Scheduling optimization of hull block assembly line using constraint programming and discrete-event simulation
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Scheduling of a block assembly line in a shipyard is commonly known as the Permutation Flow-shop Scheduling Problem (PFSP) in Operation Research (OR), which has been extensively studied in various papers since the 1950s. However, existing solutions often involve simplifying real-world problems with certain assumptions, limiting their practical applicability. In recent times, Constraint Programming (CP) has emerged as a strong alternative to exact algorithms and has been successfully applied to various PFSP problems, addressing the limitations of exact algorithms. In light of this, our study proposes a two-step optimization process to overcome these limitations. First, a new PFSP problem, Multi-Objective PFSP with hard due date constraint (MOPFSP-hd) is introduced. The problem is solved with CP algorithm. Next, the feasibility and objective value of the optimized solution is validated using Discrete-Event Simulation (DES). Two industrial cases are conducted to evaluate the performance of our proposed framework. The experimental results from both cases demonstrated a significant improvement in makespan compared to manually planned schedule. Additionally, the solutions derived from our proposed model are reported to be feasible, while the manually planned schedules are often infeasible by not satisfying all the constraints or encountering delays. Finally, the difference between the objectives calculated from CP and DES model is analyzed quantitatively using Critical Path Method (CPM).
Abstract
Scheduling of a block assembly line in a shipyard is commonly known as the Permutation Flow-shop Scheduling Problem (PFSP) in Operation Research (OR), which has been extensively studied in various papers since the 1950s. However, existing solutions often involve simplifying real-world problems with certain assumptions, limiting their practical applicability. In recent times, Constraint Programming (CP) has emerged as a strong alternative to exact algorithms and has been successfully applied to various PFSP problems, addressing the limitations of exact algorithms. In light of this, our study proposes a two-step optimization process to overcome these limitations. First, a new PFSP problem, Multi-Objective PFSP with hard due date constraint (MOPFSP-hd) is introduced. The problem is solved with CP algorithm. Next, the feasibility and objective value of the optimized solution is validated using Discrete-Event Simulation (DES). Two industrial cases are conducted to evaluate the performance of our proposed framework. The experimental results from both cases demonstrated a significant improvement in makespan compared to manually planned schedule. Additionally, the solutions derived from our proposed model are reported to be feasible, while the manually planned schedules are often infeasible by not satisfying all the constraints or encountering delays. Finally, the difference between the objectives calculated from CP and DES model is analyzed quantitatively using Critical Path Method (CPM).