Best-matching heuristics in collaborative e-Work
Abstract
Today’s information driven world provides individuals and organizations with endless opportunities and challenges; only those capable of leveraging the information from distributed processes, locations or agents and creating value from it will be able to survive in the emerging world economy. Specifically, collaborative e-Work is revolutionizing the capabilities of e-Business, e-Production, and e-Service; by building upon existing and well-established work, business and service models, theories, and solutions and merging them with the new electronic world, they are augmenting their abilities to meet customer’s needs and address the ever-changing landscape of manufacturing and services. The focus of this research is an investigation into the use of a Best-Matching methodology for streamlining, leveraging and matching information supply and demand to enable collaborative e-Work decision-making. The Best-Matching methodology developed provides the foundation for the creation of near-optimal and computational efficient heuristics and protocols to support e-Work activities. An assembly network is used as a case study to demonstrate the economic (cost and value) and performance benefits of Best-Matching of parts and its implications in the selection of suppliers. Five heuristics (Reverse, Forward, Fixed, Overall and Random) and an optimal algorithm were developed to address the five complexities (population size, number of agents, critical attributes, type of information, type of matching, time) that were identified as critical for Best-Matching. The results from the assembly case study show statistically significant quality improvements over commonly used assembly practices while also providing greater economic value and enabling the customer to make better supplier selection decisions. Finally, the information collected from the use of Best-Matching was leveraged to define Lines of Collaboration and Command that enable individuals and organizations to decide whom to collaborate with, under what circumstances and with what required information.
Degree
Ph.D.
Advisors
Nof, Purdue University.
Subject Area
Industrial engineering
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.