The design of responsive and green networks necessarily entails the optimisation of multiple conflicting objectives with strategic, tactical, or operational decisions. This paper addresses a bi-objective green p-hub centre routing problem with hub location-allocation decisions and vehicle routing decisions. In their respective routes, vehicles may only travel using one selected speed between each node pair. The objectives are the minimisation of the worst service time and the environmental costs incurred during the transportation of all necessary demand flows, respectively. Since the studied problem is NP-hard, a meta-heuristic approach based on the non-dominated sorting genetic algorithm-II meta-heuristic is proposed. Additionally, min-max location and sequential allocation-routing method is developed to generate initial solutions. Furthermore, problemspecific crossover and mutation operators are implemented to efficiently explore the search space. Whereas, a novel rankbased speed selection procedure is devised to determine the appropriate travel speeds for generated off-springs based on their relative ranks in current population. Computational experiments are performed on the Australian Post (AP) dataset, and results indicate that our proposed heuristic approach provides good solutions in competitive CPU times. Finally, a discussion on the obtained Pareto frontier approximations is offered, and analysis is conducted on the effects of key decision parameters such as the number of located hub nodes, and the number of vehicles available at open hubs.

Software-defined networking (SDN) enables dynamic, flexible, and programmatically efficient network design by separating the control plane from the data plane, revolutionizing network control and management. In response to the demands of large data centers, SDNs facilitate resource provision, traffic management, and network reconfiguration, while enhancing network virtualization and security. However, SDNs are vulnerable to conventional security threats. This paper proposes a comprehensive three-step approach for assessing security risks in SDN environments. Our methodology incorporates Multi-Criteria Decision Making (MCDM) techniques as follows: (1) Initial threat identification using Hesitant Fuzzy Sets-TOPSIS (HFS-TOPSIS), (2) Risk quantification via Hesitant Fuzzy Analytical Hierarchical Process (HF-AHP), and (3) Mitigation planning through Fault Tree Analysis (FTA). This structured approach ensures a thorough risk assessment and effective mitigation, enabling network administrators to identify potential risks and implement appropriate remedies before deploying SDN architectures. Our approach is compared with traditional risk-scoring methods to demonstrate its superior performance.

Food security is a priority in sustainable development due to the growing population and continuous urbanization. As a result, the majority of the support and effort directed toward establishing sustainable agriculture has been directed toward the typical rural sector. A significant amount of research on sustainable agriculture in urban areas has been conducted. The concepts of digitalization and smart innovation have been widely adopted by the agricultural sector, which simplifies the integration of agriculture 4.0 into urban farming. This research investigates the sustainable location of urban farming in the context of agriculture 4.0, a three-step methodology for optimizing an urban location plan. The proposed architecture is founded on an optimization model, which optimizes the ecological benefit, the CO2 emissions, the transportation costs, the sensor costs, and the water consumption. The methodology first formulates the problem as a multi-objective optimization model, and then the proposed model is solved using weighting methods and the augmented epsilon constraint approach. Finally, a multi-criteria decision- making method (VIKOR) is used to compare the different solutions obtained.

Port management is critical to the maritime transportation industry. Effective port management aims, among other things, to reduce the amount of time required to operate the cargo volume of the vessels. Numerous uncertainty factors, such as weather and mechanical issues, frequently affect maritime transportation, which can hamper port operations planning. The lack of certainty surrounding a number of relevant parameters makes efficient preparation for quayside operations difficult. As a result, when making decisions, it is critical to account for the possibility of uncertainty. The ability to deal with unfavorable and unpredictable events is one of the characteristics of a great practical solution. In this paper, we investigate a scenario in which machine learning techniques are used to predict data for both the berth allocation and the quay crane assignment problems. We model the problem as a mixed integer linear program with multiple objectives that takes berth allocation and quay crane assignment into account. We propose a genetic algorithm scheme to solve the problem. Crossover and mutation procedures are provided. Finally, managerial implications and future research directions are given.

Blockchain technology provides a promising solution for collaborative economy systems by offering a decentralized, transparent, and secure platform. This is mainly accomplished through smart contracts, which are self-executing computer programs that facilitate, verify, and enforce the negotiation or performance of a contract. Digital tokens, on the other hand, are used to represent assets or currencies in these systems. Despite the benefits of Blockchain-based collaborative economy systems, significant security concerns are associated with them. These include the possibility of fraud, risk assessment, bugs in smart contracts, and cyber-attacks. For instance, attackers can exploit vulnerabilities in smart contracts to perform reentrancy and infinite loop attacks, leading to significant financial losses. To address these security challenges, this paper proposes integrating artificial intelligence models to prevent vulnerabilities in smart contracts and detect anomalies. Specifically, Graph Neural Networks models can be utilized to safeguard Blockchain-based collaborative economy platforms from attacks such as reentrancy and infinite loop attacks. According to the findings, this approach can accurately identify both normal and abnormal traffic and classify specific types of attacks. The framework's performance is further evaluated using various metrics to ensure its effectiveness in detecting anomalies, thereby providing an additional layer of security for Blockchain-based collaborative economy systems.

A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to solve the Uncapacitated Single Allocation Hub Location problem. The GA uses binary and integer encoding with genetic operators adapted to this problem. Our GA is improved by initially locating hubs at middle nodes. In our implementation we use the power of the GPU to compute in parallel several initial solutions, varying the number of hubs. The obtained experimental results compared with the best known solutions on all benchmarks. They show that our approach outperforms most well-known heuristics in terms of solution quality and time execution. Also it allowed to solve instances problem unsolved before.