Learnheuristics in routing and scheduling problems: A review

Abstract

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.

A Bayesian Optimization Approach for Tuning a Grouping Genetic Algorithm for Solving Practically Oriented Pickup and Delivery Problems

“…Applying dynamic or adaptive parameter tuning techniques in comparison to static methods should also be considered (cf. [42,43]). Furthermore, the knowledge that similar instances (within a class) can be solved efficiently in the same manner is supposed to be included in an a priori parameter selection approach.…”

“…An overview of which meta-heuristic-related configuration problems can arise, and how ML techniques are applied to solve them for algorithms solving combinatorial optimization problems, can be found in [41]. A review of such learn-heuristics, especially for rout-ing problems, is given by [42], while, in [43], a literature review for adaptive parameter configuration for evolutionary algorithms is given.…”