Logistics and Operations Modelling and Optimisation for Sustainable Supply Chain

Dear Colleagues,

Optimization plays an important role in several branches of engineering sciences. Optimization challenges are becoming increasingly complex, especially in supply chain planning; nowadays, traditional methods are not effective enough to solve complex optimization problems. New variants of meta-heuristic algorithms have recently emerged that primarily aim to combine innovative methods to better search for solutions. Many such algorithms have been inspired by nature; the most significant include the Keshtel Algorithm (KA), Red Deer Algorithm (RDA), and Gray Wolf Optimizer (GWO).

Various optimization problems have also been proposed to find suitable, eligible, optimal solutions for sustainability. Furthermore, researchers and decision-makers have developed and widely employed meta-heuristic algorithms for various complex problems. In fact, it can be argued that in the future, almost all aspects of human daily life will be affected by this technology. Experts believe that meta-heuristic algorithms will soon be able to find the best possible solution for supply chains.

This Special Issue, “Logistics and Operations Modelling and Optimization for Sustainable Supply Chain”, enables transparent and rapid communication of research highlighting the role of optimization in multidisciplinary areas across mathematical programming and optimization. The emphasis is on the impact, depth, and originality of new concepts, methods, and observations with respect to novel methods such as meta-heuristic algorithms. This Special Issue seeks to clarify how optimization approaches can be evaluated and implemented in sustainable supply chains.

We welcome submissions highlighting new solutions to sustainable supply chain problems from a broad range of perspectives, which may include, but are not limited to, the following:

• Sustainable supply chain network design using approximate optimization methods;
• Sustainable Production and distribution using novel meta-heuristic algorithms;
• Transportation optimization using novel meta-heuristic algorithms;
• Supply chain planning using new emerged meta-heuristic algorithms;
• Simulation-based optimization for sustainable supply chain planning;
• Application of meta-heuristics in developing machine learning for sustainable supply chain planning;
• Application of meta-heuristics in improving the neural networks for sustainable supply chain planning;
• Application of meta-heuristics in developing clustering methods for sustainable supply chain planning;
• Logistic network design using data-driven

Prof. Dr. Gerhard-Wilhelm Weber
Dr. Alireza Goli
Dr. Erfan Babaee Tirkolaee
Guest Editors

Manuscript Submission Information

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