Integrating Sustainability in Manufacturing Process Planning

Open Access
Hatim, Qais Yahya
Graduate Program:
Industrial Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
May 06, 2015
Committee Members:
  • Christopher Saldana, Dissertation Advisor
  • Soundar Rajan Tirupatikumara, Committee Chair
  • Lyle Norman Long, Committee Member
  • David R Riley Ii, Committee Member
  • Robert Carl Voigt, Committee Member
  • Sudarsan Rachuri, Special Member
  • Sustainability
  • Process Planning
  • Simulation
  • Optimization
  • Methodology
More recently, interest in designing products and manufacturing processes with major consideration given to the resources used and waste produced over the entirety of product/process life cycle, viz. sustainable manufacturing has increased. Unlike design and manufacturing process development activities that generally have access to a wealth of material information, sustainability assessment activities are generally made difficult by lack of a centralized source of information to incorporate sustainability knowledge into current manufacturing life cycle strategies. Even though considerable research has been accomplished in sustainable manufacturing domain, its application to real life problems is known to be in the early stages due to complexity in information representation, model compositions, system integrations, and computation. Moreover, isolated activities of process planning within the gate-to-gate life cycle can lead to localized solutions in sustainability assessment. Integrating operation plans and process plans provides globalized sustainable and productive solutions in the manufacturing gate-to-gate life cycle. This thesis, first, presents an attempt to understand these complexities for building material information model by addressing the requirements for defining a high-level material information model for sustainability that can capture this information across different life cycle stages as well as primary stakeholders. Second, the performance of job shop manufacturing is often related to diversified activities that impact sustainability and productivity. Process and operation plans are particularly considered as the main activities that significantly impact different key performance indicators. The research proposed a systematic methodology for supporting manufacturing decision-making regarding sustainability and productivity assessment by integrating these plans. In the first stage the different ways in which materials and material information influence the decision-making process were analyzed. For this purpose information modeling techniques were employed to generate manufacturing scenarios. Activity models were generated and analyzed to collect and categorize key concepts towards constructing a Materials Information Model for Sustainability. The analysis helped in identifying locations where materials factor into the decision-making process, the key information requirements that help build a material information model for sustainability. In the second stage of this research, a systematic methodology was developed for enabling the sustainability and productivity performance assessment for integrated process and operation plans at the machine cell level of manufacturing systems. Selection of processes and operations is accomplished through building a Multi-Criteria-Decision-Making formulation. This formulation enables the combined assessment of sustainability and productivity in selecting the optimum process and operation plans. Analytical Hierarchical Process (AHP) is employed to address the problem of conflicting key performance indicators during process planning. Discrete event simulation (ArenaTM) and optimization techniques (heuristic search algorithms in OptQuest) are combined to determine the set of inputs out of a number of possible planning scenarios and their interactions that optimize system sustainability and productivity performances. The possibility of applications of the approach to real-world production is demonstrated through a case study that uses the proposed methodology to analyze and understand manufacturing floor-level scenarios.