Optimization of a refinery scheduling process with column generation and a quantum annealer

This study focuses on the optimization of a refinery scheduling process with the help of an adiabatic quantum computer, and more concretely one of the quantum annealers developed by D-Wave Systems. We present an algorithm for finding a global optimal solution of a MILP that leans on a solver for QUBO problems, and apply it to various possible cases of refinery scheduling optimization. We analyze the inconveniences found during the whole process, whether due to the heuristic nature of D-Wave or the implications of reducing a MILP to QUBO, and present some experimental resultsS

Barge Prioritization, Assignment, and Scheduling During Inland Waterway Disruption Responses

Inland waterways face natural and man-made disruptions that may affect navigation and infrastructure operations leading to barge traffic disruptions and economic losses. This dissertation investigates inland waterway disruption responses to intelligently redirect disrupted barges to inland terminals and prioritize offloading while minimizing total cargo value loss. This problem is known in the literature as the cargo prioritization and terminal allocation problem (CPTAP). A previous study formulated the CPTAP as a non-linear integer programming (NLIP) model solved with a genetic algorithm (GA) approach. This dissertation contributes three new and improved approaches to solve the CPTAP. The first approach is a decomposition based sequential heuristic (DBSH) that reduces the time to obtain a response solution by decomposing the CPTAP into separate cargo prioritization, assignment, and scheduling subproblems. The DBSH integrates the Analytic Hierarchy Process and linear programming to prioritize cargo and allocate barges to terminals. Our findings show that compared to the GA approach, the DBSH is more suited to solve large sized decision problems resulting in similar or reduced cargo value loss and drastically improved computational time. The second approach formulates CPTAP as a mixed integer linear programming (MILP) model improved through the addition of valid inequalities (MILP\u27). Due to the complexity of the NLIP, the GA results were validated only for small size instances. This dissertation fills this gap by using the lower bounds of the MILP\u27 model to validate the quality of all prior GA solutions. In addition, a comparison of the MILP\u27 and GA solutions for several real world scenarios show that the MILP\u27 formulation outperforms the NLIP model solved with the GA approach by reducing the total cargo value loss objective. The third approach reformulates the MILP model via Dantzig-Wolfe decomposition and develops an exact method based on branch-and-price technique to solve the model. Previous approaches obtained optimal solutions for instances of the CPTAP that consist of up to five terminals and nine barges. The main contribution of this new approach is the ability to obtain optimal solutions of larger CPTAP instances involving up to ten terminals and thirty barges in reasonable computational time

A Combinatorial Benders' decomposition for the lock scheduling problem

The Lock Scheduling Problem (LSP) is a combinatorial optimization problem that represents a real challenge for many harbours and waterway operators. The LSP consists of three strongly interconnected sub problems: scheduling lockages, assigning ships to chambers, and positioning the ships inside the chambers. These should be interpreted respectively as a scheduling, an assignment, and a packing problem. By combining the first two problems into a master problem and using the packing problem as a sub problem, a decomposition is achieved that can be solved efficiently by a Combinatorial Benders' approach. The master problem is solved first, thereby sequencing the ships into a number of lockages. Next, for each lockage, a packing sub problem is checked for feasibility, possibly returning a number of combinatorial inequalities (cuts) to the master problem. The result is an exact approach to the LSP. Experiments are conducted on a set of instances that were generated in correspondence with real world data. The results indicate that the decomposition approach significantly outperforms other exact approaches presented in the literature, in terms of solution quality and computation time.status: publishe

A Combinatorial Benders' decomposition for the lock scheduling problem

Ships must often pass one or more locks when entering or leaving a tide independent port or when travelling on a network of waterways. These locks control the flow and the level of inland waterways, or provide a constant water level for ships while loading or unloading at the docks. We consider locks with a single chamber or several (possibly different) parallel chambers, which can transfer one or more ships in a single operation. The resulting lock scheduling problem consists of three strongly interconnected sub problems: scheduling the lockages, assigning ships to chambers, and positioning the ships inside the chambers. By combining the first two problems into a master problem and using the packing problem as a sub problem, a decomposition is achieved for which an efficient Combinatorial Benders approach has been developed. The master problem is solved first, thereby sequencing the ships into a number of lockages. Next, the feasibility of each lockage is verified by solving the corresponding packing sub problem, possibly returning a number of combinatorial inequalities (cuts) to the master problem. Experiments on a large test set show that this decomposition method strongly outperforms an existing monolithic approach, especially for instances with a complex packing sub problem. New optimal results for instances with up to 90 ships are generated in less than 12 hours, while a heuristic version of the algorithm generates (near)optimal results for instances with up to 50 ships in less than 10 minutes.status: publishe

A combinatorial Benders decomposition for the lock scheduling problem

\u3cp\u3eThe Lock Scheduling Problem (LSP) is a combinatorial optimization problem that represents a real challenge for many harbours and waterway operators. The LSP consists of three strongly interconnected subproblems: scheduling lockages, assigning ships to chambers, and positioning the ships inside the chambers. These should be interpreted respectively as a scheduling, an assignment, and a packing problem. By combining the first two problems into a master problem and using the packing problem as a subproblem, a decomposition is achieved that can be solved efficiently by a Combinatorial Benders approach. The master problem is solved first, thereby sequencing the ships into a number of lockages. Next, for each lockage, a packing subproblem is checked for feasibility, possibly returning a number of combinatorial inequalities (cuts) to the master problem. The result is an exact approach to the LSP. Experiments are conducted on a set of instances that were generated in correspondence with real world data. The results indicate that the decomposition approach significantly outperforms other exact approaches presented in the literature, in terms of solution quality and computation time.\u3c/p\u3