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This special issue aims to attract the state of the arts of techniques used to solve optimization problem appearing in different areas of logistics and transportation systems. It aims to introduce TRE as one of the pioneering journals welcoming such contributions. The guest editors hope that upon success of this SI, it becomes the first of a series of SIs which will focus specifically on this topic.
Guest editors:
Dr. habil. Shahin Gelareh - Universite d’Artois, Bethune, France ( Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo. )
Dr. habil Nelson Maculan - Federal University of Rio de Janiero (Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo.)
Dr. Rahimeh Neamatian Monemi - Université de Lille, (Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo.)
Dr. Pedro Henrique González - Federal University of Rio de Janeiro ( Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo.)
Dr Xiaopeng Li - University of Wisconsin-Madison, Madison, WI, United States ( Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo. )
Dr. Fatmah Almazkoor - University of Kuwait ( Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo.)
Dr Ran Yan - School of Civil and Environmental Engineering, Nanyang Technological University ( Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo. )
Special issue information:
A significant body of literature focuses on methods of optimisation in mathematical programming, particularly for addressing logistics and transportation problems. In addition to the theoretical developments that have led to highly efficient techniques, researchers have always tried to exploit the inherent structure of data and problem instances in handcrafting techniques to tweak and improve the performance of their ad-hoc methods. Furthermore, recent advances in machine learning and deep learning had also led to some significant improvement in the performance of solution techniques that learn to solve optimisation problems to optimality or to some provably near-optimal solutions. With that, classification and regression methods come in support of the classical techniques in search algorithms, branching and cutting decisions, estimating the primal dual properties and even further in more advanced and complex methods. Deep learning approaches use various neural-based methods such as graph convolutional networks, attention mechanisms, and reinforcement learning to learn policies for finding optimal solutions. Many promising results have been reported on many problems, especially for combinatorial optimization problems, that are building blocks for more complex optimization problems in logistics and transportation systems Although there is still much work to be done to improve efficiency of such techniques, there are already highly sophisticated techniques available.
Topics of interest
This Special Issue invites authors to submit articles focusing on optimization methods that rely on learning techniques to address problems in logistics and transportation. Theoretical papers are acceptable, provided that they have case studies/numerical examples in the logistics/transportation field; models and algorithms that utilize learning to better understand the problem structure, physics, and behavior fall in the scope of the special session. We are particularly interested in contributions that are comprehensive enough to also cover or address problems in logistics and supply chains, that consider sustainability, IoT, electric vehicles, energy efficiency, and other relevant areas. We welcome both original research and review articles. Possible contributions may include, but are not limited to, the following topics:
§ Aperfeiçoamento dos métodos clássicos via ML
§ Processo de Decisão Markov
§ Métodos neurais
§ Aprendizagem para técnicas primordiais
§ Métodos baseados na aprendizagem por reforço,
§ Novas classes de métodos.
Submission process and papers must adhere to the standard author guidelines of Transportation Research Part E: Logistics and Transportation Review, which can be found at HERE.
Submitted articles must not have been previously published or currently submitted for journal publication elsewhere. Please follow the submission guidelines, which can be found from the journal website.
All submissions to the Special section should be submitted via the Transportation Research Part E online submission system. When you submit your paper to the Special section, please choose article type “MLOPT23” Otherwise, your submission will be handled as a regular manuscript. Papers submitted to the Special section will be subjected to normal thorough double-blind review process.
Keywords:
Logistics, transportation systems, machine learning, optimization, combinatorial optimization, solution methods
Learn more about the benefits of publishing in a special issue.
Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field HERE.
Deadline: 01/02/2024
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