Lean Modular Integrated Construction Production Phase Planning under Uncertainties: A Big Data–Driven Optimization Approach

Abstract

Phase planning is one of the most important components of lean-based production planning that provides basic guidelines for the entire production process. In modular integrated construction (MiC) projects, the complicated and drawn-out features of the manufacturing or production process pose difficulties for informed phase planning decisions under uncertainties. However, owing to the nascent nature of MiC, traditional approaches have little prior knowledge of the uncertainties. This research aimed to address this problem by proposing a data-driven optimization method based on a set of valuable historical production data to hedge against uncertainties during production. A real-life case study was then conducted to validate this optimization approach. The planning solution, including the (1) critical production path, (2) detailed production schedules, and (3) simulated production process, balances production schedules and uncertainties to ensure feasible and robust phase planning. This optimization method can make full use of historical production data rather than approximations of the probability distributions to handle uncertainties in the phase planning process. This research provides an innovative and robust solution for MiC production managers to efficiently conduct phase planning under uncertainties. It enriches the literature on phase planning and contributes to lean MiC manufacturing. The biggest novelty of this research is to open up a window for researchers and practitioners to look into MiC production in factories, which are traditionally like a “black box” unknown to us.