In this dissertation, a finitely convergent disjunctive programming procedure, the Convex Hull Tree (CHT) algorithm, is proposed to obtain the convex hull of a general mixed–integer linear program with bounded integer variables. The CHT algorithm constructs a linear program that has the same optimal solution as the associated mixed-integer linear program. The standard notion of sequential cutting planes is then combined with ideasunderlying the CHT algorithm to help guide the choice of disjunctions to use within a new cutting plane method, the Cutting Plane Tree (CPT) algorithm. We show that the CPT algorithm converges to an integer optimal solution of the general mixed-integer linear program with bounded integer variables in finitely many steps. We also enhance the CPT algorithm with several techniques including a “round-of-cuts” approach and an iterative method for solving the cut generation linear program (CGLP). Two normalization constraints are discussed in detail for solving the CGLP. For moderately sized instances, our study shows that the CPT algorithm provides significant gap closures with a pure cutting plane method.

This report considers an application of mixed-integer disjunctive programming (MIDP)where a theoretical robot can jump from one point to another and where the number ofjumps is to be minimized. The robot is only able to jump to the north, south, east andwest. Furthermore, the robot should also be able to navigate and jump around or across anypotential obstacles on the way. The algorithm for solving this problem is set to terminatewhen the robot has reached a set of end coordinates. The goal of this report is to find amethod for solving this problem and to investigate the time complexity of such a method.The problem is converted to big-M representation and solved numerically. Gurobi is theoptimization solver used in this thesis. The model created and implemented with Gurobiyielded optimal solutions to problems of the form above of varying complexity. For most ofcases tested, the time complexity appeared to be linear, but this is likely due to presolvingperformed by Gurobi before running the optimization. Further tests are needed to determinethe time complexity of Gurobi’s optimization algorithm for this specific type of problem.

The indiscriminate emission of CO2 is drastically aggravating climate change. Carbon Capture and Utilization (CCU) was born as a complementary solution to this issue. This thesis studies the consumption of carbon dioxide in industrial processes, starting from synthesis gas, and using this building block in subsequent syntheses; ultimately integrating CO2 utilization with previously non-CO2 consuming processes.