This study develops and evaluates an integrated framework for modular construction and Just-in-Time (JIT) logistics under narrow street and constrained site conditions in urban regeneration areas.
A simulation-based optimization approach is proposed by integrating discrete event simulation (DES) (SimPy), genetic algorithm optimization and Monte Carlo analysis. Three construction strategies, namely conventional construction, modular bulk delivery and modular JIT delivery, are comparatively analyzed under stochastic uncertainties.
Results show that modular construction significantly reduces project duration, cost, and urban externalities compared to conventional construction. Modular bulk delivery consistently outperforms JIT delivery on duration, cost, street closure and noise exposure; JIT’s sole measurable advantage is the elimination of on-site storage requirements. Accordingly, JIT should be regarded as a constraint-driven fallback strategy, appropriate when staging space is unavailable, rather than a performance-superior alternative to bulk delivery. Under the JIT configuration, optimal parameters are identified as a 10-hour buffer time and a 6-module (minimum feasible) storage capacity, though this configuration still yields an 18.75% late-delivery rate, reflecting an inherent reliability trade-off in space-constrained JIT operations.
Rather than proposing new simulation or optimization techniques, the study’s contribution lies in applying and calibrating established methods, namely DES, genetic algorithms and Monte Carlo analysis, to the previously under-examined problem of JIT logistics parameter tuning under severe narrow-street spatial constraints, and in providing empirical evidence that JIT delivery functions as a constraint-driven fallback strategy rather than a generally preferable alternative to bulk delivery.
