Logistics and Operations Modelling and Optimization for Sustainable Supply Chain

Optimization plays an important role in several branches of the 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 [1,2]. New variants of metaheuristic algorithms have recently emerged that primarily aim to combine innovative methods to better search for solutions [3,4]. 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 [5,6,7]. Furthermore, researchers and decision makers have developed and widely employed metaheuristic algorithms for various complex problems. In fact, in the future, almost all aspects of human daily life are expected to be affected by this technology. Experts believe that metaheuristic algorithms will soon be able to find the best possible solution for supply chains.

To address the abovementioned requirements, this Special Issue (SI), entitled “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 in mathematical programming. The emphasis is on the impact, depth, and originality of new concepts, methods, and observations with respect to novel methods such as metaheuristic algorithms. This SI seeks to clarify how optimization approaches can be evaluated and implemented in sustainable supply chains. Accordingly, we received 19 manuscripts, where most of the submissions fell into the aforementioned research topics of this SI. Seven manuscripts (including six research studies and one review study) were accepted for publication in Sustainability after providing the requested revisions by the authors, for which the average acceptance rate was approximately 37%.

The published manuscripts thoroughly examined the application of optimization and decision analysis techniques to highlight current progress on novel models and solution algorithms which can contribute to a better understanding of the performance of logistics systems and sustainable supply chain management and provide useful practical insights. The details of contributions are as follows:

• A two-echelon reverse supply chain system involving a remanufacturer and a collector was studied by Salami et al., wherein the collector receives the used products by paying a reward to consumers. They conducted a numerical analysis to ensure that under a contract, the risk of uncertainty is divided among the members.
• Babaei et al. developed an algorithm based on an extended Data Envelopment Analysis (DEA) model to assess omnichannel distribution network configurations. They took into account cost, service, transparency, and environmental criteria and utilized an uncertain optimization model to investigate a case study in the Indian retail industry.
• A bibliometric systematic review was performed by Zhou and Liu in the field of blockchain-enabled cross-border e-commerce supply chain management. They collected all relevant publications from the Web of Science database from 2013 to 2021 to conduct a network and co-word study by visualizing collaborative relationships with the help of VosViewer (version 2009). They concluded that researchers and industrial managers can promote innovative management practices in cross-border e-commerce supply chains by adopting blockchain.
• Apparel supply chain management based on reorder decision making was addressed by Baghbadorani et al. using a unified modelling language to delineate the relationship between agents and simulate the supply chain processes. They also applied an enhanced African vulture optimizer, a modified bioinspired approach, and fuzzy inference theory to assist the supply chain agent in the decision-making process.
• Noruzi et al. proposed a robust bi-objective mixed-integer linear programming (MILP) model to concurrently minimize the total cost and environmental impact under an uncertain environment for a multiperiod railway network design problem. The Nondominated Sorting Genetic Algorithm II (NSGA-II) was employed to treat the problem complexity as well as the model bi-objectiveness.
• A secondary supply chain consisting of a cross-border e-commerce enterprise and a third-party logistics (3PL) enterprise was considered by Ji et al. in order to establish a Stackelberg game model. Given the stochastic fluctuation of exchange rate and demand, a combined decision model was proposed to incorporate the logistics-service level and financial service pledge rate of the 3PL enterprise. It was demonstrated that the optimal logistics-service level and pledge rate grow with an increase in import tariffs and logistics sensitivity coefficients in offshore markets.
• Daneshvar et al. formulated a distribution network problem of agricultural products with high perishability under uncertainty with the help of a robust possibilistic optimization model. A Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Arithmetic Optimization Algorithm (AOA) were implemented to tackle the problem complexity and then assessed through a statistical analysis.