A multiclass simultaneous transportation equilibrium model (MSTEM) explicitly distinguishes between different user classes in terms of socioeconomic attributes, trip purpose, pure and combined transportation modes, as well as departure time, all interacting over a physically unique multimodal network. It enhances the prediction process behaviorally by combining the trip generation and departure time choices to trip distribution, modal split, and trip assignment choices in a unified and flexible framework that has many advantages from both supply and demand sides. However, the development of this concept of multiple classes increases the mathematical complexity of travel forecasting models. In this research, the authors reduce this mathematical complexity by using the supernetwork representation formulation of the diagonalized MSTEM as a fixed demand user equilibrium (FDUE) problem.

In today's volatile business environment, a single supply chain (SC) for a firm can be quite risky. The loss resulting from any disruptive operations, in a case with a single SC, can be much higher than costs of having a few resilient supply chains. For a firm to provide its customers with the desired responsiveness, a few supply chains should be developed. However, due to scarcity of resources, it would be impossible to indiscriminately provide top quality service to all customers. This study uses the Delphi method and analytic hierarchy process (AHP) to rank a firm's customers and to determine the implied resource requirements to fulfil customer orders. Using this information, a customer priority index (CPI) matrix is constructed. The CPI matrix is subsequently divided into four quadrants and for each quadrant, a unique SC strategy is formulated, considering the customer's importance and degree of scarcity of resources.

In this paper, we propose and analyze a novel optimization model to maximize the lifetime of Internet-of-Things (IoT) networks, including the Low Power Wide-Area Network (LPWAN) based Sigfox star networks and Time Slotted Channel Hopping (TSCH) mesh networks. An IoT cloud manages the IoT network adapting to sensed phenomenon changes in the deployment area retrieved from peered cloud-based environmental monitoring systems. While increasing the number of paths for IoT devices to cloud communication increases reliability, it also comes at the expense of increased energy consumption. We consider an optimization problem to determine the best redundancy level to be applied in the IoT network such that the lifetime is maximized while achieving the quality-of-service (QoS) requirements in the presence of unreliable sensing environments. Our model is generic and easily adaptable to a given IoT technology by considering the technology's devices, environmental, and protocol specifications while spanning single-hop, multi-hop, short-range, and long-range IoT technologies. We formulate the tradeoff between energy conservation vs. reliability of an IoT network as an Integer Non-Linear Programming (INLP) optimization problem. The feasibility of our approach in maximizing the lifetime of IoT networks for both the star and mesh network topologies is demonstrated using SigFox and TSCH as representative technologies, respectively. We conduct an extensive comparative performance analysis demonstrating that our model outperforms contemporary baseline models in both SigFox and TSCH IoT network technologies.

A methodology to find an optimal architecture for appliances undergoing periodic governmental regulations and energy-efficiency improvements is proposed. For instance, manufacturers producing home appliances are continuously challenged by new rules and regulations to meet minimum energy efficiency requirements. These requirements are either enforced by new regulations or recommended by voluntary programs, depending on governmental policies. The methodology proposed in this study initially identifies all regulatory and voluntary factors that affect product architecture. These factors include product performance, economic and environmental considerations. Using these factors, the analytic hierarchy process (AHP) is applied to evaluate trade-offs among alternative product architectures with varying degrees of modularity. Lastly, AHP assists in identifying the optimal product architecture based on the formerly identified factors and criteria. Using this methodology, appliance manufacturers are able to choose the most sustainable architecture, one that is environmentally friendly, and which meets governmental regulations.