On finding paths and flows in multicriteria, stochastic and time-varying networks

This dissertation addresses two classes of network flow problems in networks with multiple, stochastic and time-varying attributes. The first problem class is concerned with providing routing instructions with the ability to make updated decisions as information about travel conditions is revealed for individual travelers in a transportation network. Three exact algorithms are presented for identifying all or a subset of the adaptive Pareto-optimal solutions with respect to the expected value of each criterion from each node to a desired destination for each departure time in the period of interest. The second problem class is concerned with problems of determining the optimal set of a priori path flows for evacuation in capacitated networks are addressed, where the time-dependent and stochastic nature of arc attributes and capacities inherent in these problems is explicitly considered. The concept of Safest Escape is formulated for developing egress instructions. An exact algorithm is proposed to determine the pattern of flow that maximizes the minimum path probability of successful arrival of supply at the sink. While the Safest Escape problem considers stochastic, time-varying capacities, arc travel times, while time-varying, are deterministic quantities. Explicit consideration of stochastic and time-varying travel times makes the SEscape problem and other related problems significantly more difficult. A meta-heuristic based on the principles of genetic algorithms is developed for determining optimal path flows with respect to several problems in dynamic networks, where arc traversal times and capacities are random variables with probability mass functions that vary with time. The proposed genetic algorithm is extended for use in more difficult, stochastic, time-varying and multicriteria, capacitated networks, for which no exact, efficient algorithms exist. Several objectives may be simultaneously considered in determining the optimal flow pattern: minimize total time, maximize expected flow and maximize the minimum path probability of successful arrival at the sink (the objective of the SEscape problem). Numerical experiments are conducted to assess the performance of all proposed approaches

Alginate–lavender nanofibers with antibacterial and anti-inflammatory activity to effectively promote burn healing

This paper was accepted for publication in the journal Journal of Materials Chemistry B and the definitive published version is available at http://dx.doi.org/10.1039/C5TB02174JOne of the current challenges in wound care is the development of multifunctional dressings that can both protect the wound from external agents and promote the regeneration of the new tissue. Here, we show the combined use of two naturally derived compounds, sodium alginate and lavender essential oil, for the production of bioactive nanofibrous dressings by electrospinning, and their efficacy for the treatment of skin burns induced by midrange ultraviolet radiation (UVB). We demonstrate that the engineered dressings reduce the risk of microbial infection of the burn, since they stop the growth of Staphylococcus aureus. Furthermore, they are able to control and reduce the inflammatory response that is induced in human foreskin fibroblasts by lipopolysaccharides, and in rodents by UVB exposure. In particular, we report a remarkable reduction of pro-inflammatory cytokines when fibroblasts or animals are treated with the alginate-based nanofibers. The down-regulation of cytokines production and the absence of erythema on the skin of the treated animals confirm that the here described dressings are promising as advanced biomedical devices for burn management

LEAST EXPECTED TIME HYPERPATHS IN STOCHASTIC, TIME-VARYING MULTIMODAL NETWORKS

The adaptive multimodal least expected time (AMLET) algorithm is presented for determining the adaptive least expected time (LET) hyperpaths from all origins to a specified destination for all departure times in a period of interest in stochastic, time-varying multimodal networks, when the fastest path can be appropriately selected depending on the arrival time at each node en route. Mode transfer delays are incorporated into the algorithm to represent waiting times required in transferring modes, such as between driving and boarding a transit vehicle. Both mode transfer delays and arc travel times may be stochastic and time varying. By considering stochastic, time-varying networks, the proposed algorithm can more realistically represent conditions in transportation networks than can exist in deterministic approaches. The resulting solutions provide not only the adaptive LET hyperpaths but also the travel mode to choose from along each path segment for completing a trip. Travelers are not restricted to traveling on only one path and one mode found to be best before they depart from the origin. Instead, they can choose their paths and travel modes en route in accordance to the knowledge of their arrival time at intermediate nodes. Hence, the solution is a set of hyperpaths instead of a single path to the destination. The AMLET algorithm is tested on a real-world transportation network, with several possible traffic scenarios to illustrate the nature of the solution paths

A P-Hub Location Problem for Determining Park-and-Ride Facility Locations with the Weibit-Based Choice Model

Park and ride (P&R) facilities provide intermodal transfer between private vehicles and public transportation systems to alleviate urban congestion. This study developed a mathematical programming formulation for determining P&R facility locations. A recently developed Weibit-based model was adopted to represent the traveler choice behavior with heterogeneity. The model’s independence of irrelevant alternatives (IIA) property was explored and used to linearize its nonlinear probability. Some numerical examples are provided to demonstrate a feature of the proposed mixed integer linear programing (MILP). The results indicate a significant impact of route-specific perception variance on the optimal P&R facility locations in a real-size transportation network

Maximum capture problem based on paired combinatorial weibit model to determine park-and-ride facility locations

202405 bcchNot applicableRGCOthersNational Natural Science Foundation of China; Smart Cities Research Institute; Research Institute of Land and SpacePublished24 monthsGreen (AAM

Modelling Trade Logistics Based on Multi-Method Simulation Approach: Case-in-Point: Mongolia

Most of landlocked developing countries (LLDCs) such as Mongolia suffer economically due to their geographical location, lack of access to seaports and underdeveloped infrastructure. Political influences and cross-border delays add to the challenges in which Mongolian firms involved in trade operate. However, recent changes in the political atmosphere of the Northeast Asian region have encouraged firms to conduct trade through advanced logistics designs. This chapter discusses a multi-method simulation approach using Anylogic software as one of the few approaches which can be used to model end-to-end cross-border trade logistics in Mongolia with a view to optimise/improve trade opportunities/operations. Successful implementation of this method could significantly impact the effectiveness of supply chain networks and trade logistics of LLDCs with similar geographical and political attributes