Abstract
This work addresses a problem that integrates the unrelated parallel machine scheduling and capacitated vehicle routing problems. In this integrated problem, a set of jobs must be processed on machines and then distributed using a fleet of vehicles to customers. The integrated problem’s objective is to determine the machines’ production scheduling and the vehicle routes that minimize the total weighted tardiness of the jobs. As the problem is NP-Hard, we propose four neighborhood search heuristics and a framework that uses machine learning to solve it. The framework aims to define the best neighborhood search heuristics to solve a given instance based on the problem characteristics. The proposed methods are evaluated and compared by computational experiments on a set of proposed instances. Results show that using a machine learning framework to solve the problem instances yields better performance than neighborhood search heuristics.
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This work was supported by CAPES and CNPq.
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de Freitas Araujo, M., Arroyo, J.E.C., Nogueira, T.H. (2023). Heuristics Assisted by Machine Learning for the Integrated Production Planning and Distribution Problem. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_13
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