A Decision Support Methodology for Integrated Machining Process and Operation Plans for Sustainability and Productivity Assessment
Qais Hatim, Christopher Saldana, Guodong Shao, Duckbong Kim, KC Morris, Paul Witherell, Sudarsan Rachuri, Soundar Kumara
In this paper, a systematic methodology is presented 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. Models are developed and used to analyze manufacturing life cycle scenarios for collecting and categorizing key concepts towards building a material information model for sustainability. With the integration of process and operation plans, a globalized assessment of sustainability and productivity is achieved and a multi-criteria decision-making method is developed to optimize 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.
International Journal of Advanced Manufacturing Technology
, Saldana, C.
, Shao, G.
, Kim, D.
, Morris, K.
, Witherell, P.
, Rachuri, S.
and Kumara, S.
A Decision Support Methodology for Integrated Machining Process and Operation Plans for Sustainability and Productivity Assessment, International Journal of Advanced Manufacturing Technology
(Accessed December 1, 2023)