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BL.Optim: a configurable optimizer towards decision-making support for various scheduling and routing problem

Abstract : Nowadays, there is a wide demand from many organizations to regularly route and schedule staff and goods to satisfy specific objectives. Scheduling and routing issues often require consideration of various businesses constraints under different industrial contexts, thereby ensuring the optimal use of resources. Decision-makers need customizable optimizers to specify business-oriented constraints and define their target planning generation needs. In our work, we present "BL.Optim", an optimizer with two-stage model-based architecture, which aims at capturing routing and scheduling requirement for a wide category of Constraint Satisfaction Problems (CSP). We demonstrated our approach on Home Health Care Routing and Scheduling Problems (HHCRSP), Home Meal Delivery Problems and Preventive Maintenance Scheduling and Routing Problems. The captured requirements are customizable by activating the necessary constraints facing the different realistic use cases. A series of CSP-based activable soft constraints and non-violated hard constraints are embedded in BL.Optim. To meet industrial requirements in solving the real-world cases with significant dimension, we opted for the Ant Colony Optimization algorithm for solution generation. Solutions generated by BL.Optim for HHCRSP are competitive against the manual scheduling result, with less 28% less assigned caregivers to perform required 142 services per day by respecting all the specified constraints.
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Contributor : Liwen ZHANG Connect in order to contact the contributor
Submitted on : Thursday, August 18, 2022 - 3:07:01 PM
Last modification on : Friday, September 16, 2022 - 10:41:27 AM


  • HAL Id : hal-03750397, version 1


Liwen Zhang, Florent Mouysset, Mustapha Derras, Christophe Bortolaso. BL.Optim: a configurable optimizer towards decision-making support for various scheduling and routing problem. EURO 2022 - 32nd European Conference on Operational Research, Jul 2022, Espoo, Finland. ⟨hal-03750397⟩



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