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Submit a Manuscript to the Journal
International Journal of Management Science and Engineering Management

For a Special Issue on
Sustainable Logistics Systems using AI-based Meta-Heuristics Approaches

Manuscript deadline
30 September 2021

Cover image - International Journal of Management Science and Engineering Management

Special Issue Editor(s)

Gursel Suer, Dept. of Industrial & Sys. Eng., Ohio University, Athens, USA
[email protected]

John Runwei Cheng, Broad Geophysical Technology, Inc., Houston, USA
[email protected]

Fulya Altiparmak, Dept. of Industrial Eng., Gazi University, Ankara, Turkey
[email protected]

Antonio Grilo, Dept. of Mech. and Indus. Eng., Universidade NOVA de Lisboa, Portugal
[email protected]

Mitsuo Gen, Fuzzy Logic Systems Institute and Tokyo Univ. of Science, Tokyo, Japan
[email protected]

Submit an ArticleVisit JournalArticles

Sustainable Logistics Systems using AI-based Meta-Heuristics Approaches

Call for Papers

Special Issue in

Inter. J. of Management Science & Engineering Management

Sustainable Logistics Systems using AI-based Meta-Heuristics Approaches

 

Aim of the Special Issue:

Nowadays, most of multi-national enterprises faces the issues of sustainable development for their logistics systems in order to meet or exceed customer expectations. Sustainable development attracts both researchers and industrial practitioners who are focused on the design and implementation of logistics system. In general, economic, environment, and social issues should be considered for the sustainability of logistics system. A lot of studies have been focused on the design and implementation of sustainable logistics system. However, unfortunately, to the best of our knowledge, a few related studies have developed heuristics to find best solutions for sustainable logistics system via AI-based meta-heuristics approaches.

AI-based meta-heuristics approaches has emerged as a capable method for quickly providing optimal or near-optimal solutions for the problems that exact optimization cannot solve. Recent advances in various AI-based meta-heuristics approaches can resolve various and complex logistics and supply chain problem types. Specially, they can adapt and solve sustainable logistics system problems efficiently, since most of sustainable logistics system problems consists of multi-objective function types and most of conventional approaches cannot locate their optimal solutions exactly. Therefore, recent advances in various AI-based meta-heuristics approaches should be examined and their applications to sustainable logistics system problems should be also reviewed.

Since the last decade, CPU + GPU heterogeneous architecture has become a mainstream computing platform that accelerates applications. GPU acceleration makes the research of parallel meta-heuristics methods truly enter into the world of high-performance computing (HPC). Benefitting from the power of GPU-accelerated computing, it demonstrates a great potential to deal with a large scale of real-world problems and a huge amount of data for many research disciplines and industrial worlds.

 

Scope of the Special Issue:

This special issue mainly encompasses the most popular and frequently employed AI-based meta-heuristics approaches such as genetic algorithm, ant colony optimization, artificial bee colony, particle swarm optimization, simulated annealing, tabu search, variable neighborhood search, and the hybrid of these approaches. Various types of AI-based meta-heuristics approaches for designing and implementing sustainable logistics system can be considered as follows: i) individual or hybrid meta-heuristics for meta-heuristics type, ii) single or multi-objective mathematical models for objective type, iii) economic, environmental and (or) social objectives for objective function type of designing sustainable logistics system, and iv) the minimization of cost/time or the maximization of profit for the aim of objective functions. v) the parallel designs and implementation of meta-heuristics methods accelerated by using GPU computation. The scope of this special issue includes, but is not limited to, the following topics:

  • Sustainable Logistics Problems (Supply Logistics, Intra and Production Logistics, Distribution Logistics, Reverse Etc.)
  • Supply Chain-related Network
  • Intelligent Logistics Systems
  • Risk and Disruption Management in Supply Chain
  • Combinatorial Optimization in Logistics System
  • Environmental, Economic and or Social Issues in Sustainability
  • Closed-loop Supply Chain Networks
  • Underground Logistics Systems
  • Advanced Production/Distribution Systems in Logistics System
  • Other Related Topics in Sustainable Logistics System
  • Novel Meta-Heuristic Algorithms
  • Soft Computing
  • Computational Intelligence
  • Nature-inspired Computing
  • Evolutionary Algorithms
  • Swarm Intelligence
  • Machine Learning
  • Parallel meta-heuristics accelerated with GPU computing
  • Other Intelligent-related Algorithms

 

Submission Guidelines:

Manuscripts should be submitted through the publisher’s online system, at

https://rp.tandfonline.com/submission/create?journalCode=tmse

Please follow the instructions described in the “Instructions for Authors” given in the following website:

https://www.tandfonline.com/action/authorSubmission?show=instructions&journalCode=tmse20

Please make sure you select "special issue Sustainable Logistics Systems using AI-based Meta-Heuristics Approaches” when submitting your paper to ScholarOne. In preparing their manuscript, the authors are asked to closely follow the “Instructions to Authors”. Submissions will be reviewed according to IJMSEM’s rigorous standards and procedures through a double-blind peer review by at least two qualified reviewers.

 

Publication Schedule:

- Deadline for manuscript submission: Sep. 30th, 2021

- Review report: Nov. 30th, 2022

- Revised paper submission deadline: Dec. 31st, 2022

- Notification of final acceptance: Feb. 28th, 2022

- Final paper submission deadline: Mar. 31st, 2022

- Publication date: May 30th 2022 (tentative schedule)

 

Guest Editors:

Mitsuo Gen, Fuzzy Logic Systems Institute & Tokyo Univ. of Science, Tokyo, Japan,

[email protected],jp   Corresponding GE

Gursel Suer, Dept. of Industrial & Sys. Eng., Ohio University, Athens, USA,

[email protected]

John Runwei Cheng, Broad Geophysical Technology, Inc., Houston, USA

[email protected]

Fulya Altiparmak; Dept. of Industrial Eng., Gazi University, Ankara, Turkey

[email protected]

Antonio Grilo; Dept. of Mech. & Indus. Eng., Universidade NOVA de Lisboa, Portugal,

[email protected]

YoungSu Yun; Division of Business Administ., Chosun University, Gwangju, S. Korea,

[email protected]   Managing GE