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Achieving sustainability in manufacturing systems: A quantitative study

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper presents a multi-objective optimisation (MOO) mathematical model that can be employed as an aid for manufacturing systems design and evaluation aiming to minimize energy consumption, amount of carbon dioxide (CO2) emissions and the total cost at an early stage. This approach associated with the number of machines, number of air-conditioning units and number of light bulbs involved in each process in conjunction with a quantity of material flow for processing the products in a manufacturing system. A real case study was examined for validating the applicability of the proposed approach. The research outcome demonstrates that this MOO model can be an effective decision-making approach for seeking a trade-off decision a mong multiple of decision-making objectives such as energy consumption, amount of CO2 emissions and total cost towards a sustainable manufacturing system (SMS) design at an early stage before actual construction or use of the system.

Original languageEnglish
Pages (from-to)604-615
Number of pages12
JournalProceedings of the International Conference on Industrial Engineering and Operations Management
Issue numberNovember
Publication statusPublished - 2019
Event1st GCC International Conference on Industrial Engineering and Operations Management, IEOM 2019 - riyadh, Saudi Arabia
Duration: 26 Nov 2019 → 28 Nov 2019

Bibliographical note

Publisher Copyright:
© IEOM Society International.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • CO
  • Energy consumption
  • Environmental constraints
  • Lean manufacturing
  • Sustainable manufacturing systems

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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