Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena® was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest® optimization tool.

In this paper, the authors proffer a novel mathematical model for the simultaneous optimization of facility location and network design in the presence of uncertainty, with the aim of minimizing operational and transportation costs. The proposed model constitutes a departure from conventional methods in its consideration of probable events in the real world and the incorporation of uncertainty assumptions into the mathematical framework. An algorithm based on simulated annealing is then advanced for the solution of the problem, and the performance of the algorithm is evaluated through comparison with exact methods for problems of modest size, as well as with a basic simulated annealing algorithm for larger problems. The results of these comparisons demonstrate the superiority of the proposed meta-heuristic algorithm. Finally, the robust approach is compared with four other approaches in the presence of uncertainty, with a thorough analysis of the results obtained from each of the methods conducted in a suite of sample problems.

Facility Layout Planning (FLP) is one of the most important strategic decisions that have a very big effect on the operational efficiency and therefore also the productivity and cost-effectiveness of manufacturing and service systems. The uncertainty which is inherent to the parameters like product demand, material flow, and processing times makes the FLP problem very complicated and dynamic. This paper exhibits a detailed review of the works done so far on FLP under uncertainty which has been based on an analysis of 163 articles from the Scopus database. The review organizes and evaluates systematically the main methodologies used, among them mathematical programming, heuristics and metaheuristics, simulation, and Multi-Criteria Decision-Making (MCDM) methods. The different types of uncertainty—fuzzy, stochastic, robust, and fuzzy-random—being modeled and integrated into solution approaches is one of the main areas of focus. It turned out from the analysis that hybrid metaheuristics, especially when combined with fuzzy MCDM techniques, are the most common and effective means of dealing with fluoroscope FLP issues today. Moreover, the paper contains a bibliometric analysis that underlines the main contributors, geographical patterns, and the trend of shifting research emphasis toward topics like construction site layout, sustainable logistics, and digital twin-supported planning. The review not only presents the gaps in the current literature but also points to further research implications including more integrated use of AI and dealing with the problem of real-time dynamic layout reconfiguration.