Machine learning-based multi-objective optimization for dynamic scheduling and routing of heterogeneous instant delivery orders and scheduling strategies with real-time adaptation
DOI:
https://doi.org/10.35335/emod.v16i2.58Keywords:
Dynamic scheduling, Heterogeneous instant delivery, Multi-objective optimization, Real-time adaptation, RoutingAbstract
This research develops a machine learning-based multi-objective optimization technique for dynamic scheduling and routing heterogeneous instant delivery orders. Instant delivery service providers confront issues improving their operations due to order characteristics, time windows, vehicle capabilities, and real-time adaption. Scheduling, routing, and optimization literature for immediate delivery services is reviewed to start the investigation. Based on gaps, a new mathematical formulation is proposed to model the problem. Machine learning allows adaptive and dynamic decision-making. The formulation is used to address the optimization problem utilizing a method. Machine learning algorithms use past data to anticipate, optimize, and schedule routes. Real-time adaption solutions address changing order characteristics and operating situations. Numerical examples and case studies evaluate the proposed approach. The optimization approach solves difficult scheduling and routing problems in these cases. The research improves operational efficiency, cost savings, and order satisfaction. This research introduces a machine learning-based multi-objective optimization framework for rapid delivery order scheduling and routing. The findings help immediate delivery service providers streamline operations, boost customer happiness, and maximize resource use. To create more comprehensive optimization models, future research can integrate traffic circumstances, environmental implications, and customer preferences
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Copyright (c) 2022 Ramson Rikson Maruwahal Sijabat, Zhou Klapp Parodos
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