Quantum Scheduling Function for Process and Resource Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods do not provide an optimization function for solving scheduling problems using quantum annealing or Ising machines, which are essential for creating a process execution plan that meets predetermined time and resource constraints.
Innovation Solution
An optimization function is created using quantum bits or spins to represent quantum states, defining constraints for process order and resource contention, which can be solved using quantum annealing or Ising machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional von Neumann type computers are used to solve scheduling problems, then existing computational methods can be applied, but the solving efficiency is insufficient for combination optimization problems
Solution Approach 1:
The patent replaces conventional von Neumann type computers with quantum annealing machines or Ising machines to solve scheduling problems. This substitution enables the system to handle combination optimization problems more efficiently by utilizing quantum mechanical principles rather than classical computational mechanisms, thereby improving solving efficiency and reducing computational time.
2Productivity
If quantum annealing machines or Ising machines are used, then high-speed solution can be achieved, but an optimization function specifically for scheduling problems must be created
Solution Approach 1:
The patent segments the scheduling problem into distinct constraint components: process order constraints and resource contention constraints. Each constraint type is formulated separately as a penalty function, allowing the complex scheduling problem to be broken down into manageable parts that can be systematically combined into the overall optimization function.
Solution Approach 2:
The patent transforms the scheduling problem parameters into quantum mechanical parameters by defining quantum bits x_p,t that represent the state of process p at time t. The optimization function uses these quantum parameters to encode scheduling decisions, converting classical scheduling constraints into quantum mechanical terms that can be processed by quantum annealing machines or Ising machines.
3Adaptability or versatility
If specific graph-related problems are solved using existing methods, then objective functions for QUBO and Ising Hamiltonians are available, but scheduling problems with process order and resource constraints cannot be solved
Solution Approach 1:
The patent creates a universal optimization function framework that can handle multiple types of constraints (process order and resource contention) within a single unified formulation. This framework allows the same quantum annealing or Ising machine to solve various scheduling problems with different constraint structures by simply changing the penalty function parameters, thereby achieving both versatility and reliable constraint satisfaction.
Data Source
AI summary
Provided is a technology for creating an optimization function for solving a scheduling problem for creating a process execution plan such that execution of all processes will be completed by a predetermined time point under constraint conditions regarding the processes and resources required by the processes, the optimization function relating to a variable that represents a quantum state. The technology includes: an input setting unit that sets a set P of processes, a set R of resources, a maximum time point End, the time p.time it takes to execute a process p, a set p.precede of processes of which execution needs to be completed before execution of the process p is started, and a set r.processes of processes that require a resource r when executed, as input to the scheduling problem for creating a plan of execution start time points of processes such that execution of all processes included in the set P will be completed by the maximum time point End under the predetermined constraint conditions; and an optimization function creation unit that creates the optimization function using the input.