Learnheuristics in routing and scheduling problems: A review
Keywords:Metaheuristics, Machine learning, Routing problems, Scheduling problems, Learnheuristics, Combinatorial optimization problems, Metaheuristics - Machine learning cooperation
Combinatorial optimization problems (COPs) are the most important class of optimization problems, with great practical significance. This class is concerned with identifying the best solution from a discrete set of all available options. The transportation (routing) and distribution (scheduling) systems are considered the most challenging optimization examples of the COPs. Given the importance of routing and scheduling problems, many methods have been proposed to address them. These methods can be categorized into traditional (exact and metaheuristics (MHs) methods) and machine learning (ML) methods. ML methods have been proposed to overcome the problems that traditional methods suffer from, especially high computational time and dependence on the knowledge of experts. Recently, ML methods and MHs have been combined to tackle the COPs, and then the learnheuristics term emerged. This combination aims to guide the MHs toward an efficient, effective, and robust search and improve their performance in terms of solution quality. This work reviews the publications in which the collaboration between MHs and ML has been utilized to propose a guideline for the researchers to put forward new algorithms that have a good ability to solve routing and scheduling problems.
How to Cite
Copyright (c) 2023 Samarra Journal of Pure and Applied Science
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the SJPAS journal right of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in Samarra Journal of Pure and Applied Science.
The Samarra Journal of Pure and Applied Science permits and encourages authors to archive Pre-print and Post-print items submitted to the journal on personal websites or institutional repositories per the author's choice while providing bibliographic details that credit their submission, and publication in this journal. This includes the archiving of a submitted version, an accepted version, or a published version without any Risks.