Affiliations: [a] Department of Civil & Environmental Engineering, University of Alberta, 3-133 Markin/CNRL Natural Resources Engineering Facility, Edmonton, Alberta, Canada | [b] Department of Civil & Environmental Engineering, University of Alberta, 3-013 Markin/CNRL Natural Resources Engineering Facility, Edmonton, Alberta, Canada
Correspondence:
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Corresponding author: Prof. A.R. Fayek, Hole School of Construction Engineering, Department of Civil & Environmental Engineering University of Alberta, 3-013 Markin/CNRL Natural Resources Engineering Facility, Edmonton, Alberta, Canada T6G 2W2. E-mail: aminah.robinson@ualberta.ca.
Abstract: The percentage of shop fabrication, including pipe spool fabrication, has been increasing on industrial construction projects in the past years. Industrial fabrication has a great impact on construction projects due to the fact that the productivity is higher in a controlled environment than in the field, and therefore time and cost of construction projects are reduced by making use of industrial fabrication. Effective planning and scheduling of industrial fabrication processes is important for the success of construction projects. Dispatching rules are among the common methods for optimizing and improving the performance of complicated systems such as industrial fabrication shops. However, the performance of dispatching rules varies under different operating conditions and scenarios, such as changes in processing times of jobs (e.g., spools, modules), and changes in the system's configurations (e.g., number of stations and resources on the shop floor in pipe spool fabrication). This paper focuses on developing a new framework for optimizing shop scheduling, particularly pipe spool fabrication shop scheduling, which uses the Pareto-optimality concept combined with fuzzy set theory for multi-criteria (i.e., multi-objective) optimization. The proposed framework makes it possible to capture uncertainty of a pipe spool fabrication shop while accounting for linguistic vagueness of the decision makers' preferences, using simulation modeling and fuzzy set theory. The implementation of the proposed framework is discussed using a real case study of a pipe spool fabrication shop.
