Abstract
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 results.
Highlights
Since the first quantum algorithms, the range of problems where quantum computers can be applied have grown over time
The refinery scheduling problem (SP) can fit in the mixed-integer linear problem (MILP) generic problem just identifying its constraints over binary variables (2a)–(2c) with (6a)–(6b), the constraints over reals (3a)–(3r) with (6c)–(6d) and the constraints over mixed variables (4a)–(4f) with (6e)–(6f)
In this paper we have described a possible technique for solving an optimization problem with continuous and binary variables, drawing upon a column generation scheme supported by the Dantzig–Wolfe decomposition
Summary
Since the first quantum algorithms, the range of problems where quantum computers can be applied have grown over time. Efforts have progressed in two fronts: to design algorithms that solve practical problems and to have operational quantum machines. Previous works on this matter that aimed to solve real-life problems with the help of a quantum annealer include Bauckhage et al (2020), Calude and Dinneen (2017) and Venturelli et al (2015). These articles consider a wide range of problems: the broadcast time problem, the job-shop scheduling problem and the. All of them solely have binary variables in their formulations
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