The Multi-Capacity Fixed-Charge Network Flow (MC-FCNF) problem, a generalization of the Fixed-Charge Network Flow problem, aims to assign capacities to edges in a flow network such that a target amount of flow can be hosted at minimum cost. The cost model for both problems dictates that the fixed cost of an edge is incurred for any non-zero amount of flow hosted by that edge. This problem naturally arises in many areas including infrastructure design, transportation, telecommunications, and supply chain management. The MC-FCNF problem is NP-Hard, so solving large instances using exact techniques is impractical. This paper presents a genetic algorithm designed to quickly find high-quality flow solutions to the MC-FCNF problem. The genetic algorithm uses a novel solution representation scheme that eliminates the need to repair invalid flow solutions, which is an issue common to many other genetic algorithms for the MC-FCNF problem. The genetic algorithm’s utility is demonstrated with an evaluation using real-world CO₂ capture, transportation, and storage infrastructure design data. The evaluation results highlight the genetic algorithm’s potential for solving large-scale network design problems.

Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in awide range of disciplines including computer science, landscape ecology, and energy-infrastructure modeling. Linear features present akey weakness to traditional routing calculations along cost surfaces because they cannot identify whether moving from acell to its adjacent neighbors constitutes crossing alinear barrier (increased cost) or following acorridor (reduced cost). Following and avoiding linear features can drastically change predicted routes. We introduce an approach to address this adjacency issue using asearch kernel that identifies these critical barriers and corridors. We have built this approach into anew Java-based open-source software package– CostMAP (cost surface multi-layer aggregation program)– which calculates cost surfaces and cost networks using the search kernel. CostMAP allows users to input multiple GIS data layers and to set weights and rules for developing aweighted-cost network. We compare CostMAP performance with traditional cost surface approaches and show significant performance gains– both following corridors and avoiding barriers– by modeling the movement of alarge terrestrial animal– the Baird’s Tapir (Tapirus bairdii)– in amovement ecology framework and by modeling pipeline routing for carbon capture and storage (CCS).

We propose some novel computational approaches to analyzing historical student transcript data to help improve course sequencing and generate default pathways for students to complete a college degree. Additionally, we examine whether there are “hidden prerequisites” to courses and whether there are courses which, when taken early in a student’s career, may improve their chances of graduation. Our analysis was done on a dataset consisting of all student-course enrollments for a period of 10 years at Montana State University.

Uncertainties arise in the actual transmission rates achievable over various channels available on wireless links and in the interference characteristics of those links. In this work, we examine a dynamical learning approach to link scheduling for coping with this uncertainty in multihop cognitive relay networks. We formalize a scheduling with uncertainty problem (SWUP) in which the power received by nodes from a transmitting node may not be known with certainty. The schedule simultaneously should optimize transmissions in the current frame as well as perform measurements to reduce the uncertainty in network parameters. We propose a greedy algorithm to solve the SWUP and demonstrate that it is able to learn network parameters over time in order to improve network efficiency. We also formulate the SWUP as a mixed integer linear program (MILP) in order to assess the optimality of SWUP-Greedy. Extensive numerical simulations demonstrate the effectiveness of our algorithms as compared to non-learning methods.