Ising Model Schedule Creation for Worker Allocation
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Solution Overview
Problem
Current methods struggle to efficiently create schedules for a large number of workers with nonlinear constraint conditions, leading to impractical computation times and manual dependency, especially in call centers with hundreds of operators and varying factors like worker requests and organizational rules.
Innovation Solution
A schedule creation assisting device and method using an Ising model, where worker attendance is represented as spins, and the computation unit minimizes an objective function with constraint conditions, outputting a schedule that balances working time and necessary workers, incorporating adiabatic quantum computation principles to handle nonlinear constraints efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If spreadsheet software is used to create schedules, then ease of operation is improved, but productivity deteriorates when the number of workers exceeds a certain level
Solution Approach 1:
The patent replaces manual mechanical scheduling operations with an automated quantum annealing system. The Ising model formulation converts the scheduling problem into a quantum optimization problem, where quantum annealing algorithms automatically find optimal schedules without manual intervention, thus maintaining ease of operation while dramatically improving productivity for large-scale problems
Solution Approach 2:
The patent changes the fundamental parameter of computation from classical spreadsheet calculations to quantum annealing computations. By formulating the scheduling problem as an Ising model with Hamiltonian H = H0 + Hproblem, the system leverages quantum mechanical parameters (quantum tunneling, superposition) to solve optimization problems that are intractable for classical computers, thereby improving productivity while keeping the user interface simple
2Adaptability or versatility
If manual scheduling by experienced persons is used, then adaptability to complex constraints is improved, but productivity deteriorates due to time consumption
Solution Approach 1:
The quantum annealing system performs self-service by automatically handling complex scheduling constraints without human intervention. The Ising model formulation encodes all constraints (work-hour limits, rest periods, skill requirements) into the Hamiltonian function, and the quantum annealer autonomously finds schedules that satisfy these constraints, maintaining adaptability while eliminating the time consumption of manual scheduling
Solution Approach 2:
The patent substitutes manual expert judgment with an automated quantum optimization system. The mechanical process of human experts manually adjusting schedules is replaced by quantum annealing algorithms that systematically explore the solution space using quantum mechanical effects, achieving both adaptability to complex constraints and high productivity through rapid computation
3Productivity
If quantum computer techniques are applied to schedule creation, then productivity is improved for large numbers of workers, but device complexity increases
Solution Approach 1:
The patent segments the complex quantum annealing system into manageable components: the Ising model formulation layer, the quantum annealer execution layer, and the result interpretation layer. This segmentation allows the system to achieve high productivity for large-scale scheduling while managing device complexity through modular architecture and standardized quantum computing interfaces
Solution Approach 2:
The patent introduces an intermediary layer in the form of the Ising model formulation that translates complex scheduling constraints into quantum-friendly Hamiltonian functions. This intermediary abstraction layer simplifies the interface between the user and the quantum hardware, allowing productivity improvements without directly exposing users to the underlying quantum device complexity
4Measurement precision
If exhaustive search methods are used to solve combinatorial optimization problems, then measurement precision of solution quality is improved, but loss of time increases dramatically
Solution Approach 1:
The patent exploits phase transitions in quantum annealing, where the system transitions from a simple initial quantum state to a complex final state that encodes the optimal schedule. During the annealing process, quantum tunneling effects allow the system to escape local minima and find global optima, achieving high measurement precision of solution quality without the exponential time loss associated with classical exhaustive search methods
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the efficient creation of schedules with nonlinear constraint conditions for large numbers of workers, reducing computation time and manual effort, while ensuring realistic and optimized shift arrangements.
Implementation Method 1
computing an Ising model in which, regarding an objective function including, as terms, the total working time length in the period, the number of necessary workers, and a constraint condition function that is minimized when the constraint condition is satisfied, whether each of the workers is to attend at work is set as a spin, and a sensitivity between variables of the constraint condition function is set as an intensity of interaction between the spins
Data Source
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AI summary
A schedule creation assisting device 100 includes: a storage unit that stores information on a total working time length in a specified period of each of workers who work in cooperation in a specified operation, a number of the workers necessary at each timing during the period, and a constraint condition regarding allocation of the workers to the operation; and a computation unit 104 that computes an Ising model in which, regarding an objective function including, as terms, the total working time length in the period, the number of necessary workers, and a constraint condition function that is minimized when the constraint condition is satisfied, whether each of the workers is to attend at work is set as a spin, and a sensitivity between variables of the constraint condition function is set as an intensity of interaction between the spins, and that outputs a schedule in which whether each of the workers is to attend at work at the each timing during the specified period is specified based on the result.