A decision support methodology for integrated machining process and operation plans for sustainability and productivity assessment

Qais Y. Hatim, Christopher Saldana, Guodong Shao, Duck Bong Kim, K. C. Morris, Paul Witherell, Sudarsan Rachuri, Soundar Kumara

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a systematic methodology to enable environmental sustainability and productivity performance assessment for integrated process and operation plans at the machine cell level of a manufacturing system. This approach determines optimal process and operation plans from a range of possible alternatives that satisfy the objectives and constraints. The methodology provides a systematic procedure to highlight parameters that have significant impact on both sustainability and productivity performance metrics. We developed models and applied them to analyze manufacturing life cycle scenarios for collecting and categorizing key concepts towards building a material information model for sustainability. Integration of process and operation plans allows globalized assessment of sustainability and productivity, while development of a multi-criteria decision-making method leads to optimization of process planning activities based on the impact of conflicting sustainability and productivity metrics. A case study is detailed to demonstrate the sustainability-focused methodology, wherein integrated simulation and optimization techniques are used to support analysis of candidate scenarios and selection of preferred alternatives from a finite set of alternate process and operation plans. A discrete event simulation tool is used to model evolution of sustainability metrics (e.g., energy consumption) and productivity metrics (e.g., production time, cost) of a shop floor. The outcomes of this work include determination of optimized feature sequence plans which optimize various key performance indicators depending on stakeholder interest based on time, sustainability and production cost.

Original languageEnglish (US)
Pages (from-to)3207-3230
Number of pages24
JournalInternational Journal of Advanced Manufacturing Technology
Volume107
Issue number7-8
DOIs
StatePublished - Apr 1 2020

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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