This study introduces the logic-based discrete-Benders decomposition (LD-BD) for Generalized Disjunctive Programming (GDP) superstructure problems with ordered Boolean variables. The key idea is to obtain Benders cuts that use neighborhood information of a reformulated version of Boolean variables. These Benders cuts are iteratively refined, which guarantees convergence to a local optimum. A mathematical case study, the optimization of a network with Continuous Stirred-Tank Reactors (CSTRs) in series, and a large-scale problem involving the design of a distillation column are considered to demonstrate the features of LD-BD. The results from these case studies have shown that the LD-BD method exhibited good performance by finding attractive locally optimal solutions relative to existing logic-based solvers for GDP problems. Based on these tests, the LD-BD method is a promising strategy to solve optimal synthesis problems with ordered discrete decisions emerging in chemical engineering applications.