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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A sustainable manufacturing system design: A fuzzy multi-objective optimization model.

Reda Nujoom1, Ahmed Mohammed2,3, Qian Wang2

  • 1School of Engineering, University of Portsmouth, Portsmouth, UK. Reda.Nujoom@port.ac.uk.

Environmental Science and Pollution Research International
|August 12, 2017
PubMed
Summary

This study introduces a sustainable manufacturing system model that minimizes costs, energy use, and carbon dioxide (CO2) emissions. It integrates lean principles with environmental considerations for efficient production.

Keywords:
CO2Energy consumptionEnvironmental constraintsLean manufacturingMulti-objectiveSustainable manufacturing systems

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Area of Science:

  • Industrial Engineering
  • Environmental Science
  • Operations Research

Background:

  • Growing global concern for environmental protection drives regulations for energy saving and reduced carbon dioxide (CO2) emissions in manufacturing.
  • Sustainable manufacturing systems offer an effective solution to minimize environmental impact.
  • While lean manufacturing reduces waste and boosts efficiency, it often overlooks environmental factors like energy consumption and CO2 emissions.

Purpose of the Study:

  • To develop a sustainable manufacturing system design that accounts for energy consumption and CO2 emissions.
  • To create a multi-objective mathematical model minimizing total cost, energy consumption, and CO2 emissions.
  • To address real-world uncertainties using a fuzzy multi-objective approach for decision-making.

Main Methods:

  • Development of a multi-objective mathematical model incorporating economic and ecological constraints.
  • Integration of energy consumption and CO2 emission measurements using various energy sources (oil, solar).
  • Application of a fuzzy multi-objective model to handle input parameter uncertainty in a real-world scenario.

Main Results:

  • The developed model effectively minimizes total cost, energy consumption, and CO2 emissions.
  • The fuzzy multi-objective approach successfully handled uncertainties in input parameters.
  • The study provides a validated model for decision-making in manufacturing system design, including machine and resource allocation.

Conclusions:

  • The proposed sustainable manufacturing system model offers a robust framework for balancing economic and environmental objectives.
  • Integrating fuzzy logic enhances the model's applicability to real-world manufacturing challenges.
  • The research provides valuable insights for designing environmentally conscious and efficient manufacturing systems.