A MOO-EDS approach for quantifying energy consumption and CO2 emissions for manufacturing system design and evaluation

Qian Wang, Reda Nujoom, Ahmed Mohammed

    Research output: Contribution to journalArticlepeer-review

    111 Downloads (Pure)

    Abstract

    A sustainable manufacturing system design can be partially achieved by promoting an energy-saving production method in which energy consumption and amount of CO2 emissions can be measured and reduced as minimal as possible. Thus, there is a need for developing a computer-based discrete event simulation (DES) tool which enables incorporating parameters of energy consumption and CO2 emissions when it is used for manufacturing systems design and evaluation. Unfortunately, such a DES tool is unavailable in the existing market. This paper presents a hybrid Multi-Objective Optimization Enterprise Dynamic Simulation (MOO-EDS) approach that can be employed as an aid for manufacturing systems design and evaluation aiming to minimize energy consumption and amount of CO2 emissions at an early stage. A real case study was examined for validating the applicability of the proposed approach. The research outcome demonstrates that the hybrid FMOO-EDS approach can be an effective decision-making tool by quantifying energy consumption and amount of CO2 emissions towards a sustainable manufacturing system design.
    Original languageEnglish
    Pages (from-to)324-328
    Number of pages5
    JournalInternational Journal of Mechanical Engineering and Robotics Research
    Volume9
    Issue number3
    Publication statusPublished - 1 Mar 2020
    Event2nd International Conference on Industrial Engineering and Intelligent Manufacturing - Shanghai, China
    Duration: 14 Aug 201916 Aug 2019
    http://www.cieim.org/index.html

    Keywords

    • sustainable manufacturing systems
    • energy consumption
    • CO2
    • modelling and simulation
    • multi-objectives

    Fingerprint

    Dive into the research topics of 'A MOO-EDS approach for quantifying energy consumption and CO2 emissions for manufacturing system design and evaluation'. Together they form a unique fingerprint.

    Cite this