Urban waste collection is one of the principal processes in municipalities with large expenses and laborious operations. Among the important issues raised in this regard, the lack of awareness of the exact amount of generated waste makes difficulties in the processes of collection, transportation and disposal. To this end, investigating the waste collection issue under uncertainty can play a key role in the decision-making process of managers. This paper addresses a novel robust bi-objective multi-trip periodic capacitated arc routing problem under demand uncertainty to treat the urban waste collection problem. The objectives are to minimize the total cost (i.e. traversing and vehicles’ usage costs) and minimize the longest tour distance of vehicles (makespan). To validate the proposed bi-objective robust model, the ε-constraint method is implemented using the CPLEX solver of GAMS software. Furthermore, a multi-objective invasive weed optimization algorithm is then developed to solve the problem in real-world sizes. The parameters of the multi-objective invasive weed optimization are tuned optimally using the Taguchi design method to enhance its performance. The computational results conducted on different test problems demonstrate that the proposed algorithm can generate high-quality solutions considering three indexes of mean of ideal distance, number of solutions and central processing unit time. It is proved that the ε-constraint method and multi-objective invasive weed optimization can efficiently solve the small- and large-sized problems, respectively. Finally, a sensitivity analysis is performed on one of the main parameters of the problem to study the behavior of the objective functions and provide the optimal policy.

Time Sensitive Networks (TSN) are a novel technology that combines the larger bandwidth capabilities of Ethernet with determinism and fault tolerance for safety relevant real time systems. TSN offers bounded latency for the time triggered (TT) communication by transmitting messages according to a global schedule. Most of the scheduling algorithms in this context provide the solution only from the perspective of scheduling constraints and do not consider the impacts of routing on the scheduling problem. Therefore, these algorithms are not capable to provide effective results in the domain of many real time systems. To address interdependence of routing and scheduling constraints, we introduce a heuristic list scheduler (HLS). Our approach generates valid schedules using joint routing and scheduling constraints in one step. Due to this approach, ability to find feasible schedules is dramatically increased in comparison to the schedulers with fixed routing. In addition, HLS supports multi-cast communication, distributed real time applications and inter-flow dependencies. Experimental results shows the significant increase in the schedulability because of the task and message scheduling combined with routing.

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The energy sector has been, in recent years, the target of sophisticated cyberattacks. Although the importance of collaborative cyber-security consciousness, expressed as extensive cyber threat intelligence sharing, is undoubted, the standardization of the means of exchanging cyber threat information efficiently and securely has been inadequately addressed and is mostly expressed by the emergence of the Trusted Automated eXchange of Indicator Information (TAXII TM ) protocol which faces major deficiencies when it comes to data integrity assurance and suitability for event-driven architectures. This paper presents a novel approach enabling secure and real-time exchange of cyber threat information, by extending the technological capacity of the TAXII framework and addressing its deficiencies through the integration of Distributed Ledger Technologies (DLT) and a generalized publish-subscribe middleware.The applicability of the proposed solution has been validated in several use cases addressing the real needs of Electrical Power and Energy Systems.