BL.Optim: an OptaPlanner based optimizer towards resolution of large-scale realistic scheduling and routing problems
Abstract
Nowadays, a diverse range of business sectors necessitates the development of optimal planning that aligns with various business-oriented objectives and constraints Consequently, generating scheduling and routing solutions for organizational staff has become exceedingly complex, especially with large-scale realistic problems. Optimization models play a vital role in assisting organizational decision-makers for addressing operational challenges and generating satisfactory solutions using powerful optimizers. This research work tackles the challenges associated with realistic Scheduling and Routing Problems (SRPs) with a multi-day horizon. To address these challenges, BL.Optim is introduced, which is an OptaPlanner-based optimizer with a model-based architecture. BL.Optim aims to capture routing and scheduling requirements for a wide category of Constraint Satisfaction Problems (CSP), with customizable requirements that can activate necessary constraints according to specific realistic use cases. A set of pre-formulated CSP-based soft constraints and non-violated hard constraints are embedded within BL.Optim. To assess the optimality of solutions generated by BL.Optim, two real-life use cases of Preventive Maintenance SRPs in Spain and France are presented, involving up to 481 activities to be performed by 18 staff members over 4 weeks. These use cases are tested in BL.Optim under different metaheuristic configurations (e.g., tabu search), and the solution scores are compared. Experimental results demonstrate high-quality optimized solutions free from hard constraint violations and with minimal penalties for soft constraints, achieved within a short computing time of just 8 minutes.
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