies were employed for optimization and local search.

Main Results:

  • The proposed MOPSO with VNS algorithm demonstrated effectiveness in addressing the FJSP.
  • Evaluation using Kacem and Brdata instances confirmed the algorithm's performance compared to other approaches.
  • The method successfully preserved non-dominated individuals and enhanced local search capabilities.

Conclusions:

  • The integrated MOPSO and VNS approach provides an effective solution for the flexible job shop problem.
  • This method enhances the rational utilization of machine resources in manufacturing.
  • The algorithm shows significant potential for improving scheduling efficiency in complex manufacturing environments.