A customized genetic algorithm for bi-objective routing in a dynamic network

•A bi-objective moving target travelling salesman problem is considered.•An integer linear programming model based on predicted trajectories is introduced.•A fast probabilistic dynamic programming based heuristic is proposed.•A hybrid customized genetic algorithm is developed and used in maritime lo...

Full description

Saved in:
Bibliographic Details
Published in:European journal of operational research Vol. 297; no. 2; pp. 615 - 629
Main Authors: Maskooki, Alaleh, Deb, Kalyanmoy, Kallio, Markku
Format: Journal Article
Language:English
Published: Elsevier B.V 01.03.2022
Subjects:
Dynamic network
Dynamic programming
Genetic algorithms
Moving-target traveling salesman problem
Moving-target traveling salesman problem
Dynamic network
Dynamic programming
Genetic algorithms
ISSN:0377-2217, 1872-6860
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A bi-objective moving target travelling salesman problem is considered.•An integer linear programming model based on predicted trajectories is introduced.•A fast probabilistic dynamic programming based heuristic is proposed.•A hybrid customized genetic algorithm is developed and used in maritime logistics.•The proposed method estimates Pareto frontiers much faster than a general solver. The article presents a proposed customized genetic algorithm (CGA) to find the Pareto frontier for a bi-objective integer linear programming (ILP) model of routing in a dynamic network, where the number of nodes and edge weights vary over time. Utilizing a hybrid method, the CGA combines a genetic algorithm with dynamic programming (DP); it is a fast alternative to an ILP solver for finding efficient solutions, particularly for large dimensions. A non-dominated sorting genetic algorithm (NSGA-II) is used as a base multi-objective evolutionary algorithm. Real data are used for target trajectories, from a case study of application of a surveillance boat to measure greenhouse-gas emissions of ships on the Baltic sea. The CGA’s performance is evaluated in comparison to ILP solutions in terms of accuracy and computation efficiency. Results over multiple runs indicate convergence to the efficient frontier, with a considerable computation speed-up relative to the ILP solver. The study stays as a model for hybridizing evolutionary optimization and DP methods together in solving complex real-world problems.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2021.05.018