In an article just lately printed within the journal Scientific Stories, researchers investigated the potential of quantum computing expertise for fixing the automated guided automobile (AGV) scheduling drawback.
Quantum Computing Potential
At the moment, AGVs are used extensively in each side of manufacturing, transportation, and logistics, which considerably improved industrial intelligence and automation ranges and enhanced effectivity. The quantity of AGVs’ parallel work is rising to satisfy the necessities of software eventualities, which vastly will increase the AGV scheduling challenges.
The AGV scheduling drawback is a difficult combinatorial optimization drawback. Though a number of research have been carried out on AGV scheduling issues protecting a number of eventualities like terminals and workshops, discovering high-quality scheduling options shortly/inside a brief timeframe stays a significant problem.
Lately, vital progress has been achieved in each sensible purposes and theoretical understanding of quantum computing. The quantum computer systems’ dependence on quantum mechanical ideas is the elemental distinction between them and conventional computer systems.
Particularly, quantum bits are utilized as basic info storage models in quantum computer systems, which allow these computer systems to carry considerably extra info than conventional computer systems. Moreover, quantum computer systems are advantageous for addressing issues like combinatorial optimization.
Combinatorial optimization issues could be mapped to the Ising mannequin’s floor state search drawback. The scheduling drawback of AGVs could be thought of as a kind of routing drawback. Conventional options for routing issues typically require vital computational sources.
Nevertheless, quantum computing methods have displayed nice potential in fixing optimization and routing issues. Though a number of research have utilized quantum computing to unravel sensible optimization issues, quantum computing analysis on AGV scheduling stays on the nascent stage, with a number of researchers utilizing simulators to unravel them.
The Research
On this examine, researchers utilized quantum computing expertise to the AGV scheduling drawback analysis, and proposed new quadratic unconstrained binary optimization (QUBO) fashions that adapt to fixing the issue underneath two separate standards: minimizing the general AGV journey time and job completion time/makespan.
Particularly, two forms of QUBO fashions appropriate for numerous AGV scheduling goals had been constructed, and the scheduling scheme was coded into the Hamiltonian operator’s floor state. The issue was solved utilizing an optical coherent Ising machine (CIM).
The target of the examine was to successfully meet the necessities of large-scale scheduling. In conventional AGV scheduling drawback analysis, the computation time considerably will increase with the rising variety of duties and AGVs. In sensible eventualities, dispatchers set a number of scheduling goals based mostly on the character of the work, with minimizing the whole journey time and job completion time being the commonest goals. Thus, researchers constructed the QUBO fashions based mostly on completely different goals and offered the options and theoretical underpinnings for every.
The CIM and a conventional laptop had been used to carry out the numerical experiments on the proposed QUBO mannequin and the normal mannequin, respectively. Gurobi solver was utilized to unravel the proposed blended integer programming (MIP) mannequin on a conventional laptop, and its computing efficiency was demonstrated underneath numerous drawback scales.
Moreover, an optical quantum laptop was employed to unravel the arc and node fashions’ drawback instances at completely different scales, and the computation efficiency was in contrast with the efficiency of conventional computer systems. The elements of the CIM used on this examine had been primarily composed {of electrical} and optical elements.
The machine’s optical half was composed of periodically poled lithium niobate crystals, fiber rings, erbium-doped fiber amplifiers, and pulsed lasers. {The electrical} half consisted of field-programmable gate arrays, analog-to-digital/digital-to-analog converters, and optical balanced homodyne detectors.
Significance of the Research
The comparability of the arc mannequin and node mannequin efficiency on a quantum laptop with the MIP mannequin efficiency on conventional computer systems confirmed that the options obtained utilizing CIM had been all optimum options. In small-scale examples, the CIM was considerably quicker in comparison with the normal laptop.
The CIM’s computation time didn’t improve considerably with rising drawback scales, not like conventional computer systems. This indicated the nice software and improvement potential of CIM. Moreover, little distinction was noticed within the computing efficiency between the arc mannequin and the node mannequin on the quantum laptop.
Particularly, the node mannequin was barely quicker in comparison with the arc mannequin, and the arc mannequin was extra common in comparison with the node mannequin. Total, the experimental outcomes displayed that the optical quantum laptop may save 92% computation time on common than the normal calculation methodology.
To summarize, the findings of this examine demonstrated that CIM has vital software potential in fixing the AGV scheduling drawback and different related combinatorial optimization issues. Nevertheless, the advantages of quantum computing in large-scale conditions/issues couldn’t be demonstrated attributable to {hardware} constraints, which was the foremost limitation of this examine.
Journal Reference
Tang, L., Yang, C., Wen, Okay., Wu, W., Guo, Y. (2024). Quantum computing for a number of AGV scheduling fashions. Scientific Stories, 14(1), 1-16. https://doi.org/10.1038/s41598-024-62821-6, https://www.nature.com/articles/s41598-024-62821-6
RelatedPosts
In an article just lately printed within the journal Scientific Stories, researchers investigated the potential of quantum computing expertise for fixing the automated guided automobile (AGV) scheduling drawback.
Quantum Computing Potential
At the moment, AGVs are used extensively in each side of manufacturing, transportation, and logistics, which considerably improved industrial intelligence and automation ranges and enhanced effectivity. The quantity of AGVs’ parallel work is rising to satisfy the necessities of software eventualities, which vastly will increase the AGV scheduling challenges.
The AGV scheduling drawback is a difficult combinatorial optimization drawback. Though a number of research have been carried out on AGV scheduling issues protecting a number of eventualities like terminals and workshops, discovering high-quality scheduling options shortly/inside a brief timeframe stays a significant problem.
Lately, vital progress has been achieved in each sensible purposes and theoretical understanding of quantum computing. The quantum computer systems’ dependence on quantum mechanical ideas is the elemental distinction between them and conventional computer systems.
Particularly, quantum bits are utilized as basic info storage models in quantum computer systems, which allow these computer systems to carry considerably extra info than conventional computer systems. Moreover, quantum computer systems are advantageous for addressing issues like combinatorial optimization.
Combinatorial optimization issues could be mapped to the Ising mannequin’s floor state search drawback. The scheduling drawback of AGVs could be thought of as a kind of routing drawback. Conventional options for routing issues typically require vital computational sources.
Nevertheless, quantum computing methods have displayed nice potential in fixing optimization and routing issues. Though a number of research have utilized quantum computing to unravel sensible optimization issues, quantum computing analysis on AGV scheduling stays on the nascent stage, with a number of researchers utilizing simulators to unravel them.
The Research
On this examine, researchers utilized quantum computing expertise to the AGV scheduling drawback analysis, and proposed new quadratic unconstrained binary optimization (QUBO) fashions that adapt to fixing the issue underneath two separate standards: minimizing the general AGV journey time and job completion time/makespan.
Particularly, two forms of QUBO fashions appropriate for numerous AGV scheduling goals had been constructed, and the scheduling scheme was coded into the Hamiltonian operator’s floor state. The issue was solved utilizing an optical coherent Ising machine (CIM).
The target of the examine was to successfully meet the necessities of large-scale scheduling. In conventional AGV scheduling drawback analysis, the computation time considerably will increase with the rising variety of duties and AGVs. In sensible eventualities, dispatchers set a number of scheduling goals based mostly on the character of the work, with minimizing the whole journey time and job completion time being the commonest goals. Thus, researchers constructed the QUBO fashions based mostly on completely different goals and offered the options and theoretical underpinnings for every.
The CIM and a conventional laptop had been used to carry out the numerical experiments on the proposed QUBO mannequin and the normal mannequin, respectively. Gurobi solver was utilized to unravel the proposed blended integer programming (MIP) mannequin on a conventional laptop, and its computing efficiency was demonstrated underneath numerous drawback scales.
Moreover, an optical quantum laptop was employed to unravel the arc and node fashions’ drawback instances at completely different scales, and the computation efficiency was in contrast with the efficiency of conventional computer systems. The elements of the CIM used on this examine had been primarily composed {of electrical} and optical elements.
The machine’s optical half was composed of periodically poled lithium niobate crystals, fiber rings, erbium-doped fiber amplifiers, and pulsed lasers. {The electrical} half consisted of field-programmable gate arrays, analog-to-digital/digital-to-analog converters, and optical balanced homodyne detectors.
Significance of the Research
The comparability of the arc mannequin and node mannequin efficiency on a quantum laptop with the MIP mannequin efficiency on conventional computer systems confirmed that the options obtained utilizing CIM had been all optimum options. In small-scale examples, the CIM was considerably quicker in comparison with the normal laptop.
The CIM’s computation time didn’t improve considerably with rising drawback scales, not like conventional computer systems. This indicated the nice software and improvement potential of CIM. Moreover, little distinction was noticed within the computing efficiency between the arc mannequin and the node mannequin on the quantum laptop.
Particularly, the node mannequin was barely quicker in comparison with the arc mannequin, and the arc mannequin was extra common in comparison with the node mannequin. Total, the experimental outcomes displayed that the optical quantum laptop may save 92% computation time on common than the normal calculation methodology.
To summarize, the findings of this examine demonstrated that CIM has vital software potential in fixing the AGV scheduling drawback and different related combinatorial optimization issues. Nevertheless, the advantages of quantum computing in large-scale conditions/issues couldn’t be demonstrated attributable to {hardware} constraints, which was the foremost limitation of this examine.
Journal Reference
Tang, L., Yang, C., Wen, Okay., Wu, W., Guo, Y. (2024). Quantum computing for a number of AGV scheduling fashions. Scientific Stories, 14(1), 1-16. https://doi.org/10.1038/s41598-024-62821-6, https://www.nature.com/articles/s41598-024-62821-6
