SUBJECT: Ph.D. Proposal Presentation
BY: Manik Rajora
TIME: Tuesday, October 4, 2016, 10:00 a.m.
PLACE: MARC Building, 201
TITLE: Intelligent Manufacturing for Production and Process Planning based upon Hierarchically Coupled Constraint Optimization and Multiple Solution Methodology
COMMITTEE: Dr. Steven Y. Liang, Chair (ME)
Dr. Shreyes Melkote (ME)
Dr. Roger Jiao (ME)
Dr. Nagi Gebraeel (ISYE)
Dr. Wei Xu (Industry)


In manufacturing industries, hierarchically coupled constraint problems (HCCPs), such as Assembly Job Shop Scheduling Problems (AJSSP), multi-stage production optimization etc., occur commonly but there is no clear definition to identify these problems. Numerous techniques have been developed to solve different problems that can be classified as HCCPs. However, these techniques are not universally applicable to all HCCPs and are unable to cope with large scale problems. To develop a universal method to solve HCCPs, it is of utmost importance to establish an abstract definition and identify the common principles amongst different HCCPs. In this part of the research the said definition and the common principles will first be established. Based on the established definition and common principles, a new optimization technique based on evolutionary computation will be developed to solve HCCPs. The developed optimization algorithm will be applied to solve a variety of HCCPs to validate its performance. The expected outcome of the first part of the research will be a definition to identify HCCPs and a new algorithm to efficiently solve HCCPs with its performance verified through a variety of applications.

It can be observed from experimental data that different process parameter combinations can lead to the same performance indicators. It is important to obtain multiple solutions as a single solution may theoretically satisfy the objective function but might not be applicable in real life scenarios as it may lead to undesired experimental or production conditions. However, current optimization techniques have the inability to map to multiple process parameter combinations with identical performance indicator. To solve this problem, a new approach will be developed based on segmented yet continuous mapping to obtain multiple solutions with identical objective value. To measure the unbiased and generic performance of this technique, an abstract definition will also be established. The developed approach will be applied to obtain multiple solutions in case studies related to process parameter optimization of electrochemical-micromachining (EMM) and electrochemical machining (ECM) of saw-tooth profile. The expected outcome of this research will be an algorithm that is able to efficiently solve the multiple solution mapping problem with its performance verified through the process parameter optimization of different processes.