Heuristic Optimization Algorithms for Logistics

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 1033

Special Issue Editors


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Guest Editor
Department of Computer and Systems Engineering, Instituto Universitario de Desarrollo Regional, University of La Laguna, 38200 La Laguna, Spain
Interests: artificial intelligence; heuristics; optimization; logisitics

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Guest Editor
Department of Economics and Business, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain
Interests: optimization; metaheuristics; logistics & transportation; machine learning; home health care
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Special Issue Information

Dear Colleagues,

Heuristics, problem-solving techniques, can be used to address the complexity and uncertainty inherent in logistics operations. Heuristic optimization algorithms have gained significant attention due to their ability to efficiently find near-optimal solutions for complex logistics problems. Logistics, encompassing the management of the flow of goods, services, and information, requires robust optimization techniques to enhance efficiency, reduce costs, and improve overall performance. Heuristics offer practical and computationally efficient solutions for tackling intricate logistics challenges. These algorithms leverage simple rules and iterative processes to explore solution spaces effectively, making them well suited for real-world logistics applications.

This Special Issue aims to bring together cutting-edge research and innovative applications of heuristic optimization algorithms in logistics. Topics of interest include algorithmic developments, case studies, and experimental validations that demonstrate the effectiveness of heuristics in optimizing various aspects of logistics, including load packing, route planning, vehicle scheduling, inventory management, and facility location. This includes the decisions that have to be taken in the design and management of an efficient supply chain, from a strategic (long-term), tactical (medium-term), and operational (short-term) point of view, implying a combination of two or more of the above class of problems. The interdisciplinary nature of this Special Issue encourages contributions from researchers and practitioners in computer science, operations research, and logistics, fostering a collaborative environment for advancing the state of the art in heuristic optimization for logistics. Overall, this Special Issue seeks to contribute to the development of practical and scalable solutions that address the evolving challenges in the dynamic and complex field of logistics.

Prof. Dr. José Andrés Moreno Pérez
Dr. Jesica de Armas
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • heuristics
  • metaheuristics
  • combinatorial optimization
  • optimization algorithms
  • logistics
  • transport
  • supply chain management

Published Papers (1 paper)

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19 pages, 729 KiB  
Article
A Sim-Learnheuristic for the Team Orienteering Problem: Applications to Unmanned Aerial Vehicles
by Mohammad Peyman, Xabier A. Martin, Javier Panadero and Angel A. Juan
Algorithms 2024, 17(5), 200; https://doi.org/10.3390/a17050200 - 8 May 2024
Viewed by 592
Abstract
In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and [...] Read more.
In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic versions of the TOP, our approach considers a hybrid scenario, which combines deterministic, stochastic, and dynamic characteristics. The TOP involves visiting a set of customers using a team of vehicles to maximize the total collected reward. However, this hybrid version becomes notably complex due to the presence of uncertain travel times with dynamically changing factors. Some travel times are stochastic, while others are subject to dynamic factors such as weather conditions and traffic congestion. Our novel approach combines a savings-based heuristic algorithm, Monte Carlo simulations, and a multiple regression model. This integration incorporates the stochastic and dynamic nature of travel times, considering various dynamic conditions, and generates high-quality solutions in short computational times for the presented problem. Full article
(This article belongs to the Special Issue Heuristic Optimization Algorithms for Logistics)
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